Module pywrapcp
Expand source code
# This file was automatically generated by SWIG (http://www.swig.org).
# Version 4.0.2
#
# Do not make changes to this file unless you know what you are doing--modify
# the SWIG interface file instead.
from sys import version_info as _swig_python_version_info
if _swig_python_version_info < (2, 7, 0):
raise RuntimeError("Python 2.7 or later required")
# Import the low-level C/C++ module
if __package__ or "." in __name__:
from . import _pywrapcp
else:
import _pywrapcp
try:
import builtins as __builtin__
except ImportError:
import __builtin__
def _swig_repr(self):
try:
strthis = "proxy of " + self.this.__repr__()
except __builtin__.Exception:
strthis = ""
return "<%s.%s; %s >" % (self.__class__.__module__, self.__class__.__name__, strthis,)
def _swig_setattr_nondynamic_instance_variable(set):
def set_instance_attr(self, name, value):
if name == "thisown":
self.this.own(value)
elif name == "this":
set(self, name, value)
elif hasattr(self, name) and isinstance(getattr(type(self), name), property):
set(self, name, value)
else:
raise AttributeError("You cannot add instance attributes to %s" % self)
return set_instance_attr
def _swig_setattr_nondynamic_class_variable(set):
def set_class_attr(cls, name, value):
if hasattr(cls, name) and not isinstance(getattr(cls, name), property):
set(cls, name, value)
else:
raise AttributeError("You cannot add class attributes to %s" % cls)
return set_class_attr
def _swig_add_metaclass(metaclass):
"""Class decorator for adding a metaclass to a SWIG wrapped class - a slimmed down version of six.add_metaclass"""
def wrapper(cls):
return metaclass(cls.__name__, cls.__bases__, cls.__dict__.copy())
return wrapper
class _SwigNonDynamicMeta(type):
"""Meta class to enforce nondynamic attributes (no new attributes) for a class"""
__setattr__ = _swig_setattr_nondynamic_class_variable(type.__setattr__)
import weakref
class DefaultPhaseParameters(object):
r"""
This struct holds all parameters for the default search.
DefaultPhaseParameters is only used by Solver::MakeDefaultPhase methods.
Note this is for advanced users only.
"""
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
__repr__ = _swig_repr
CHOOSE_MAX_SUM_IMPACT = _pywrapcp.DefaultPhaseParameters_CHOOSE_MAX_SUM_IMPACT
CHOOSE_MAX_AVERAGE_IMPACT = _pywrapcp.DefaultPhaseParameters_CHOOSE_MAX_AVERAGE_IMPACT
CHOOSE_MAX_VALUE_IMPACT = _pywrapcp.DefaultPhaseParameters_CHOOSE_MAX_VALUE_IMPACT
SELECT_MIN_IMPACT = _pywrapcp.DefaultPhaseParameters_SELECT_MIN_IMPACT
SELECT_MAX_IMPACT = _pywrapcp.DefaultPhaseParameters_SELECT_MAX_IMPACT
NONE = _pywrapcp.DefaultPhaseParameters_NONE
NORMAL = _pywrapcp.DefaultPhaseParameters_NORMAL
VERBOSE = _pywrapcp.DefaultPhaseParameters_VERBOSE
var_selection_schema = property(_pywrapcp.DefaultPhaseParameters_var_selection_schema_get, _pywrapcp.DefaultPhaseParameters_var_selection_schema_set, doc=r"""
This parameter describes how the next variable to instantiate
will be chosen.
""")
value_selection_schema = property(_pywrapcp.DefaultPhaseParameters_value_selection_schema_get, _pywrapcp.DefaultPhaseParameters_value_selection_schema_set, doc=r""" This parameter describes which value to select for a given var.""")
initialization_splits = property(_pywrapcp.DefaultPhaseParameters_initialization_splits_get, _pywrapcp.DefaultPhaseParameters_initialization_splits_set, doc=r"""
Maximum number of intervals that the initialization of impacts will scan
per variable.
""")
run_all_heuristics = property(_pywrapcp.DefaultPhaseParameters_run_all_heuristics_get, _pywrapcp.DefaultPhaseParameters_run_all_heuristics_set, doc=r"""
The default phase will run heuristics periodically. This parameter
indicates if we should run all heuristics, or a randomly selected
one.
""")
heuristic_period = property(_pywrapcp.DefaultPhaseParameters_heuristic_period_get, _pywrapcp.DefaultPhaseParameters_heuristic_period_set, doc=r"""
The distance in nodes between each run of the heuristics. A
negative or null value will mean that we will not run heuristics
at all.
""")
heuristic_num_failures_limit = property(_pywrapcp.DefaultPhaseParameters_heuristic_num_failures_limit_get, _pywrapcp.DefaultPhaseParameters_heuristic_num_failures_limit_set, doc=r""" The failure limit for each heuristic that we run.""")
persistent_impact = property(_pywrapcp.DefaultPhaseParameters_persistent_impact_get, _pywrapcp.DefaultPhaseParameters_persistent_impact_set, doc=r"""
Whether to keep the impact from the first search for other searches,
or to recompute the impact for each new search.
""")
random_seed = property(_pywrapcp.DefaultPhaseParameters_random_seed_get, _pywrapcp.DefaultPhaseParameters_random_seed_set, doc=r""" Seed used to initialize the random part in some heuristics.""")
display_level = property(_pywrapcp.DefaultPhaseParameters_display_level_get, _pywrapcp.DefaultPhaseParameters_display_level_set, doc=r"""
This represents the amount of information displayed by the default search.
NONE means no display, VERBOSE means extra information.
""")
decision_builder = property(_pywrapcp.DefaultPhaseParameters_decision_builder_get, _pywrapcp.DefaultPhaseParameters_decision_builder_set, doc=r""" When defined, this overrides the default impact based decision builder.""")
def __init__(self):
_pywrapcp.DefaultPhaseParameters_swiginit(self, _pywrapcp.new_DefaultPhaseParameters())
__swig_destroy__ = _pywrapcp.delete_DefaultPhaseParameters
# Register DefaultPhaseParameters in _pywrapcp:
_pywrapcp.DefaultPhaseParameters_swigregister(DefaultPhaseParameters)
class Solver(object):
r"""
Solver Class
A solver represents the main computation engine. It implements the entire
range of Constraint Programming protocols:
- Reversibility
- Propagation
- Search
Usually, Constraint Programming code consists of
- the creation of the Solver,
- the creation of the decision variables of the model,
- the creation of the constraints of the model and their addition to the
solver() through the AddConstraint() method,
- the creation of the main DecisionBuilder class,
- the launch of the solve() method with the decision builder.
For the time being, Solver is neither MT_SAFE nor MT_HOT.
"""
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
__repr__ = _swig_repr
INT_VAR_DEFAULT = _pywrapcp.Solver_INT_VAR_DEFAULT
r""" The default behavior is CHOOSE_FIRST_UNBOUND."""
INT_VAR_SIMPLE = _pywrapcp.Solver_INT_VAR_SIMPLE
r""" The simple selection is CHOOSE_FIRST_UNBOUND."""
CHOOSE_FIRST_UNBOUND = _pywrapcp.Solver_CHOOSE_FIRST_UNBOUND
r"""
Select the first unbound variable.
Variables are considered in the order of the vector of IntVars used
to create the selector.
"""
CHOOSE_RANDOM = _pywrapcp.Solver_CHOOSE_RANDOM
r""" Randomly select one of the remaining unbound variables."""
CHOOSE_MIN_SIZE_LOWEST_MIN = _pywrapcp.Solver_CHOOSE_MIN_SIZE_LOWEST_MIN
r"""
Among unbound variables, select the variable with the smallest size,
i.e., the smallest number of possible values.
In case of a tie, the selected variables is the one with the lowest min
value.
In case of a tie, the first one is selected, first being defined by the
order in the vector of IntVars used to create the selector.
"""
CHOOSE_MIN_SIZE_HIGHEST_MIN = _pywrapcp.Solver_CHOOSE_MIN_SIZE_HIGHEST_MIN
r"""
Among unbound variables, select the variable with the smallest size,
i.e., the smallest number of possible values.
In case of a tie, the selected variable is the one with the highest min
value.
In case of a tie, the first one is selected, first being defined by the
order in the vector of IntVars used to create the selector.
"""
CHOOSE_MIN_SIZE_LOWEST_MAX = _pywrapcp.Solver_CHOOSE_MIN_SIZE_LOWEST_MAX
r"""
Among unbound variables, select the variable with the smallest size,
i.e., the smallest number of possible values.
In case of a tie, the selected variables is the one with the lowest max
value.
In case of a tie, the first one is selected, first being defined by the
order in the vector of IntVars used to create the selector.
"""
CHOOSE_MIN_SIZE_HIGHEST_MAX = _pywrapcp.Solver_CHOOSE_MIN_SIZE_HIGHEST_MAX
r"""
Among unbound variables, select the variable with the smallest size,
i.e., the smallest number of possible values.
In case of a tie, the selected variable is the one with the highest max
value.
In case of a tie, the first one is selected, first being defined by the
order in the vector of IntVars used to create the selector.
"""
CHOOSE_LOWEST_MIN = _pywrapcp.Solver_CHOOSE_LOWEST_MIN
r"""
Among unbound variables, select the variable with the smallest minimal
value.
In case of a tie, the first one is selected, "first" defined by the
order in the vector of IntVars used to create the selector.
"""
CHOOSE_HIGHEST_MAX = _pywrapcp.Solver_CHOOSE_HIGHEST_MAX
r"""
Among unbound variables, select the variable with the highest maximal
value.
In case of a tie, the first one is selected, first being defined by the
order in the vector of IntVars used to create the selector.
"""
CHOOSE_MIN_SIZE = _pywrapcp.Solver_CHOOSE_MIN_SIZE
r"""
Among unbound variables, select the variable with the smallest size.
In case of a tie, the first one is selected, first being defined by the
order in the vector of IntVars used to create the selector.
"""
CHOOSE_MAX_SIZE = _pywrapcp.Solver_CHOOSE_MAX_SIZE
r"""
Among unbound variables, select the variable with the highest size.
In case of a tie, the first one is selected, first being defined by the
order in the vector of IntVars used to create the selector.
"""
CHOOSE_MAX_REGRET_ON_MIN = _pywrapcp.Solver_CHOOSE_MAX_REGRET_ON_MIN
r"""
Among unbound variables, select the variable with the largest
gap between the first and the second values of the domain.
"""
CHOOSE_PATH = _pywrapcp.Solver_CHOOSE_PATH
r"""
Selects the next unbound variable on a path, the path being defined by
the variables: var[i] corresponds to the index of the next of i.
"""
INT_VALUE_DEFAULT = _pywrapcp.Solver_INT_VALUE_DEFAULT
r""" The default behavior is ASSIGN_MIN_VALUE."""
INT_VALUE_SIMPLE = _pywrapcp.Solver_INT_VALUE_SIMPLE
r""" The simple selection is ASSIGN_MIN_VALUE."""
ASSIGN_MIN_VALUE = _pywrapcp.Solver_ASSIGN_MIN_VALUE
r""" Selects the min value of the selected variable."""
ASSIGN_MAX_VALUE = _pywrapcp.Solver_ASSIGN_MAX_VALUE
r""" Selects the max value of the selected variable."""
ASSIGN_RANDOM_VALUE = _pywrapcp.Solver_ASSIGN_RANDOM_VALUE
r""" Selects randomly one of the possible values of the selected variable."""
ASSIGN_CENTER_VALUE = _pywrapcp.Solver_ASSIGN_CENTER_VALUE
r"""
Selects the first possible value which is the closest to the center
of the domain of the selected variable.
The center is defined as (min + max) / 2.
"""
SPLIT_LOWER_HALF = _pywrapcp.Solver_SPLIT_LOWER_HALF
r"""
Split the domain in two around the center, and choose the lower
part first.
"""
SPLIT_UPPER_HALF = _pywrapcp.Solver_SPLIT_UPPER_HALF
r"""
Split the domain in two around the center, and choose the lower
part first.
"""
SEQUENCE_DEFAULT = _pywrapcp.Solver_SEQUENCE_DEFAULT
SEQUENCE_SIMPLE = _pywrapcp.Solver_SEQUENCE_SIMPLE
CHOOSE_MIN_SLACK_RANK_FORWARD = _pywrapcp.Solver_CHOOSE_MIN_SLACK_RANK_FORWARD
CHOOSE_RANDOM_RANK_FORWARD = _pywrapcp.Solver_CHOOSE_RANDOM_RANK_FORWARD
INTERVAL_DEFAULT = _pywrapcp.Solver_INTERVAL_DEFAULT
r""" The default is INTERVAL_SET_TIMES_FORWARD."""
INTERVAL_SIMPLE = _pywrapcp.Solver_INTERVAL_SIMPLE
r""" The simple is INTERVAL_SET_TIMES_FORWARD."""
INTERVAL_SET_TIMES_FORWARD = _pywrapcp.Solver_INTERVAL_SET_TIMES_FORWARD
r"""
Selects the variable with the lowest starting time of all variables,
and fixes its starting time to this lowest value.
"""
INTERVAL_SET_TIMES_BACKWARD = _pywrapcp.Solver_INTERVAL_SET_TIMES_BACKWARD
r"""
Selects the variable with the highest ending time of all variables,
and fixes the ending time to this highest values.
"""
TWOOPT = _pywrapcp.Solver_TWOOPT
r"""
Operator which reverses a sub-chain of a path. It is called TwoOpt
because it breaks two arcs on the path; resulting paths are called
two-optimal.
Possible neighbors for the path 1 -> 2 -> 3 -> 4 -> 5
(where (1, 5) are first and last nodes of the path and can therefore not
be moved):
1 -> [3 -> 2] -> 4 -> 5
1 -> [4 -> 3 -> 2] -> 5
1 -> 2 -> [4 -> 3] -> 5
"""
OROPT = _pywrapcp.Solver_OROPT
r"""
Relocate: OROPT and RELOCATE.
Operator which moves a sub-chain of a path to another position; the
specified chain length is the fixed length of the chains being moved.
When this length is 1, the operator simply moves a node to another
position.
Possible neighbors for the path 1 -> 2 -> 3 -> 4 -> 5, for a chain
length of 2 (where (1, 5) are first and last nodes of the path and can
therefore not be moved):
1 -> 4 -> [2 -> 3] -> 5
1 -> [3 -> 4] -> 2 -> 5
Using Relocate with chain lengths of 1, 2 and 3 together is equivalent
to the OrOpt operator on a path. The OrOpt operator is a limited
version of 3Opt (breaks 3 arcs on a path).
"""
RELOCATE = _pywrapcp.Solver_RELOCATE
r""" Relocate neighborhood with length of 1 (see OROPT comment)."""
EXCHANGE = _pywrapcp.Solver_EXCHANGE
r"""
Operator which exchanges the positions of two nodes.
Possible neighbors for the path 1 -> 2 -> 3 -> 4 -> 5
(where (1, 5) are first and last nodes of the path and can therefore not
be moved):
1 -> [3] -> [2] -> 4 -> 5
1 -> [4] -> 3 -> [2] -> 5
1 -> 2 -> [4] -> [3] -> 5
"""
CROSS = _pywrapcp.Solver_CROSS
r"""
Operator which cross exchanges the starting chains of 2 paths, including
exchanging the whole paths.
First and last nodes are not moved.
Possible neighbors for the paths 1 -> 2 -> 3 -> 4 -> 5 and 6 -> 7 -> 8
(where (1, 5) and (6, 8) are first and last nodes of the paths and can
therefore not be moved):
1 -> [7] -> 3 -> 4 -> 5 6 -> [2] -> 8
1 -> [7] -> 4 -> 5 6 -> [2 -> 3] -> 8
1 -> [7] -> 5 6 -> [2 -> 3 -> 4] -> 8
"""
MAKEACTIVE = _pywrapcp.Solver_MAKEACTIVE
r"""
Operator which inserts an inactive node into a path.
Possible neighbors for the path 1 -> 2 -> 3 -> 4 with 5 inactive
(where 1 and 4 are first and last nodes of the path) are:
1 -> [5] -> 2 -> 3 -> 4
1 -> 2 -> [5] -> 3 -> 4
1 -> 2 -> 3 -> [5] -> 4
"""
MAKEINACTIVE = _pywrapcp.Solver_MAKEINACTIVE
r"""
Operator which makes path nodes inactive.
Possible neighbors for the path 1 -> 2 -> 3 -> 4 (where 1 and 4 are
first and last nodes of the path) are:
1 -> 3 -> 4 with 2 inactive
1 -> 2 -> 4 with 3 inactive
"""
MAKECHAININACTIVE = _pywrapcp.Solver_MAKECHAININACTIVE
r"""
Operator which makes a "chain" of path nodes inactive.
Possible neighbors for the path 1 -> 2 -> 3 -> 4 (where 1 and 4 are
first and last nodes of the path) are:
1 -> 3 -> 4 with 2 inactive
1 -> 2 -> 4 with 3 inactive
1 -> 4 with 2 and 3 inactive
"""
SWAPACTIVE = _pywrapcp.Solver_SWAPACTIVE
r"""
Operator which replaces an active node by an inactive one.
Possible neighbors for the path 1 -> 2 -> 3 -> 4 with 5 inactive
(where 1 and 4 are first and last nodes of the path) are:
1 -> [5] -> 3 -> 4 with 2 inactive
1 -> 2 -> [5] -> 4 with 3 inactive
"""
EXTENDEDSWAPACTIVE = _pywrapcp.Solver_EXTENDEDSWAPACTIVE
r"""
Operator which makes an inactive node active and an active one inactive.
It is similar to SwapActiveOperator except that it tries to insert the
inactive node in all possible positions instead of just the position of
the node made inactive.
Possible neighbors for the path 1 -> 2 -> 3 -> 4 with 5 inactive
(where 1 and 4 are first and last nodes of the path) are:
1 -> [5] -> 3 -> 4 with 2 inactive
1 -> 3 -> [5] -> 4 with 2 inactive
1 -> [5] -> 2 -> 4 with 3 inactive
1 -> 2 -> [5] -> 4 with 3 inactive
"""
PATHLNS = _pywrapcp.Solver_PATHLNS
r"""
Operator which relaxes two sub-chains of three consecutive arcs each.
Each sub-chain is defined by a start node and the next three arcs. Those
six arcs are relaxed to build a new neighbor.
PATHLNS explores all possible pairs of starting nodes and so defines
n^2 neighbors, n being the number of nodes.
Note that the two sub-chains can be part of the same path; they even may
overlap.
"""
FULLPATHLNS = _pywrapcp.Solver_FULLPATHLNS
r"""
Operator which relaxes one entire path and all inactive nodes, thus
defining num_paths neighbors.
"""
UNACTIVELNS = _pywrapcp.Solver_UNACTIVELNS
r"""
Operator which relaxes all inactive nodes and one sub-chain of six
consecutive arcs. That way the path can be improved by inserting
inactive nodes or swapping arcs.
"""
INCREMENT = _pywrapcp.Solver_INCREMENT
r"""
Operator which defines one neighbor per variable. Each neighbor tries to
increment by one the value of the corresponding variable. When a new
solution is found the neighborhood is rebuilt from scratch, i.e., tries
to increment values in the variable order.
Consider for instance variables x and y. x is incremented one by one to
its max, and when it is not possible to increment x anymore, y is
incremented once. If this is a solution, then next neighbor tries to
increment x.
"""
DECREMENT = _pywrapcp.Solver_DECREMENT
r"""
Operator which defines a neighborhood to decrement values.
The behavior is the same as INCREMENT, except values are decremented
instead of incremented.
"""
SIMPLELNS = _pywrapcp.Solver_SIMPLELNS
r"""
Operator which defines one neighbor per variable. Each neighbor relaxes
one variable.
When a new solution is found the neighborhood is rebuilt from scratch.
Consider for instance variables x and y. First x is relaxed and the
solver is looking for the best possible solution (with only x relaxed).
Then y is relaxed, and the solver is looking for a new solution.
If a new solution is found, then the next variable to be relaxed is x.
"""
GE = _pywrapcp.Solver_GE
r""" Move is accepted when the current objective value >= objective.Min."""
LE = _pywrapcp.Solver_LE
r""" Move is accepted when the current objective value <= objective.Max."""
EQ = _pywrapcp.Solver_EQ
r"""
Move is accepted when the current objective value is in the interval
objective.Min .. objective.Max.
"""
DELAYED_PRIORITY = _pywrapcp.Solver_DELAYED_PRIORITY
r"""
DELAYED_PRIORITY is the lowest priority: Demons will be processed after
VAR_PRIORITY and NORMAL_PRIORITY demons.
"""
VAR_PRIORITY = _pywrapcp.Solver_VAR_PRIORITY
r""" VAR_PRIORITY is between DELAYED_PRIORITY and NORMAL_PRIORITY."""
NORMAL_PRIORITY = _pywrapcp.Solver_NORMAL_PRIORITY
r""" NORMAL_PRIORITY is the highest priority: Demons will be processed first."""
def __init__(self, *args):
_pywrapcp.Solver_swiginit(self, _pywrapcp.new_Solver(*args))
self.__python_constraints = []
__swig_destroy__ = _pywrapcp.delete_Solver
def Parameters(self) -> "operations_research::ConstraintSolverParameters":
r""" Stored Parameters."""
return _pywrapcp.Solver_Parameters(self)
@staticmethod
def DefaultSolverParameters() -> "operations_research::ConstraintSolverParameters":
r""" Create a ConstraintSolverParameters proto with all the default values."""
return _pywrapcp.Solver_DefaultSolverParameters()
def AddConstraint(self, c: "Constraint") -> "void":
r"""
Adds the constraint 'c' to the model.
After calling this method, and until there is a backtrack that undoes the
addition, any assignment of variables to values must satisfy the given
constraint in order to be considered feasible. There are two fairly
different use cases:
- the most common use case is modeling: the given constraint is really
part of the problem that the user is trying to solve. In this use case,
AddConstraint is called outside of search (i.e., with state() ==
OUTSIDE_SEARCH). Most users should only use AddConstraint in this
way. In this case, the constraint will belong to the model forever: it
cannot not be removed by backtracking.
- a rarer use case is that 'c' is not a real constraint of the model. It
may be a constraint generated by a branching decision (a constraint whose
goal is to restrict the search space), a symmetry breaking constraint (a
constraint that does restrict the search space, but in a way that cannot
have an impact on the quality of the solutions in the subtree), or an
inferred constraint that, while having no semantic value to the model (it
does not restrict the set of solutions), is worth having because we
believe it may strengthen the propagation. In these cases, it happens
that the constraint is added during the search (i.e., with state() ==
IN_SEARCH or state() == IN_ROOT_NODE). When a constraint is
added during a search, it applies only to the subtree of the search tree
rooted at the current node, and will be automatically removed by
backtracking.
This method does not take ownership of the constraint. If the constraint
has been created by any factory method (Solver::MakeXXX), it will
automatically be deleted. However, power users who implement their own
constraints should do: solver.AddConstraint(solver.RevAlloc(new
MyConstraint(...));
"""
return _pywrapcp.Solver_AddConstraint(self, c)
def Solve(self, *args) -> "bool":
return _pywrapcp.Solver_Solve(self, *args)
def NewSearch(self, *args) -> "void":
return _pywrapcp.Solver_NewSearch(self, *args)
def NextSolution(self) -> "bool":
return _pywrapcp.Solver_NextSolution(self)
def RestartSearch(self) -> "void":
return _pywrapcp.Solver_RestartSearch(self)
def EndSearch(self) -> "void":
return _pywrapcp.Solver_EndSearch(self)
def SolveAndCommit(self, *args) -> "bool":
return _pywrapcp.Solver_SolveAndCommit(self, *args)
def CheckAssignment(self, solution: "Assignment") -> "bool":
r""" Checks whether the given assignment satisfies all relevant constraints."""
return _pywrapcp.Solver_CheckAssignment(self, solution)
def CheckConstraint(self, ct: "Constraint") -> "bool":
r"""
Checks whether adding this constraint will lead to an immediate
failure. It will return false if the model is already inconsistent, or if
adding the constraint makes it inconsistent.
"""
return _pywrapcp.Solver_CheckConstraint(self, ct)
def Fail(self) -> "void":
r""" Abandon the current branch in the search tree. A backtrack will follow."""
return _pywrapcp.Solver_Fail(self)
@staticmethod
def MemoryUsage() -> "int64_t":
r""" Current memory usage in bytes"""
return _pywrapcp.Solver_MemoryUsage()
def WallTime(self) -> "int64_t":
r"""
DEPRECATED: Use Now() instead.
Time elapsed, in ms since the creation of the solver.
"""
return _pywrapcp.Solver_WallTime(self)
def Branches(self) -> "int64_t":
r""" The number of branches explored since the creation of the solver."""
return _pywrapcp.Solver_Branches(self)
def Solutions(self) -> "int64_t":
r""" The number of solutions found since the start of the search."""
return _pywrapcp.Solver_Solutions(self)
def Failures(self) -> "int64_t":
r""" The number of failures encountered since the creation of the solver."""
return _pywrapcp.Solver_Failures(self)
def AcceptedNeighbors(self) -> "int64_t":
r""" The number of accepted neighbors."""
return _pywrapcp.Solver_AcceptedNeighbors(self)
def Stamp(self) -> "uint64_t":
r"""
The stamp indicates how many moves in the search tree we have performed.
It is useful to detect if we need to update same lazy structures.
"""
return _pywrapcp.Solver_Stamp(self)
def FailStamp(self) -> "uint64_t":
r""" The fail_stamp() is incremented after each backtrack."""
return _pywrapcp.Solver_FailStamp(self)
def IntVar(self, *args) -> "operations_research::IntVar *":
r"""
*Overload 1:*
MakeIntVar will create the best range based int var for the bounds given.
|
*Overload 2:*
MakeIntVar will create a variable with the given sparse domain.
|
*Overload 3:*
MakeIntVar will create a variable with the given sparse domain.
|
*Overload 4:*
MakeIntVar will create the best range based int var for the bounds given.
|
*Overload 5:*
MakeIntVar will create a variable with the given sparse domain.
|
*Overload 6:*
MakeIntVar will create a variable with the given sparse domain.
"""
return _pywrapcp.Solver_IntVar(self, *args)
def BoolVar(self, *args) -> "operations_research::IntVar *":
r"""
*Overload 1:*
MakeBoolVar will create a variable with a {0, 1} domain.
|
*Overload 2:*
MakeBoolVar will create a variable with a {0, 1} domain.
"""
return _pywrapcp.Solver_BoolVar(self, *args)
def IntConst(self, *args) -> "operations_research::IntVar *":
r"""
*Overload 1:*
IntConst will create a constant expression.
|
*Overload 2:*
IntConst will create a constant expression.
"""
return _pywrapcp.Solver_IntConst(self, *args)
def Sum(self, vars: "std::vector< operations_research::IntVar * > const &") -> "operations_research::IntExpr *":
r""" sum of all vars."""
return _pywrapcp.Solver_Sum(self, vars)
def ScalProd(self, *args) -> "operations_research::IntExpr *":
r"""
*Overload 1:*
scalar product
|
*Overload 2:*
scalar product
"""
return _pywrapcp.Solver_ScalProd(self, *args)
def MonotonicElement(self, values: "operations_research::Solver::IndexEvaluator1", increasing: "bool", index: "IntVar") -> "operations_research::IntExpr *":
r"""
Function based element. The constraint takes ownership of the
callback. The callback must be monotonic. It must be able to
cope with any possible value in the domain of 'index'
(potentially negative ones too). Furtermore, monotonicity is not
checked. Thus giving a non-monotonic function, or specifying an
incorrect increasing parameter will result in undefined behavior.
"""
return _pywrapcp.Solver_MonotonicElement(self, values, increasing, index)
def Element(self, *args) -> "operations_research::IntExpr *":
r"""
*Overload 1:*
values[index]
|
*Overload 2:*
values[index]
|
*Overload 3:*
Function-based element. The constraint takes ownership of the
callback. The callback must be able to cope with any possible
value in the domain of 'index' (potentially negative ones too).
|
*Overload 4:*
2D version of function-based element expression, values(expr1, expr2).
|
*Overload 5:*
vars[expr]
"""
return _pywrapcp.Solver_Element(self, *args)
def IndexExpression(self, vars: "std::vector< operations_research::IntVar * > const &", value: "int64_t") -> "operations_research::IntExpr *":
r"""
Returns the expression expr such that vars[expr] == value.
It assumes that vars are all different.
"""
return _pywrapcp.Solver_IndexExpression(self, vars, value)
def Min(self, *args) -> "operations_research::IntExpr *":
r"""
*Overload 1:*
std::min(vars)
|
*Overload 2:*
std::min (left, right)
|
*Overload 3:*
std::min(expr, value)
|
*Overload 4:*
std::min(expr, value)
"""
return _pywrapcp.Solver_Min(self, *args)
def Max(self, *args) -> "operations_research::IntExpr *":
r"""
*Overload 1:*
std::max(vars)
|
*Overload 2:*
std::max(left, right)
|
*Overload 3:*
std::max(expr, value)
|
*Overload 4:*
std::max(expr, value)
"""
return _pywrapcp.Solver_Max(self, *args)
def ConvexPiecewiseExpr(self, expr: "IntExpr", early_cost: "int64_t", early_date: "int64_t", late_date: "int64_t", late_cost: "int64_t") -> "operations_research::IntExpr *":
r""" Convex piecewise function."""
return _pywrapcp.Solver_ConvexPiecewiseExpr(self, expr, early_cost, early_date, late_date, late_cost)
def SemiContinuousExpr(self, expr: "IntExpr", fixed_charge: "int64_t", step: "int64_t") -> "operations_research::IntExpr *":
r"""
Semi continuous Expression (x <= 0 -> f(x) = 0; x > 0 -> f(x) = ax + b)
a >= 0 and b >= 0
"""
return _pywrapcp.Solver_SemiContinuousExpr(self, expr, fixed_charge, step)
def ConditionalExpression(self, condition: "IntVar", expr: "IntExpr", unperformed_value: "int64_t") -> "operations_research::IntExpr *":
r""" Conditional Expr condition ? expr : unperformed_value"""
return _pywrapcp.Solver_ConditionalExpression(self, condition, expr, unperformed_value)
def TrueConstraint(self) -> "operations_research::Constraint *":
r""" This constraint always succeeds."""
return _pywrapcp.Solver_TrueConstraint(self)
def FalseConstraint(self, *args) -> "operations_research::Constraint *":
return _pywrapcp.Solver_FalseConstraint(self, *args)
def IsEqualCstCt(self, var: "IntExpr", value: "int64_t", boolvar: "IntVar") -> "operations_research::Constraint *":
r""" boolvar == (var == value)"""
return _pywrapcp.Solver_IsEqualCstCt(self, var, value, boolvar)
def IsEqualCstVar(self, var: "IntExpr", value: "int64_t") -> "operations_research::IntVar *":
r""" status var of (var == value)"""
return _pywrapcp.Solver_IsEqualCstVar(self, var, value)
def IsEqualCt(self, v1: "IntExpr", v2: "IntExpr", b: "IntVar") -> "operations_research::Constraint *":
r""" b == (v1 == v2)"""
return _pywrapcp.Solver_IsEqualCt(self, v1, v2, b)
def IsEqualVar(self, v1: "IntExpr", v2: "IntExpr") -> "operations_research::IntVar *":
r""" status var of (v1 == v2)"""
return _pywrapcp.Solver_IsEqualVar(self, v1, v2)
def IsDifferentCstCt(self, var: "IntExpr", value: "int64_t", boolvar: "IntVar") -> "operations_research::Constraint *":
r""" boolvar == (var != value)"""
return _pywrapcp.Solver_IsDifferentCstCt(self, var, value, boolvar)
def IsDifferentCstVar(self, var: "IntExpr", value: "int64_t") -> "operations_research::IntVar *":
r""" status var of (var != value)"""
return _pywrapcp.Solver_IsDifferentCstVar(self, var, value)
def IsDifferentVar(self, v1: "IntExpr", v2: "IntExpr") -> "operations_research::IntVar *":
r""" status var of (v1 != v2)"""
return _pywrapcp.Solver_IsDifferentVar(self, v1, v2)
def IsDifferentCt(self, v1: "IntExpr", v2: "IntExpr", b: "IntVar") -> "operations_research::Constraint *":
r""" b == (v1 != v2)"""
return _pywrapcp.Solver_IsDifferentCt(self, v1, v2, b)
def IsLessOrEqualCstCt(self, var: "IntExpr", value: "int64_t", boolvar: "IntVar") -> "operations_research::Constraint *":
r""" boolvar == (var <= value)"""
return _pywrapcp.Solver_IsLessOrEqualCstCt(self, var, value, boolvar)
def IsLessOrEqualCstVar(self, var: "IntExpr", value: "int64_t") -> "operations_research::IntVar *":
r""" status var of (var <= value)"""
return _pywrapcp.Solver_IsLessOrEqualCstVar(self, var, value)
def IsLessOrEqualVar(self, left: "IntExpr", right: "IntExpr") -> "operations_research::IntVar *":
r""" status var of (left <= right)"""
return _pywrapcp.Solver_IsLessOrEqualVar(self, left, right)
def IsLessOrEqualCt(self, left: "IntExpr", right: "IntExpr", b: "IntVar") -> "operations_research::Constraint *":
r""" b == (left <= right)"""
return _pywrapcp.Solver_IsLessOrEqualCt(self, left, right, b)
def IsGreaterOrEqualCstCt(self, var: "IntExpr", value: "int64_t", boolvar: "IntVar") -> "operations_research::Constraint *":
r""" boolvar == (var >= value)"""
return _pywrapcp.Solver_IsGreaterOrEqualCstCt(self, var, value, boolvar)
def IsGreaterOrEqualCstVar(self, var: "IntExpr", value: "int64_t") -> "operations_research::IntVar *":
r""" status var of (var >= value)"""
return _pywrapcp.Solver_IsGreaterOrEqualCstVar(self, var, value)
def IsGreaterOrEqualVar(self, left: "IntExpr", right: "IntExpr") -> "operations_research::IntVar *":
r""" status var of (left >= right)"""
return _pywrapcp.Solver_IsGreaterOrEqualVar(self, left, right)
def IsGreaterOrEqualCt(self, left: "IntExpr", right: "IntExpr", b: "IntVar") -> "operations_research::Constraint *":
r""" b == (left >= right)"""
return _pywrapcp.Solver_IsGreaterOrEqualCt(self, left, right, b)
def IsGreaterCstCt(self, v: "IntExpr", c: "int64_t", b: "IntVar") -> "operations_research::Constraint *":
r""" b == (v > c)"""
return _pywrapcp.Solver_IsGreaterCstCt(self, v, c, b)
def IsGreaterCstVar(self, var: "IntExpr", value: "int64_t") -> "operations_research::IntVar *":
r""" status var of (var > value)"""
return _pywrapcp.Solver_IsGreaterCstVar(self, var, value)
def IsGreaterVar(self, left: "IntExpr", right: "IntExpr") -> "operations_research::IntVar *":
r""" status var of (left > right)"""
return _pywrapcp.Solver_IsGreaterVar(self, left, right)
def IsGreaterCt(self, left: "IntExpr", right: "IntExpr", b: "IntVar") -> "operations_research::Constraint *":
r""" b == (left > right)"""
return _pywrapcp.Solver_IsGreaterCt(self, left, right, b)
def IsLessCstCt(self, v: "IntExpr", c: "int64_t", b: "IntVar") -> "operations_research::Constraint *":
r""" b == (v < c)"""
return _pywrapcp.Solver_IsLessCstCt(self, v, c, b)
def IsLessCstVar(self, var: "IntExpr", value: "int64_t") -> "operations_research::IntVar *":
r""" status var of (var < value)"""
return _pywrapcp.Solver_IsLessCstVar(self, var, value)
def IsLessVar(self, left: "IntExpr", right: "IntExpr") -> "operations_research::IntVar *":
r""" status var of (left < right)"""
return _pywrapcp.Solver_IsLessVar(self, left, right)
def IsLessCt(self, left: "IntExpr", right: "IntExpr", b: "IntVar") -> "operations_research::Constraint *":
r""" b == (left < right)"""
return _pywrapcp.Solver_IsLessCt(self, left, right, b)
def SumLessOrEqual(self, vars: "std::vector< operations_research::IntVar * > const &", cst: "int64_t") -> "operations_research::Constraint *":
r""" Variation on arrays."""
return _pywrapcp.Solver_SumLessOrEqual(self, vars, cst)
def SumGreaterOrEqual(self, vars: "std::vector< operations_research::IntVar * > const &", cst: "int64_t") -> "operations_research::Constraint *":
return _pywrapcp.Solver_SumGreaterOrEqual(self, vars, cst)
def SumEquality(self, *args) -> "operations_research::Constraint *":
return _pywrapcp.Solver_SumEquality(self, *args)
def ScalProdEquality(self, *args) -> "operations_research::Constraint *":
return _pywrapcp.Solver_ScalProdEquality(self, *args)
def ScalProdGreaterOrEqual(self, *args) -> "operations_research::Constraint *":
return _pywrapcp.Solver_ScalProdGreaterOrEqual(self, *args)
def ScalProdLessOrEqual(self, *args) -> "operations_research::Constraint *":
return _pywrapcp.Solver_ScalProdLessOrEqual(self, *args)
def MinEquality(self, vars: "std::vector< operations_research::IntVar * > const &", min_var: "IntVar") -> "operations_research::Constraint *":
return _pywrapcp.Solver_MinEquality(self, vars, min_var)
def MaxEquality(self, vars: "std::vector< operations_research::IntVar * > const &", max_var: "IntVar") -> "operations_research::Constraint *":
return _pywrapcp.Solver_MaxEquality(self, vars, max_var)
def ElementEquality(self, *args) -> "operations_research::Constraint *":
return _pywrapcp.Solver_ElementEquality(self, *args)
def AbsEquality(self, var: "IntVar", abs_var: "IntVar") -> "operations_research::Constraint *":
r""" Creates the constraint abs(var) == abs_var."""
return _pywrapcp.Solver_AbsEquality(self, var, abs_var)
def IndexOfConstraint(self, vars: "std::vector< operations_research::IntVar * > const &", index: "IntVar", target: "int64_t") -> "operations_research::Constraint *":
r"""
This constraint is a special case of the element constraint with
an array of integer variables, where the variables are all
different and the index variable is constrained such that
vars[index] == target.
"""
return _pywrapcp.Solver_IndexOfConstraint(self, vars, index, target)
def ConstraintInitialPropagateCallback(self, ct: "Constraint") -> "operations_research::Demon *":
r"""
This method is a specialized case of the MakeConstraintDemon
method to call the InitiatePropagate of the constraint 'ct'.
"""
return _pywrapcp.Solver_ConstraintInitialPropagateCallback(self, ct)
def DelayedConstraintInitialPropagateCallback(self, ct: "Constraint") -> "operations_research::Demon *":
r"""
This method is a specialized case of the MakeConstraintDemon
method to call the InitiatePropagate of the constraint 'ct' with
low priority.
"""
return _pywrapcp.Solver_DelayedConstraintInitialPropagateCallback(self, ct)
def ClosureDemon(self, closure: "operations_research::Solver::Closure") -> "operations_research::Demon *":
r""" Creates a demon from a closure."""
return _pywrapcp.Solver_ClosureDemon(self, closure)
def BetweenCt(self, expr: "IntExpr", l: "int64_t", u: "int64_t") -> "operations_research::Constraint *":
r""" (l <= expr <= u)"""
return _pywrapcp.Solver_BetweenCt(self, expr, l, u)
def IsBetweenCt(self, expr: "IntExpr", l: "int64_t", u: "int64_t", b: "IntVar") -> "operations_research::Constraint *":
r""" b == (l <= expr <= u)"""
return _pywrapcp.Solver_IsBetweenCt(self, expr, l, u, b)
def IsBetweenVar(self, v: "IntExpr", l: "int64_t", u: "int64_t") -> "operations_research::IntVar *":
return _pywrapcp.Solver_IsBetweenVar(self, v, l, u)
def MemberCt(self, *args) -> "operations_research::Constraint *":
return _pywrapcp.Solver_MemberCt(self, *args)
def NotMemberCt(self, *args) -> "operations_research::Constraint *":
r"""
*Overload 1:*
expr not in set.
|
*Overload 2:*
expr should not be in the list of forbidden intervals [start[i]..end[i]].
|
*Overload 3:*
expr should not be in the list of forbidden intervals [start[i]..end[i]].
"""
return _pywrapcp.Solver_NotMemberCt(self, *args)
def IsMemberCt(self, *args) -> "operations_research::Constraint *":
return _pywrapcp.Solver_IsMemberCt(self, *args)
def IsMemberVar(self, *args) -> "operations_research::IntVar *":
return _pywrapcp.Solver_IsMemberVar(self, *args)
def Count(self, *args) -> "operations_research::Constraint *":
r"""
*Overload 1:*
|{i | vars[i] == value}| == max_count
|
*Overload 2:*
|{i | vars[i] == value}| == max_count
"""
return _pywrapcp.Solver_Count(self, *args)
def Distribute(self, *args) -> "operations_research::Constraint *":
r"""
*Overload 1:*
Aggregated version of count: |{i | v[i] == values[j]}| == cards[j]
|
*Overload 2:*
Aggregated version of count: |{i | v[i] == values[j]}| == cards[j]
|
*Overload 3:*
Aggregated version of count: |{i | v[i] == j}| == cards[j]
|
*Overload 4:*
Aggregated version of count with bounded cardinalities:
forall j in 0 .. card_size - 1: card_min <= |{i | v[i] == j}| <= card_max
|
*Overload 5:*
Aggregated version of count with bounded cardinalities:
forall j in 0 .. card_size - 1:
card_min[j] <= |{i | v[i] == j}| <= card_max[j]
|
*Overload 6:*
Aggregated version of count with bounded cardinalities:
forall j in 0 .. card_size - 1:
card_min[j] <= |{i | v[i] == j}| <= card_max[j]
|
*Overload 7:*
Aggregated version of count with bounded cardinalities:
forall j in 0 .. card_size - 1:
card_min[j] <= |{i | v[i] == values[j]}| <= card_max[j]
|
*Overload 8:*
Aggregated version of count with bounded cardinalities:
forall j in 0 .. card_size - 1:
card_min[j] <= |{i | v[i] == values[j]}| <= card_max[j]
"""
return _pywrapcp.Solver_Distribute(self, *args)
def Deviation(self, vars: "std::vector< operations_research::IntVar * > const &", deviation_var: "IntVar", total_sum: "int64_t") -> "operations_research::Constraint *":
r"""
Deviation constraint:
sum_i |n * vars[i] - total_sum| <= deviation_var and
sum_i vars[i] == total_sum
n = #vars
"""
return _pywrapcp.Solver_Deviation(self, vars, deviation_var, total_sum)
def AllDifferent(self, *args) -> "operations_research::Constraint *":
r"""
*Overload 1:*
All variables are pairwise different. This corresponds to the
stronger version of the propagation algorithm.
|
*Overload 2:*
All variables are pairwise different. If 'stronger_propagation'
is true, stronger, and potentially slower propagation will
occur. This API will be deprecated in the future.
"""
return _pywrapcp.Solver_AllDifferent(self, *args)
def AllDifferentExcept(self, vars: "std::vector< operations_research::IntVar * > const &", escape_value: "int64_t") -> "operations_research::Constraint *":
r"""
All variables are pairwise different, unless they are assigned to
the escape value.
"""
return _pywrapcp.Solver_AllDifferentExcept(self, vars, escape_value)
def SortingConstraint(self, vars: "std::vector< operations_research::IntVar * > const &", sorted: "std::vector< operations_research::IntVar * > const &") -> "operations_research::Constraint *":
r"""
Creates a constraint binding the arrays of variables "vars" and
"sorted_vars": sorted_vars[0] must be equal to the minimum of all
variables in vars, and so on: the value of sorted_vars[i] must be
equal to the i-th value of variables invars.
This constraint propagates in both directions: from "vars" to
"sorted_vars" and vice-versa.
Behind the scenes, this constraint maintains that:
- sorted is always increasing.
- whatever the values of vars, there exists a permutation that
injects its values into the sorted variables.
For more info, please have a look at:
https://mpi-inf.mpg.de/~mehlhorn/ftp/Mehlhorn-Thiel.pdf
"""
return _pywrapcp.Solver_SortingConstraint(self, vars, sorted)
def LexicalLess(self, left: "std::vector< operations_research::IntVar * > const &", right: "std::vector< operations_research::IntVar * > const &") -> "operations_research::Constraint *":
r"""
Creates a constraint that enforces that left is lexicographically less
than right.
"""
return _pywrapcp.Solver_LexicalLess(self, left, right)
def LexicalLessOrEqual(self, left: "std::vector< operations_research::IntVar * > const &", right: "std::vector< operations_research::IntVar * > const &") -> "operations_research::Constraint *":
r"""
Creates a constraint that enforces that left is lexicographically less
than or equal to right.
"""
return _pywrapcp.Solver_LexicalLessOrEqual(self, left, right)
def InversePermutationConstraint(self, left: "std::vector< operations_research::IntVar * > const &", right: "std::vector< operations_research::IntVar * > const &") -> "operations_research::Constraint *":
r"""
Creates a constraint that enforces that 'left' and 'right' both
represent permutations of [0..left.size()-1], and that 'right' is
the inverse permutation of 'left', i.e. for all i in
[0..left.size()-1], right[left[i]] = i.
"""
return _pywrapcp.Solver_InversePermutationConstraint(self, left, right)
def NullIntersect(self, first_vars: "std::vector< operations_research::IntVar * > const &", second_vars: "std::vector< operations_research::IntVar * > const &") -> "operations_research::Constraint *":
r"""
Creates a constraint that states that all variables in the first
vector are different from all variables in the second
group. Thus the set of values in the first vector does not
intersect with the set of values in the second vector.
"""
return _pywrapcp.Solver_NullIntersect(self, first_vars, second_vars)
def NullIntersectExcept(self, first_vars: "std::vector< operations_research::IntVar * > const &", second_vars: "std::vector< operations_research::IntVar * > const &", escape_value: "int64_t") -> "operations_research::Constraint *":
r"""
Creates a constraint that states that all variables in the first
vector are different from all variables from the second group,
unless they are assigned to the escape value. Thus the set of
values in the first vector minus the escape value does not
intersect with the set of values in the second vector.
"""
return _pywrapcp.Solver_NullIntersectExcept(self, first_vars, second_vars, escape_value)
def Circuit(self, nexts: "std::vector< operations_research::IntVar * > const &") -> "operations_research::Constraint *":
r""" Force the "nexts" variable to create a complete Hamiltonian path."""
return _pywrapcp.Solver_Circuit(self, nexts)
def SubCircuit(self, nexts: "std::vector< operations_research::IntVar * > const &") -> "operations_research::Constraint *":
r"""
Force the "nexts" variable to create a complete Hamiltonian path
for those that do not loop upon themselves.
"""
return _pywrapcp.Solver_SubCircuit(self, nexts)
def DelayedPathCumul(self, nexts: "std::vector< operations_research::IntVar * > const &", active: "std::vector< operations_research::IntVar * > const &", cumuls: "std::vector< operations_research::IntVar * > const &", transits: "std::vector< operations_research::IntVar * > const &") -> "operations_research::Constraint *":
r"""
Delayed version of the same constraint: propagation on the nexts variables
is delayed until all constraints have propagated.
"""
return _pywrapcp.Solver_DelayedPathCumul(self, nexts, active, cumuls, transits)
def PathCumul(self, *args) -> "operations_research::Constraint *":
r"""
*Overload 1:*
Creates a constraint which accumulates values along a path such that:
cumuls[next[i]] = cumuls[i] + transits[i].
Active variables indicate if the corresponding next variable is active;
this could be useful to model unperformed nodes in a routing problem.
|
*Overload 2:*
Creates a constraint which accumulates values along a path such that:
cumuls[next[i]] = cumuls[i] + transit_evaluator(i, next[i]).
Active variables indicate if the corresponding next variable is active;
this could be useful to model unperformed nodes in a routing problem.
Ownership of transit_evaluator is taken and it must be a repeatable
callback.
|
*Overload 3:*
Creates a constraint which accumulates values along a path such that:
cumuls[next[i]] = cumuls[i] + transit_evaluator(i, next[i]) + slacks[i].
Active variables indicate if the corresponding next variable is active;
this could be useful to model unperformed nodes in a routing problem.
Ownership of transit_evaluator is taken and it must be a repeatable
callback.
"""
return _pywrapcp.Solver_PathCumul(self, *args)
def AllowedAssignments(self, *args) -> "operations_research::Constraint *":
r"""
*Overload 1:*
This method creates a constraint where the graph of the relation
between the variables is given in extension. There are 'arity'
variables involved in the relation and the graph is given by a
integer tuple set.
|
*Overload 2:*
Compatibility layer for Python API.
"""
return _pywrapcp.Solver_AllowedAssignments(self, *args)
def TransitionConstraint(self, *args) -> "operations_research::Constraint *":
return _pywrapcp.Solver_TransitionConstraint(self, *args)
def NonOverlappingBoxesConstraint(self, *args) -> "operations_research::Constraint *":
return _pywrapcp.Solver_NonOverlappingBoxesConstraint(self, *args)
def Pack(self, vars: "std::vector< operations_research::IntVar * > const &", number_of_bins: "int") -> "operations_research::Pack *":
r"""
This constraint packs all variables onto 'number_of_bins'
variables. For any given variable, a value of 'number_of_bins'
indicates that the variable is not assigned to any bin.
Dimensions, i.e., cumulative constraints on this packing, can be
added directly from the pack class.
"""
return _pywrapcp.Solver_Pack(self, vars, number_of_bins)
def FixedDurationIntervalVar(self, *args) -> "operations_research::IntervalVar *":
r"""
*Overload 1:*
Creates an interval var with a fixed duration. The duration must
be greater than 0. If optional is true, then the interval can be
performed or unperformed. If optional is false, then the interval
is always performed.
|
*Overload 2:*
Creates a performed interval var with a fixed duration. The duration must
be greater than 0.
|
*Overload 3:*
Creates an interval var with a fixed duration, and performed_variable.
The duration must be greater than 0.
"""
return _pywrapcp.Solver_FixedDurationIntervalVar(self, *args)
def FixedInterval(self, start: "int64_t", duration: "int64_t", name: "std::string const &") -> "operations_research::IntervalVar *":
r""" Creates a fixed and performed interval."""
return _pywrapcp.Solver_FixedInterval(self, start, duration, name)
def IntervalVar(self, start_min: "int64_t", start_max: "int64_t", duration_min: "int64_t", duration_max: "int64_t", end_min: "int64_t", end_max: "int64_t", optional: "bool", name: "std::string const &") -> "operations_research::IntervalVar *":
r"""
Creates an interval var by specifying the bounds on start,
duration, and end.
"""
return _pywrapcp.Solver_IntervalVar(self, start_min, start_max, duration_min, duration_max, end_min, end_max, optional, name)
def MirrorInterval(self, interval_var: "IntervalVar") -> "operations_research::IntervalVar *":
r"""
Creates an interval var that is the mirror image of the given one, that
is, the interval var obtained by reversing the axis.
"""
return _pywrapcp.Solver_MirrorInterval(self, interval_var)
def FixedDurationStartSyncedOnStartIntervalVar(self, interval_var: "IntervalVar", duration: "int64_t", offset: "int64_t") -> "operations_research::IntervalVar *":
r"""
Creates an interval var with a fixed duration whose start is
synchronized with the start of another interval, with a given
offset. The performed status is also in sync with the performed
status of the given interval variable.
"""
return _pywrapcp.Solver_FixedDurationStartSyncedOnStartIntervalVar(self, interval_var, duration, offset)
def FixedDurationStartSyncedOnEndIntervalVar(self, interval_var: "IntervalVar", duration: "int64_t", offset: "int64_t") -> "operations_research::IntervalVar *":
r"""
Creates an interval var with a fixed duration whose start is
synchronized with the end of another interval, with a given
offset. The performed status is also in sync with the performed
status of the given interval variable.
"""
return _pywrapcp.Solver_FixedDurationStartSyncedOnEndIntervalVar(self, interval_var, duration, offset)
def FixedDurationEndSyncedOnStartIntervalVar(self, interval_var: "IntervalVar", duration: "int64_t", offset: "int64_t") -> "operations_research::IntervalVar *":
r"""
Creates an interval var with a fixed duration whose end is
synchronized with the start of another interval, with a given
offset. The performed status is also in sync with the performed
status of the given interval variable.
"""
return _pywrapcp.Solver_FixedDurationEndSyncedOnStartIntervalVar(self, interval_var, duration, offset)
def FixedDurationEndSyncedOnEndIntervalVar(self, interval_var: "IntervalVar", duration: "int64_t", offset: "int64_t") -> "operations_research::IntervalVar *":
r"""
Creates an interval var with a fixed duration whose end is
synchronized with the end of another interval, with a given
offset. The performed status is also in sync with the performed
status of the given interval variable.
"""
return _pywrapcp.Solver_FixedDurationEndSyncedOnEndIntervalVar(self, interval_var, duration, offset)
def IntervalRelaxedMin(self, interval_var: "IntervalVar") -> "operations_research::IntervalVar *":
r"""
Creates and returns an interval variable that wraps around the given one,
relaxing the min start and end. Relaxing means making unbounded when
optional. If the variable is non-optional, this method returns
interval_var.
More precisely, such an interval variable behaves as follows:
When the underlying must be performed, the returned interval variable
behaves exactly as the underlying;
When the underlying may or may not be performed, the returned interval
variable behaves like the underlying, except that it is unbounded on
the min side;
When the underlying cannot be performed, the returned interval variable
is of duration 0 and must be performed in an interval unbounded on
both sides.
This is very useful to implement propagators that may only modify
the start max or end max.
"""
return _pywrapcp.Solver_IntervalRelaxedMin(self, interval_var)
def IntervalRelaxedMax(self, interval_var: "IntervalVar") -> "operations_research::IntervalVar *":
r"""
Creates and returns an interval variable that wraps around the given one,
relaxing the max start and end. Relaxing means making unbounded when
optional. If the variable is non optional, this method returns
interval_var.
More precisely, such an interval variable behaves as follows:
When the underlying must be performed, the returned interval variable
behaves exactly as the underlying;
When the underlying may or may not be performed, the returned interval
variable behaves like the underlying, except that it is unbounded on
the max side;
When the underlying cannot be performed, the returned interval variable
is of duration 0 and must be performed in an interval unbounded on
both sides.
This is very useful for implementing propagators that may only modify
the start min or end min.
"""
return _pywrapcp.Solver_IntervalRelaxedMax(self, interval_var)
def TemporalDisjunction(self, *args) -> "operations_research::Constraint *":
r"""
*Overload 1:*
This constraint implements a temporal disjunction between two
interval vars t1 and t2. 'alt' indicates which alternative was
chosen (alt == 0 is equivalent to t1 before t2).
|
*Overload 2:*
This constraint implements a temporal disjunction between two
interval vars.
"""
return _pywrapcp.Solver_TemporalDisjunction(self, *args)
def DisjunctiveConstraint(self, intervals: "std::vector< operations_research::IntervalVar * > const &", name: "std::string const &") -> "operations_research::DisjunctiveConstraint *":
r"""
This constraint forces all interval vars into an non-overlapping
sequence. Intervals with zero duration can be scheduled anywhere.
"""
return _pywrapcp.Solver_DisjunctiveConstraint(self, intervals, name)
def Cumulative(self, *args) -> "operations_research::Constraint *":
r"""
*Overload 1:*
This constraint forces that, for any integer t, the sum of the demands
corresponding to an interval containing t does not exceed the given
capacity.
Intervals and demands should be vectors of equal size.
Demands should only contain non-negative values. Zero values are
supported, and the corresponding intervals are filtered out, as they
neither impact nor are impacted by this constraint.
|
*Overload 2:*
This constraint forces that, for any integer t, the sum of the demands
corresponding to an interval containing t does not exceed the given
capacity.
Intervals and demands should be vectors of equal size.
Demands should only contain non-negative values. Zero values are
supported, and the corresponding intervals are filtered out, as they
neither impact nor are impacted by this constraint.
|
*Overload 3:*
This constraint forces that, for any integer t, the sum of the demands
corresponding to an interval containing t does not exceed the given
capacity.
Intervals and demands should be vectors of equal size.
Demands should only contain non-negative values. Zero values are
supported, and the corresponding intervals are filtered out, as they
neither impact nor are impacted by this constraint.
|
*Overload 4:*
This constraint enforces that, for any integer t, the sum of the demands
corresponding to an interval containing t does not exceed the given
capacity.
Intervals and demands should be vectors of equal size.
Demands should only contain non-negative values. Zero values are
supported, and the corresponding intervals are filtered out, as they
neither impact nor are impacted by this constraint.
|
*Overload 5:*
This constraint enforces that, for any integer t, the sum of demands
corresponding to an interval containing t does not exceed the given
capacity.
Intervals and demands should be vectors of equal size.
Demands should be positive.
|
*Overload 6:*
This constraint enforces that, for any integer t, the sum of demands
corresponding to an interval containing t does not exceed the given
capacity.
Intervals and demands should be vectors of equal size.
Demands should be positive.
"""
return _pywrapcp.Solver_Cumulative(self, *args)
def Cover(self, vars: "std::vector< operations_research::IntervalVar * > const &", target_var: "IntervalVar") -> "operations_research::Constraint *":
r"""
This constraint states that the target_var is the convex hull of
the intervals. If none of the interval variables is performed,
then the target var is unperformed too. Also, if the target
variable is unperformed, then all the intervals variables are
unperformed too.
"""
return _pywrapcp.Solver_Cover(self, vars, target_var)
def Assignment(self, *args) -> "operations_research::Assignment *":
r"""
*Overload 1:*
This method creates an empty assignment.
|
*Overload 2:*
This method creates an assignment which is a copy of 'a'.
"""
return _pywrapcp.Solver_Assignment(self, *args)
def FirstSolutionCollector(self, *args) -> "operations_research::SolutionCollector *":
r"""
*Overload 1:*
Collect the first solution of the search.
|
*Overload 2:*
Collect the first solution of the search. The variables will need to
be added later.
"""
return _pywrapcp.Solver_FirstSolutionCollector(self, *args)
def LastSolutionCollector(self, *args) -> "operations_research::SolutionCollector *":
r"""
*Overload 1:*
Collect the last solution of the search.
|
*Overload 2:*
Collect the last solution of the search. The variables will need to
be added later.
"""
return _pywrapcp.Solver_LastSolutionCollector(self, *args)
def BestValueSolutionCollector(self, *args) -> "operations_research::SolutionCollector *":
r"""
*Overload 1:*
Collect the solution corresponding to the optimal value of the objective
of 'assignment'; if 'assignment' does not have an objective no solution is
collected. This collector only collects one solution corresponding to the
best objective value (the first one found).
|
*Overload 2:*
Collect the solution corresponding to the optimal value of the
objective of 'assignment'; if 'assignment' does not have an objective no
solution is collected. This collector only collects one solution
corresponding to the best objective value (the first one
found). The variables will need to be added later.
"""
return _pywrapcp.Solver_BestValueSolutionCollector(self, *args)
def AllSolutionCollector(self, *args) -> "operations_research::SolutionCollector *":
r"""
*Overload 1:*
Collect all solutions of the search.
|
*Overload 2:*
Collect all solutions of the search. The variables will need to
be added later.
"""
return _pywrapcp.Solver_AllSolutionCollector(self, *args)
def Minimize(self, v: "IntVar", step: "int64_t") -> "operations_research::OptimizeVar *":
r""" Creates a minimization objective."""
return _pywrapcp.Solver_Minimize(self, v, step)
def Maximize(self, v: "IntVar", step: "int64_t") -> "operations_research::OptimizeVar *":
r""" Creates a maximization objective."""
return _pywrapcp.Solver_Maximize(self, v, step)
def Optimize(self, maximize: "bool", v: "IntVar", step: "int64_t") -> "operations_research::OptimizeVar *":
r""" Creates a objective with a given sense (true = maximization)."""
return _pywrapcp.Solver_Optimize(self, maximize, v, step)
def WeightedMinimize(self, *args) -> "operations_research::OptimizeVar *":
r"""
*Overload 1:*
Creates a minimization weighted objective. The actual objective is
scalar_prod(sub_objectives, weights).
|
*Overload 2:*
Creates a minimization weighted objective. The actual objective is
scalar_prod(sub_objectives, weights).
"""
return _pywrapcp.Solver_WeightedMinimize(self, *args)
def WeightedMaximize(self, *args) -> "operations_research::OptimizeVar *":
r"""
*Overload 1:*
Creates a maximization weigthed objective.
|
*Overload 2:*
Creates a maximization weigthed objective.
"""
return _pywrapcp.Solver_WeightedMaximize(self, *args)
def WeightedOptimize(self, *args) -> "operations_research::OptimizeVar *":
r"""
*Overload 1:*
Creates a weighted objective with a given sense (true = maximization).
|
*Overload 2:*
Creates a weighted objective with a given sense (true = maximization).
"""
return _pywrapcp.Solver_WeightedOptimize(self, *args)
def TabuSearch(self, maximize: "bool", v: "IntVar", step: "int64_t", vars: "std::vector< operations_research::IntVar * > const &", keep_tenure: "int64_t", forbid_tenure: "int64_t", tabu_factor: "double") -> "operations_research::SearchMonitor *":
r"""
MetaHeuristics which try to get the search out of local optima.
Creates a Tabu Search monitor.
In the context of local search the behavior is similar to MakeOptimize(),
creating an objective in a given sense. The behavior differs once a local
optimum is reached: thereafter solutions which degrade the value of the
objective are allowed if they are not "tabu". A solution is "tabu" if it
doesn't respect the following rules:
- improving the best solution found so far
- variables in the "keep" list must keep their value, variables in the
"forbid" list must not take the value they have in the list.
Variables with new values enter the tabu lists after each new solution
found and leave the lists after a given number of iterations (called
tenure). Only the variables passed to the method can enter the lists.
The tabu criterion is softened by the tabu factor which gives the number
of "tabu" violations which is tolerated; a factor of 1 means no violations
allowed; a factor of 0 means all violations are allowed.
"""
return _pywrapcp.Solver_TabuSearch(self, maximize, v, step, vars, keep_tenure, forbid_tenure, tabu_factor)
def SimulatedAnnealing(self, maximize: "bool", v: "IntVar", step: "int64_t", initial_temperature: "int64_t") -> "operations_research::SearchMonitor *":
r""" Creates a Simulated Annealing monitor."""
return _pywrapcp.Solver_SimulatedAnnealing(self, maximize, v, step, initial_temperature)
def LubyRestart(self, scale_factor: "int") -> "operations_research::SearchMonitor *":
r"""
This search monitor will restart the search periodically.
At the iteration n, it will restart after scale_factor * Luby(n) failures
where Luby is the Luby Strategy (i.e. 1 1 2 1 1 2 4 1 1 2 1 1 2 4 8...).
"""
return _pywrapcp.Solver_LubyRestart(self, scale_factor)
def ConstantRestart(self, frequency: "int") -> "operations_research::SearchMonitor *":
r"""
This search monitor will restart the search periodically after 'frequency'
failures.
"""
return _pywrapcp.Solver_ConstantRestart(self, frequency)
def TimeLimit(self, *args) -> "operations_research::RegularLimit *":
return _pywrapcp.Solver_TimeLimit(self, *args)
def BranchesLimit(self, branches: "int64_t") -> "operations_research::RegularLimit *":
r"""
Creates a search limit that constrains the number of branches
explored in the search tree.
"""
return _pywrapcp.Solver_BranchesLimit(self, branches)
def FailuresLimit(self, failures: "int64_t") -> "operations_research::RegularLimit *":
r"""
Creates a search limit that constrains the number of failures
that can happen when exploring the search tree.
"""
return _pywrapcp.Solver_FailuresLimit(self, failures)
def SolutionsLimit(self, solutions: "int64_t") -> "operations_research::RegularLimit *":
r"""
Creates a search limit that constrains the number of solutions found
during the search.
"""
return _pywrapcp.Solver_SolutionsLimit(self, solutions)
def Limit(self, *args) -> "operations_research::SearchLimit *":
r"""
*Overload 1:*
Limits the search with the 'time', 'branches', 'failures' and
'solutions' limits. 'smart_time_check' reduces the calls to the wall
|
*Overload 2:*
Creates a search limit from its protobuf description
|
*Overload 3:*
Creates a search limit that is reached when either of the underlying limit
is reached. That is, the returned limit is more stringent than both
argument limits.
"""
return _pywrapcp.Solver_Limit(self, *args)
def CustomLimit(self, limiter: "std::function< bool () >") -> "operations_research::SearchLimit *":
r"""
Callback-based search limit. Search stops when limiter returns true; if
this happens at a leaf the corresponding solution will be rejected.
"""
return _pywrapcp.Solver_CustomLimit(self, limiter)
def SearchLog(self, *args) -> "operations_research::SearchMonitor *":
return _pywrapcp.Solver_SearchLog(self, *args)
def SearchTrace(self, prefix: "std::string const &") -> "operations_research::SearchMonitor *":
r"""
Creates a search monitor that will trace precisely the behavior of the
search. Use this only for low level debugging.
"""
return _pywrapcp.Solver_SearchTrace(self, prefix)
def PrintModelVisitor(self) -> "operations_research::ModelVisitor *":
r""" Prints the model."""
return _pywrapcp.Solver_PrintModelVisitor(self)
def StatisticsModelVisitor(self) -> "operations_research::ModelVisitor *":
r""" Displays some nice statistics on the model."""
return _pywrapcp.Solver_StatisticsModelVisitor(self)
def AssignVariableValue(self, var: "IntVar", val: "int64_t") -> "operations_research::Decision *":
r""" Decisions."""
return _pywrapcp.Solver_AssignVariableValue(self, var, val)
def VariableLessOrEqualValue(self, var: "IntVar", value: "int64_t") -> "operations_research::Decision *":
return _pywrapcp.Solver_VariableLessOrEqualValue(self, var, value)
def VariableGreaterOrEqualValue(self, var: "IntVar", value: "int64_t") -> "operations_research::Decision *":
return _pywrapcp.Solver_VariableGreaterOrEqualValue(self, var, value)
def SplitVariableDomain(self, var: "IntVar", val: "int64_t", start_with_lower_half: "bool") -> "operations_research::Decision *":
return _pywrapcp.Solver_SplitVariableDomain(self, var, val, start_with_lower_half)
def AssignVariableValueOrFail(self, var: "IntVar", value: "int64_t") -> "operations_research::Decision *":
return _pywrapcp.Solver_AssignVariableValueOrFail(self, var, value)
def AssignVariablesValues(self, vars: "std::vector< operations_research::IntVar * > const &", values: "std::vector< int64_t > const &") -> "operations_research::Decision *":
return _pywrapcp.Solver_AssignVariablesValues(self, vars, values)
def FailDecision(self) -> "operations_research::Decision *":
return _pywrapcp.Solver_FailDecision(self)
def Decision(self, apply: "operations_research::Solver::Action", refute: "operations_research::Solver::Action") -> "operations_research::Decision *":
return _pywrapcp.Solver_Decision(self, apply, refute)
def Compose(self, dbs: "std::vector< operations_research::DecisionBuilder * > const &") -> "operations_research::DecisionBuilder *":
return _pywrapcp.Solver_Compose(self, dbs)
def Try(self, dbs: "std::vector< operations_research::DecisionBuilder * > const &") -> "operations_research::DecisionBuilder *":
return _pywrapcp.Solver_Try(self, dbs)
def DefaultPhase(self, *args) -> "operations_research::DecisionBuilder *":
return _pywrapcp.Solver_DefaultPhase(self, *args)
def ScheduleOrPostpone(self, var: "IntervalVar", est: "int64_t", marker: "int64_t *const") -> "operations_research::Decision *":
r"""
Returns a decision that tries to schedule a task at a given time.
On the Apply branch, it will set that interval var as performed and set
its start to 'est'. On the Refute branch, it will just update the
'marker' to 'est' + 1. This decision is used in the
INTERVAL_SET_TIMES_FORWARD strategy.
"""
return _pywrapcp.Solver_ScheduleOrPostpone(self, var, est, marker)
def ScheduleOrExpedite(self, var: "IntervalVar", est: "int64_t", marker: "int64_t *const") -> "operations_research::Decision *":
r"""
Returns a decision that tries to schedule a task at a given time.
On the Apply branch, it will set that interval var as performed and set
its end to 'est'. On the Refute branch, it will just update the
'marker' to 'est' - 1. This decision is used in the
INTERVAL_SET_TIMES_BACKWARD strategy.
"""
return _pywrapcp.Solver_ScheduleOrExpedite(self, var, est, marker)
def RankFirstInterval(self, sequence: "SequenceVar", index: "int") -> "operations_research::Decision *":
r"""
Returns a decision that tries to rank first the ith interval var
in the sequence variable.
"""
return _pywrapcp.Solver_RankFirstInterval(self, sequence, index)
def RankLastInterval(self, sequence: "SequenceVar", index: "int") -> "operations_research::Decision *":
r"""
Returns a decision that tries to rank last the ith interval var
in the sequence variable.
"""
return _pywrapcp.Solver_RankLastInterval(self, sequence, index)
def Phase(self, *args) -> "operations_research::DecisionBuilder *":
return _pywrapcp.Solver_Phase(self, *args)
def DecisionBuilderFromAssignment(self, assignment: "Assignment", db: "DecisionBuilder", vars: "std::vector< operations_research::IntVar * > const &") -> "operations_research::DecisionBuilder *":
r"""
Returns a decision builder for which the left-most leaf corresponds
to assignment, the rest of the tree being explored using 'db'.
"""
return _pywrapcp.Solver_DecisionBuilderFromAssignment(self, assignment, db, vars)
def ConstraintAdder(self, ct: "Constraint") -> "operations_research::DecisionBuilder *":
r"""
Returns a decision builder that will add the given constraint to
the model.
"""
return _pywrapcp.Solver_ConstraintAdder(self, ct)
def SolveOnce(self, db: "DecisionBuilder", monitors: "std::vector< operations_research::SearchMonitor * > const &") -> "operations_research::DecisionBuilder *":
return _pywrapcp.Solver_SolveOnce(self, db, monitors)
def NestedOptimize(self, *args) -> "operations_research::DecisionBuilder *":
return _pywrapcp.Solver_NestedOptimize(self, *args)
def RestoreAssignment(self, assignment: "Assignment") -> "operations_research::DecisionBuilder *":
r"""
Returns a DecisionBuilder which restores an Assignment
(calls void Assignment::Restore())
"""
return _pywrapcp.Solver_RestoreAssignment(self, assignment)
def StoreAssignment(self, assignment: "Assignment") -> "operations_research::DecisionBuilder *":
r"""
Returns a DecisionBuilder which stores an Assignment
(calls void Assignment::Store())
"""
return _pywrapcp.Solver_StoreAssignment(self, assignment)
def Operator(self, *args) -> "operations_research::LocalSearchOperator *":
return _pywrapcp.Solver_Operator(self, *args)
def RandomLnsOperator(self, *args) -> "operations_research::LocalSearchOperator *":
return _pywrapcp.Solver_RandomLnsOperator(self, *args)
def MoveTowardTargetOperator(self, *args) -> "operations_research::LocalSearchOperator *":
r"""
*Overload 1:*
Creates a local search operator that tries to move the assignment of some
variables toward a target. The target is given as an Assignment. This
operator generates neighbors in which the only difference compared to the
current state is that one variable that belongs to the target assignment
is set to its target value.
|
*Overload 2:*
Creates a local search operator that tries to move the assignment of some
variables toward a target. The target is given either as two vectors: a
vector of variables and a vector of associated target values. The two
vectors should be of the same length. This operator generates neighbors in
which the only difference compared to the current state is that one
variable that belongs to the given vector is set to its target value.
"""
return _pywrapcp.Solver_MoveTowardTargetOperator(self, *args)
def ConcatenateOperators(self, *args) -> "operations_research::LocalSearchOperator *":
return _pywrapcp.Solver_ConcatenateOperators(self, *args)
def RandomConcatenateOperators(self, *args) -> "operations_research::LocalSearchOperator *":
r"""
*Overload 1:*
Randomized version of local search concatenator; calls a random operator
at each call to MakeNextNeighbor().
|
*Overload 2:*
Randomized version of local search concatenator; calls a random operator
at each call to MakeNextNeighbor(). The provided seed is used to
initialize the random number generator.
"""
return _pywrapcp.Solver_RandomConcatenateOperators(self, *args)
def NeighborhoodLimit(self, op: "LocalSearchOperator", limit: "int64_t") -> "operations_research::LocalSearchOperator *":
r"""
Creates a local search operator that wraps another local search
operator and limits the number of neighbors explored (i.e., calls
to MakeNextNeighbor from the current solution (between two calls
to Start()). When this limit is reached, MakeNextNeighbor()
returns false. The counter is cleared when Start() is called.
"""
return _pywrapcp.Solver_NeighborhoodLimit(self, op, limit)
def LocalSearchPhase(self, *args) -> "operations_research::DecisionBuilder *":
return _pywrapcp.Solver_LocalSearchPhase(self, *args)
def LocalSearchPhaseParameters(self, *args) -> "operations_research::LocalSearchPhaseParameters *":
return _pywrapcp.Solver_LocalSearchPhaseParameters(self, *args)
def SearchDepth(self) -> "int":
r"""
Gets the search depth of the current active search. Returns -1 if
there is no active search opened.
"""
return _pywrapcp.Solver_SearchDepth(self)
def SearchLeftDepth(self) -> "int":
r"""
Gets the search left depth of the current active search. Returns -1 if
there is no active search opened.
"""
return _pywrapcp.Solver_SearchLeftDepth(self)
def SolveDepth(self) -> "int":
r"""
Gets the number of nested searches. It returns 0 outside search,
1 during the top level search, 2 or more in case of nested searches.
"""
return _pywrapcp.Solver_SolveDepth(self)
def Rand64(self, size: "int64_t") -> "int64_t":
r""" Returns a random value between 0 and 'size' - 1;"""
return _pywrapcp.Solver_Rand64(self, size)
def Rand32(self, size: "int32_t") -> "int32_t":
r""" Returns a random value between 0 and 'size' - 1;"""
return _pywrapcp.Solver_Rand32(self, size)
def ReSeed(self, seed: "int32_t") -> "void":
r""" Reseed the solver random generator."""
return _pywrapcp.Solver_ReSeed(self, seed)
def LocalSearchProfile(self) -> "std::string":
r""" Returns local search profiling information in a human readable format."""
return _pywrapcp.Solver_LocalSearchProfile(self)
def Constraints(self) -> "int":
r"""
Counts the number of constraints that have been added
to the solver before the search.
"""
return _pywrapcp.Solver_Constraints(self)
def Accept(self, visitor: "operations_research::ModelVisitor *const") -> "void":
r""" Accepts the given model visitor."""
return _pywrapcp.Solver_Accept(self, visitor)
def FinishCurrentSearch(self) -> "void":
r""" Tells the solver to kill or restart the current search."""
return _pywrapcp.Solver_FinishCurrentSearch(self)
def RestartCurrentSearch(self) -> "void":
return _pywrapcp.Solver_RestartCurrentSearch(self)
def ShouldFail(self) -> "void":
r"""
These methods are only useful for the SWIG wrappers, which need a way
to externally cause the Solver to fail.
"""
return _pywrapcp.Solver_ShouldFail(self)
def __str__(self) -> "std::string":
return _pywrapcp.Solver___str__(self)
def Add(self, ct):
if isinstance(ct, PyConstraint):
self.__python_constraints.append(ct)
self.AddConstraint(ct)
def TreeNoCycle(self, nexts: "std::vector< operations_research::IntVar * > const &", active: "std::vector< operations_research::IntVar * > const &", callback: "operations_research::Solver::IndexFilter1"=0) -> "operations_research::Constraint *":
return _pywrapcp.Solver_TreeNoCycle(self, nexts, active, callback)
def SearchLogWithCallback(self, period: "int", callback: "std::function< std::string () >") -> "operations_research::SearchMonitor *":
return _pywrapcp.Solver_SearchLogWithCallback(self, period, callback)
def ElementFunction(self, values: "std::function< int64_t (int64_t) >", index: "IntVar") -> "operations_research::IntExpr *":
return _pywrapcp.Solver_ElementFunction(self, values, index)
def VarEvalValStrPhase(self, vars: "std::vector< operations_research::IntVar * > const &", var_evaluator: "std::function< int64_t (int64_t) >", val_str: "operations_research::Solver::IntValueStrategy") -> "operations_research::DecisionBuilder *":
return _pywrapcp.Solver_VarEvalValStrPhase(self, vars, var_evaluator, val_str)
def VarStrValEvalPhase(self, vars: "std::vector< operations_research::IntVar * > const &", var_str: "operations_research::Solver::IntVarStrategy", val_eval: "operations_research::Solver::IndexEvaluator2") -> "operations_research::DecisionBuilder *":
return _pywrapcp.Solver_VarStrValEvalPhase(self, vars, var_str, val_eval)
def VarEvalValEvalPhase(self, vars: "std::vector< operations_research::IntVar * > const &", var_eval: "std::function< int64_t (int64_t) >", val_eval: "operations_research::Solver::IndexEvaluator2") -> "operations_research::DecisionBuilder *":
return _pywrapcp.Solver_VarEvalValEvalPhase(self, vars, var_eval, val_eval)
def VarStrValEvalTieBreakPhase(self, vars: "std::vector< operations_research::IntVar * > const &", var_str: "operations_research::Solver::IntVarStrategy", val_eval: "operations_research::Solver::IndexEvaluator2", tie_breaker: "std::function< int64_t (int64_t) >") -> "operations_research::DecisionBuilder *":
return _pywrapcp.Solver_VarStrValEvalTieBreakPhase(self, vars, var_str, val_eval, tie_breaker)
def VarEvalValEvalTieBreakPhase(self, vars: "std::vector< operations_research::IntVar * > const &", var_eval: "std::function< int64_t (int64_t) >", val_eval: "operations_research::Solver::IndexEvaluator2", tie_breaker: "std::function< int64_t (int64_t) >") -> "operations_research::DecisionBuilder *":
return _pywrapcp.Solver_VarEvalValEvalTieBreakPhase(self, vars, var_eval, val_eval, tie_breaker)
def EvalEvalStrPhase(self, vars: "std::vector< operations_research::IntVar * > const &", evaluator: "operations_research::Solver::IndexEvaluator2", str: "operations_research::Solver::EvaluatorStrategy") -> "operations_research::DecisionBuilder *":
return _pywrapcp.Solver_EvalEvalStrPhase(self, vars, evaluator, str)
def EvalEvalStrTieBreakPhase(self, vars: "std::vector< operations_research::IntVar * > const &", evaluator: "operations_research::Solver::IndexEvaluator2", tie_breaker: "operations_research::Solver::IndexEvaluator1", str: "operations_research::Solver::EvaluatorStrategy") -> "operations_research::DecisionBuilder *":
return _pywrapcp.Solver_EvalEvalStrTieBreakPhase(self, vars, evaluator, tie_breaker, str)
def GuidedLocalSearch(self, *args) -> "operations_research::SearchMonitor *":
return _pywrapcp.Solver_GuidedLocalSearch(self, *args)
def SumObjectiveFilter(self, vars: "std::vector< operations_research::IntVar * > const &", values: "operations_research::Solver::IndexEvaluator2", filter_enum: "operations_research::Solver::LocalSearchFilterBound") -> "operations_research::LocalSearchFilter *":
return _pywrapcp.Solver_SumObjectiveFilter(self, vars, values, filter_enum)
# Register Solver in _pywrapcp:
_pywrapcp.Solver_swigregister(Solver)
def Solver_DefaultSolverParameters() -> "operations_research::ConstraintSolverParameters":
r""" Create a ConstraintSolverParameters proto with all the default values."""
return _pywrapcp.Solver_DefaultSolverParameters()
def Solver_MemoryUsage() -> "int64_t":
r""" Current memory usage in bytes"""
return _pywrapcp.Solver_MemoryUsage()
class BaseObject(object):
r"""
A BaseObject is the root of all reversibly allocated objects.
A DebugString method and the associated << operator are implemented
as a convenience.
"""
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
def __init__(self):
if self.__class__ == BaseObject:
_self = None
else:
_self = self
_pywrapcp.BaseObject_swiginit(self, _pywrapcp.new_BaseObject(_self, ))
__swig_destroy__ = _pywrapcp.delete_BaseObject
def DebugString(self) -> "std::string":
return _pywrapcp.BaseObject_DebugString(self)
def __str__(self) -> "std::string":
return _pywrapcp.BaseObject___str__(self)
def __repr__(self) -> "std::string":
return _pywrapcp.BaseObject___repr__(self)
def __disown__(self):
self.this.disown()
_pywrapcp.disown_BaseObject(self)
return weakref.proxy(self)
# Register BaseObject in _pywrapcp:
_pywrapcp.BaseObject_swigregister(BaseObject)
class PropagationBaseObject(BaseObject):
r"""
NOLINT
The PropagationBaseObject is a subclass of BaseObject that is also
friend to the Solver class. It allows accessing methods useful when
writing new constraints or new expressions.
"""
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
__repr__ = _swig_repr
def __init__(self, s: "Solver"):
if self.__class__ == PropagationBaseObject:
_self = None
else:
_self = self
_pywrapcp.PropagationBaseObject_swiginit(self, _pywrapcp.new_PropagationBaseObject(_self, s))
__swig_destroy__ = _pywrapcp.delete_PropagationBaseObject
def DebugString(self) -> "std::string":
return _pywrapcp.PropagationBaseObject_DebugString(self)
def solver(self) -> "operations_research::Solver *":
return _pywrapcp.PropagationBaseObject_solver(self)
def Name(self) -> "std::string":
r""" Object naming."""
return _pywrapcp.PropagationBaseObject_Name(self)
def __disown__(self):
self.this.disown()
_pywrapcp.disown_PropagationBaseObject(self)
return weakref.proxy(self)
# Register PropagationBaseObject in _pywrapcp:
_pywrapcp.PropagationBaseObject_swigregister(PropagationBaseObject)
class Decision(BaseObject):
r"""
A Decision represents a choice point in the search tree. The two main
methods are Apply() to go left, or Refute() to go right.
"""
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
def __init__(self):
if self.__class__ == Decision:
_self = None
else:
_self = self
_pywrapcp.Decision_swiginit(self, _pywrapcp.new_Decision(_self, ))
__swig_destroy__ = _pywrapcp.delete_Decision
def ApplyWrapper(self, s: "Solver") -> "void":
r""" Apply will be called first when the decision is executed."""
return _pywrapcp.Decision_ApplyWrapper(self, s)
def RefuteWrapper(self, s: "Solver") -> "void":
r""" Refute will be called after a backtrack."""
return _pywrapcp.Decision_RefuteWrapper(self, s)
def DebugString(self) -> "std::string":
return _pywrapcp.Decision_DebugString(self)
def __repr__(self) -> "std::string":
return _pywrapcp.Decision___repr__(self)
def __str__(self) -> "std::string":
return _pywrapcp.Decision___str__(self)
def __disown__(self):
self.this.disown()
_pywrapcp.disown_Decision(self)
return weakref.proxy(self)
# Register Decision in _pywrapcp:
_pywrapcp.Decision_swigregister(Decision)
class DecisionBuilder(BaseObject):
r"""
A DecisionBuilder is responsible for creating the search tree. The
important method is Next(), which returns the next decision to execute.
"""
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
def __init__(self):
if self.__class__ == DecisionBuilder:
_self = None
else:
_self = self
_pywrapcp.DecisionBuilder_swiginit(self, _pywrapcp.new_DecisionBuilder(_self, ))
__swig_destroy__ = _pywrapcp.delete_DecisionBuilder
def NextWrapper(self, s: "Solver") -> "operations_research::Decision *":
r"""
This is the main method of the decision builder class. It must
return a decision (an instance of the class Decision). If it
returns nullptr, this means that the decision builder has finished
its work.
"""
return _pywrapcp.DecisionBuilder_NextWrapper(self, s)
def DebugString(self) -> "std::string":
return _pywrapcp.DecisionBuilder_DebugString(self)
def __repr__(self) -> "std::string":
return _pywrapcp.DecisionBuilder___repr__(self)
def __str__(self) -> "std::string":
return _pywrapcp.DecisionBuilder___str__(self)
def __disown__(self):
self.this.disown()
_pywrapcp.disown_DecisionBuilder(self)
return weakref.proxy(self)
# Register DecisionBuilder in _pywrapcp:
_pywrapcp.DecisionBuilder_swigregister(DecisionBuilder)
class Demon(BaseObject):
r"""
A Demon is the base element of a propagation queue. It is the main
object responsible for implementing the actual propagation
of the constraint and pruning the inconsistent values in the domains
of the variables. The main concept is that demons are listeners that are
attached to the variables and listen to their modifications.
There are two methods:
- Run() is the actual method called when the demon is processed.
- priority() returns its priority. Standard priorities are slow, normal
or fast. "immediate" is reserved for variables and is treated separately.
"""
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
__repr__ = _swig_repr
def __init__(self):
r"""
This indicates the priority of a demon. Immediate demons are treated
separately and corresponds to variables.
"""
if self.__class__ == Demon:
_self = None
else:
_self = self
_pywrapcp.Demon_swiginit(self, _pywrapcp.new_Demon(_self, ))
__swig_destroy__ = _pywrapcp.delete_Demon
def RunWrapper(self, s: "Solver") -> "void":
r""" This is the main callback of the demon."""
return _pywrapcp.Demon_RunWrapper(self, s)
def Priority(self) -> "operations_research::Solver::DemonPriority":
r"""
This method returns the priority of the demon. Usually a demon is
fast, slow or normal. Immediate demons are reserved for internal
use to maintain variables.
"""
return _pywrapcp.Demon_Priority(self)
def DebugString(self) -> "std::string":
return _pywrapcp.Demon_DebugString(self)
def Inhibit(self, s: "Solver") -> "void":
r"""
This method inhibits the demon in the search tree below the
current position.
"""
return _pywrapcp.Demon_Inhibit(self, s)
def Desinhibit(self, s: "Solver") -> "void":
r""" This method un-inhibits the demon that was previously inhibited."""
return _pywrapcp.Demon_Desinhibit(self, s)
def __disown__(self):
self.this.disown()
_pywrapcp.disown_Demon(self)
return weakref.proxy(self)
# Register Demon in _pywrapcp:
_pywrapcp.Demon_swigregister(Demon)
class Constraint(PropagationBaseObject):
r"""
A constraint is the main modeling object. It provides two methods:
- Post() is responsible for creating the demons and attaching them to
immediate demons().
- InitialPropagate() is called once just after Post and performs
the initial propagation. The subsequent propagations will be performed
by the demons Posted during the post() method.
"""
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
def __init__(self, solver: "Solver"):
if self.__class__ == Constraint:
_self = None
else:
_self = self
_pywrapcp.Constraint_swiginit(self, _pywrapcp.new_Constraint(_self, solver))
__swig_destroy__ = _pywrapcp.delete_Constraint
def Post(self) -> "void":
r"""
This method is called when the constraint is processed by the
solver. Its main usage is to attach demons to variables.
"""
return _pywrapcp.Constraint_Post(self)
def InitialPropagateWrapper(self) -> "void":
r"""
This method performs the initial propagation of the
constraint. It is called just after the post.
"""
return _pywrapcp.Constraint_InitialPropagateWrapper(self)
def DebugString(self) -> "std::string":
return _pywrapcp.Constraint_DebugString(self)
def Var(self) -> "operations_research::IntVar *":
r"""
Creates a Boolean variable representing the status of the constraint
(false = constraint is violated, true = constraint is satisfied). It
returns nullptr if the constraint does not support this API.
"""
return _pywrapcp.Constraint_Var(self)
def __repr__(self) -> "std::string":
return _pywrapcp.Constraint___repr__(self)
def __str__(self) -> "std::string":
return _pywrapcp.Constraint___str__(self)
def __add__(self, *args) -> "operations_research::IntExpr *":
return _pywrapcp.Constraint___add__(self, *args)
def __radd__(self, v: "int64_t") -> "operations_research::IntExpr *":
return _pywrapcp.Constraint___radd__(self, v)
def __sub__(self, *args) -> "operations_research::IntExpr *":
return _pywrapcp.Constraint___sub__(self, *args)
def __rsub__(self, v: "int64_t") -> "operations_research::IntExpr *":
return _pywrapcp.Constraint___rsub__(self, v)
def __mul__(self, *args) -> "operations_research::IntExpr *":
return _pywrapcp.Constraint___mul__(self, *args)
def __rmul__(self, v: "int64_t") -> "operations_research::IntExpr *":
return _pywrapcp.Constraint___rmul__(self, v)
def __floordiv__(self, v: "int64_t") -> "operations_research::IntExpr *":
return _pywrapcp.Constraint___floordiv__(self, v)
def __neg__(self) -> "operations_research::IntExpr *":
return _pywrapcp.Constraint___neg__(self)
def __abs__(self) -> "operations_research::IntExpr *":
return _pywrapcp.Constraint___abs__(self)
def Square(self) -> "operations_research::IntExpr *":
return _pywrapcp.Constraint_Square(self)
def __eq__(self, *args) -> "operations_research::Constraint *":
return _pywrapcp.Constraint___eq__(self, *args)
def __ne__(self, *args) -> "operations_research::Constraint *":
return _pywrapcp.Constraint___ne__(self, *args)
def __ge__(self, *args) -> "operations_research::Constraint *":
return _pywrapcp.Constraint___ge__(self, *args)
def __gt__(self, *args) -> "operations_research::Constraint *":
return _pywrapcp.Constraint___gt__(self, *args)
def __le__(self, *args) -> "operations_research::Constraint *":
return _pywrapcp.Constraint___le__(self, *args)
def __lt__(self, *args) -> "operations_research::Constraint *":
return _pywrapcp.Constraint___lt__(self, *args)
def MapTo(self, vars: "std::vector< operations_research::IntVar * > const &") -> "operations_research::Constraint *":
return _pywrapcp.Constraint_MapTo(self, vars)
def IndexOf(self, *args) -> "operations_research::IntExpr *":
return _pywrapcp.Constraint_IndexOf(self, *args)
def __disown__(self):
self.this.disown()
_pywrapcp.disown_Constraint(self)
return weakref.proxy(self)
# Register Constraint in _pywrapcp:
_pywrapcp.Constraint_swigregister(Constraint)
class SearchMonitor(BaseObject):
r""" A search monitor is a simple set of callbacks to monitor all search events"""
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
def __init__(self, s: "Solver"):
if self.__class__ == SearchMonitor:
_self = None
else:
_self = self
_pywrapcp.SearchMonitor_swiginit(self, _pywrapcp.new_SearchMonitor(_self, s))
__swig_destroy__ = _pywrapcp.delete_SearchMonitor
def EnterSearch(self) -> "void":
r""" Beginning of the search."""
return _pywrapcp.SearchMonitor_EnterSearch(self)
def RestartSearch(self) -> "void":
r""" Restart the search."""
return _pywrapcp.SearchMonitor_RestartSearch(self)
def ExitSearch(self) -> "void":
r""" End of the search."""
return _pywrapcp.SearchMonitor_ExitSearch(self)
def BeginNextDecision(self, b: "DecisionBuilder") -> "void":
r""" Before calling DecisionBuilder::Next."""
return _pywrapcp.SearchMonitor_BeginNextDecision(self, b)
def EndNextDecision(self, b: "DecisionBuilder", d: "Decision") -> "void":
r""" After calling DecisionBuilder::Next, along with the returned decision."""
return _pywrapcp.SearchMonitor_EndNextDecision(self, b, d)
def ApplyDecision(self, d: "Decision") -> "void":
r""" Before applying the decision."""
return _pywrapcp.SearchMonitor_ApplyDecision(self, d)
def RefuteDecision(self, d: "Decision") -> "void":
r""" Before refuting the decision."""
return _pywrapcp.SearchMonitor_RefuteDecision(self, d)
def AfterDecision(self, d: "Decision", apply: "bool") -> "void":
r"""
Just after refuting or applying the decision, apply is true after Apply.
This is called only if the Apply() or Refute() methods have not failed.
"""
return _pywrapcp.SearchMonitor_AfterDecision(self, d, apply)
def BeginFail(self) -> "void":
r""" Just when the failure occurs."""
return _pywrapcp.SearchMonitor_BeginFail(self)
def EndFail(self) -> "void":
r""" After completing the backtrack."""
return _pywrapcp.SearchMonitor_EndFail(self)
def BeginInitialPropagation(self) -> "void":
r""" Before the initial propagation."""
return _pywrapcp.SearchMonitor_BeginInitialPropagation(self)
def EndInitialPropagation(self) -> "void":
r""" After the initial propagation."""
return _pywrapcp.SearchMonitor_EndInitialPropagation(self)
def AcceptSolution(self) -> "bool":
r"""
This method is called when a solution is found. It asserts whether the
solution is valid. A value of false indicates that the solution
should be discarded.
"""
return _pywrapcp.SearchMonitor_AcceptSolution(self)
def AtSolution(self) -> "bool":
r"""
This method is called when a valid solution is found. If the
return value is true, then search will resume after. If the result
is false, then search will stop there.
"""
return _pywrapcp.SearchMonitor_AtSolution(self)
def NoMoreSolutions(self) -> "void":
r""" When the search tree is finished."""
return _pywrapcp.SearchMonitor_NoMoreSolutions(self)
def LocalOptimum(self) -> "bool":
r"""
When a local optimum is reached. If 'true' is returned, the last solution
is discarded and the search proceeds with the next one.
"""
return _pywrapcp.SearchMonitor_LocalOptimum(self)
def AcceptDelta(self, delta: "Assignment", deltadelta: "Assignment") -> "bool":
return _pywrapcp.SearchMonitor_AcceptDelta(self, delta, deltadelta)
def AcceptNeighbor(self) -> "void":
r""" After accepting a neighbor during local search."""
return _pywrapcp.SearchMonitor_AcceptNeighbor(self)
def solver(self) -> "operations_research::Solver *":
return _pywrapcp.SearchMonitor_solver(self)
def __repr__(self) -> "std::string":
return _pywrapcp.SearchMonitor___repr__(self)
def __str__(self) -> "std::string":
return _pywrapcp.SearchMonitor___str__(self)
def __disown__(self):
self.this.disown()
_pywrapcp.disown_SearchMonitor(self)
return weakref.proxy(self)
# Register SearchMonitor in _pywrapcp:
_pywrapcp.SearchMonitor_swigregister(SearchMonitor)
class IntExpr(PropagationBaseObject):
r"""
The class IntExpr is the base of all integer expressions in
constraint programming.
It contains the basic protocol for an expression:
- setting and modifying its bound
- querying if it is bound
- listening to events modifying its bounds
- casting it into a variable (instance of IntVar)
"""
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
def __init__(self, *args, **kwargs):
raise AttributeError("No constructor defined - class is abstract")
def Min(self) -> "int64_t":
return _pywrapcp.IntExpr_Min(self)
def SetMin(self, m: "int64_t") -> "void":
return _pywrapcp.IntExpr_SetMin(self, m)
def Max(self) -> "int64_t":
return _pywrapcp.IntExpr_Max(self)
def SetMax(self, m: "int64_t") -> "void":
return _pywrapcp.IntExpr_SetMax(self, m)
def SetRange(self, l: "int64_t", u: "int64_t") -> "void":
r""" This method sets both the min and the max of the expression."""
return _pywrapcp.IntExpr_SetRange(self, l, u)
def SetValue(self, v: "int64_t") -> "void":
r""" This method sets the value of the expression."""
return _pywrapcp.IntExpr_SetValue(self, v)
def Bound(self) -> "bool":
r""" Returns true if the min and the max of the expression are equal."""
return _pywrapcp.IntExpr_Bound(self)
def IsVar(self) -> "bool":
r""" Returns true if the expression is indeed a variable."""
return _pywrapcp.IntExpr_IsVar(self)
def Var(self) -> "operations_research::IntVar *":
r""" Creates a variable from the expression."""
return _pywrapcp.IntExpr_Var(self)
def VarWithName(self, name: "std::string const &") -> "operations_research::IntVar *":
r"""
Creates a variable from the expression and set the name of the
resulting var. If the expression is already a variable, then it
will set the name of the expression, possibly overwriting it.
This is just a shortcut to Var() followed by set_name().
"""
return _pywrapcp.IntExpr_VarWithName(self, name)
def WhenRange(self, *args) -> "void":
r"""
*Overload 1:*
Attach a demon that will watch the min or the max of the expression.
|
*Overload 2:*
Attach a demon that will watch the min or the max of the expression.
"""
return _pywrapcp.IntExpr_WhenRange(self, *args)
def __repr__(self) -> "std::string":
return _pywrapcp.IntExpr___repr__(self)
def __str__(self) -> "std::string":
return _pywrapcp.IntExpr___str__(self)
def __add__(self, *args) -> "operations_research::IntExpr *":
return _pywrapcp.IntExpr___add__(self, *args)
def __radd__(self, v: "int64_t") -> "operations_research::IntExpr *":
return _pywrapcp.IntExpr___radd__(self, v)
def __sub__(self, *args) -> "operations_research::IntExpr *":
return _pywrapcp.IntExpr___sub__(self, *args)
def __rsub__(self, v: "int64_t") -> "operations_research::IntExpr *":
return _pywrapcp.IntExpr___rsub__(self, v)
def __mul__(self, *args) -> "operations_research::IntExpr *":
return _pywrapcp.IntExpr___mul__(self, *args)
def __rmul__(self, v: "int64_t") -> "operations_research::IntExpr *":
return _pywrapcp.IntExpr___rmul__(self, v)
def __floordiv__(self, *args) -> "operations_research::IntExpr *":
return _pywrapcp.IntExpr___floordiv__(self, *args)
def __mod__(self, *args) -> "operations_research::IntExpr *":
return _pywrapcp.IntExpr___mod__(self, *args)
def __neg__(self) -> "operations_research::IntExpr *":
return _pywrapcp.IntExpr___neg__(self)
def __abs__(self) -> "operations_research::IntExpr *":
return _pywrapcp.IntExpr___abs__(self)
def Square(self) -> "operations_research::IntExpr *":
return _pywrapcp.IntExpr_Square(self)
def __eq__(self, *args) -> "operations_research::Constraint *":
return _pywrapcp.IntExpr___eq__(self, *args)
def __ne__(self, *args) -> "operations_research::Constraint *":
return _pywrapcp.IntExpr___ne__(self, *args)
def __ge__(self, *args) -> "operations_research::Constraint *":
return _pywrapcp.IntExpr___ge__(self, *args)
def __gt__(self, *args) -> "operations_research::Constraint *":
return _pywrapcp.IntExpr___gt__(self, *args)
def __le__(self, *args) -> "operations_research::Constraint *":
return _pywrapcp.IntExpr___le__(self, *args)
def __lt__(self, *args) -> "operations_research::Constraint *":
return _pywrapcp.IntExpr___lt__(self, *args)
def MapTo(self, vars: "std::vector< operations_research::IntVar * > const &") -> "operations_research::Constraint *":
return _pywrapcp.IntExpr_MapTo(self, vars)
def IndexOf(self, *args) -> "operations_research::IntExpr *":
return _pywrapcp.IntExpr_IndexOf(self, *args)
def IsMember(self, values: "std::vector< int64_t > const &") -> "operations_research::IntVar *":
return _pywrapcp.IntExpr_IsMember(self, values)
def Member(self, values: "std::vector< int64_t > const &") -> "operations_research::Constraint *":
return _pywrapcp.IntExpr_Member(self, values)
def NotMember(self, starts: "std::vector< int64_t > const &", ends: "std::vector< int64_t > const &") -> "operations_research::Constraint *":
return _pywrapcp.IntExpr_NotMember(self, starts, ends)
# Register IntExpr in _pywrapcp:
_pywrapcp.IntExpr_swigregister(IntExpr)
class IntVarIterator(BaseObject):
r"""
The class Iterator has two direct subclasses. HoleIterators
iterates over all holes, that is value removed between the
current min and max of the variable since the last time the
variable was processed in the queue. DomainIterators iterates
over all elements of the variable domain. Both iterators are not
robust to domain changes. Hole iterators can also report values outside
the current min and max of the variable.
HoleIterators should only be called from a demon attached to the
variable that has created this iterator.
IntVar* current_var;
std::unique_ptr<IntVarIterator> it(current_var->MakeHoleIterator(false));
for (const int64_t hole : InitAndGetValues(it)) {
use the hole
}
"""
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
def __init__(self, *args, **kwargs):
raise AttributeError("No constructor defined - class is abstract")
__repr__ = _swig_repr
def Init(self) -> "void":
r""" This method must be called before each loop."""
return _pywrapcp.IntVarIterator_Init(self)
def Ok(self) -> "bool":
r""" This method indicates if we can call Value() or not."""
return _pywrapcp.IntVarIterator_Ok(self)
def Value(self) -> "int64_t":
r""" This method returns the current value of the iterator."""
return _pywrapcp.IntVarIterator_Value(self)
def Next(self) -> "void":
r""" This method moves the iterator to the next value."""
return _pywrapcp.IntVarIterator_Next(self)
def DebugString(self) -> "std::string":
r""" Pretty Print."""
return _pywrapcp.IntVarIterator_DebugString(self)
def __iter__(self):
self.Init()
return self
def next(self):
if self.Ok():
result = self.Value()
self.Next()
return result
else:
raise StopIteration()
def __next__(self):
return self.next()
# Register IntVarIterator in _pywrapcp:
_pywrapcp.IntVarIterator_swigregister(IntVarIterator)
class IntVar(IntExpr):
r"""
The class IntVar is a subset of IntExpr. In addition to the
IntExpr protocol, it offers persistence, removing values from the domains,
and a finer model for events.
"""
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
def __init__(self, *args, **kwargs):
raise AttributeError("No constructor defined - class is abstract")
def IsVar(self) -> "bool":
return _pywrapcp.IntVar_IsVar(self)
def Var(self) -> "operations_research::IntVar *":
return _pywrapcp.IntVar_Var(self)
def Value(self) -> "int64_t":
r"""
This method returns the value of the variable. This method checks
before that the variable is bound.
"""
return _pywrapcp.IntVar_Value(self)
def RemoveValue(self, v: "int64_t") -> "void":
r""" This method removes the value 'v' from the domain of the variable."""
return _pywrapcp.IntVar_RemoveValue(self, v)
def RemoveInterval(self, l: "int64_t", u: "int64_t") -> "void":
r"""
This method removes the interval 'l' .. 'u' from the domain of
the variable. It assumes that 'l' <= 'u'.
"""
return _pywrapcp.IntVar_RemoveInterval(self, l, u)
def RemoveValues(self, values: "std::vector< int64_t > const &") -> "void":
r""" This method remove the values from the domain of the variable."""
return _pywrapcp.IntVar_RemoveValues(self, values)
def SetValues(self, values: "std::vector< int64_t > const &") -> "void":
r""" This method intersects the current domain with the values in the array."""
return _pywrapcp.IntVar_SetValues(self, values)
def WhenBound(self, *args) -> "void":
r"""
*Overload 1:*
This method attaches a demon that will be awakened when the
variable is bound.
|
*Overload 2:*
This method attaches a closure that will be awakened when the
variable is bound.
"""
return _pywrapcp.IntVar_WhenBound(self, *args)
def WhenDomain(self, *args) -> "void":
r"""
*Overload 1:*
This method attaches a demon that will watch any domain
modification of the domain of the variable.
|
*Overload 2:*
This method attaches a closure that will watch any domain
modification of the domain of the variable.
"""
return _pywrapcp.IntVar_WhenDomain(self, *args)
def Size(self) -> "uint64_t":
r""" This method returns the number of values in the domain of the variable."""
return _pywrapcp.IntVar_Size(self)
def Contains(self, v: "int64_t") -> "bool":
r"""
This method returns whether the value 'v' is in the domain of the
variable.
"""
return _pywrapcp.IntVar_Contains(self, v)
def HoleIteratorAux(self, reversible: "bool") -> "operations_research::IntVarIterator *":
r"""
Creates a hole iterator. When 'reversible' is false, the returned
object is created on the normal C++ heap and the solver does NOT
take ownership of the object.
"""
return _pywrapcp.IntVar_HoleIteratorAux(self, reversible)
def DomainIteratorAux(self, reversible: "bool") -> "operations_research::IntVarIterator *":
r"""
Creates a domain iterator. When 'reversible' is false, the
returned object is created on the normal C++ heap and the solver
does NOT take ownership of the object.
"""
return _pywrapcp.IntVar_DomainIteratorAux(self, reversible)
def OldMin(self) -> "int64_t":
r""" Returns the previous min."""
return _pywrapcp.IntVar_OldMin(self)
def OldMax(self) -> "int64_t":
r""" Returns the previous max."""
return _pywrapcp.IntVar_OldMax(self)
def __repr__(self) -> "std::string":
return _pywrapcp.IntVar___repr__(self)
def __str__(self) -> "std::string":
return _pywrapcp.IntVar___str__(self)
def DomainIterator(self):
return iter(self.DomainIteratorAux(False))
def HoleIterator(self):
return iter(self.HoleIteratorAux(False))
# Register IntVar in _pywrapcp:
_pywrapcp.IntVar_swigregister(IntVar)
class SolutionCollector(SearchMonitor):
r"""
This class is the root class of all solution collectors.
It implements a basic query API to be used independently
of the collector used.
"""
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
def __init__(self, *args, **kwargs):
raise AttributeError("No constructor defined")
__repr__ = _swig_repr
def DebugString(self) -> "std::string":
return _pywrapcp.SolutionCollector_DebugString(self)
def Add(self, *args) -> "void":
return _pywrapcp.SolutionCollector_Add(self, *args)
def AddObjective(self, objective: "IntVar") -> "void":
return _pywrapcp.SolutionCollector_AddObjective(self, objective)
def EnterSearch(self) -> "void":
r""" Beginning of the search."""
return _pywrapcp.SolutionCollector_EnterSearch(self)
def SolutionCount(self) -> "int":
r""" Returns how many solutions were stored during the search."""
return _pywrapcp.SolutionCollector_SolutionCount(self)
def Solution(self, n: "int") -> "operations_research::Assignment *":
r""" Returns the nth solution."""
return _pywrapcp.SolutionCollector_Solution(self, n)
def WallTime(self, n: "int") -> "int64_t":
r""" Returns the wall time in ms for the nth solution."""
return _pywrapcp.SolutionCollector_WallTime(self, n)
def Branches(self, n: "int") -> "int64_t":
r""" Returns the number of branches when the nth solution was found."""
return _pywrapcp.SolutionCollector_Branches(self, n)
def Failures(self, n: "int") -> "int64_t":
r"""
Returns the number of failures encountered at the time of the nth
solution.
"""
return _pywrapcp.SolutionCollector_Failures(self, n)
def ObjectiveValue(self, n: "int") -> "int64_t":
r""" Returns the objective value of the nth solution."""
return _pywrapcp.SolutionCollector_ObjectiveValue(self, n)
def Value(self, n: "int", var: "IntVar") -> "int64_t":
r""" This is a shortcut to get the Value of 'var' in the nth solution."""
return _pywrapcp.SolutionCollector_Value(self, n, var)
def StartValue(self, n: "int", var: "IntervalVar") -> "int64_t":
r""" This is a shortcut to get the StartValue of 'var' in the nth solution."""
return _pywrapcp.SolutionCollector_StartValue(self, n, var)
def EndValue(self, n: "int", var: "IntervalVar") -> "int64_t":
r""" This is a shortcut to get the EndValue of 'var' in the nth solution."""
return _pywrapcp.SolutionCollector_EndValue(self, n, var)
def DurationValue(self, n: "int", var: "IntervalVar") -> "int64_t":
r""" This is a shortcut to get the DurationValue of 'var' in the nth solution."""
return _pywrapcp.SolutionCollector_DurationValue(self, n, var)
def PerformedValue(self, n: "int", var: "IntervalVar") -> "int64_t":
r""" This is a shortcut to get the PerformedValue of 'var' in the nth solution."""
return _pywrapcp.SolutionCollector_PerformedValue(self, n, var)
def ForwardSequence(self, n: "int", var: "SequenceVar") -> "std::vector< int > const &":
r"""
This is a shortcut to get the ForwardSequence of 'var' in the
nth solution. The forward sequence is the list of ranked interval
variables starting from the start of the sequence.
"""
return _pywrapcp.SolutionCollector_ForwardSequence(self, n, var)
def BackwardSequence(self, n: "int", var: "SequenceVar") -> "std::vector< int > const &":
r"""
This is a shortcut to get the BackwardSequence of 'var' in the
nth solution. The backward sequence is the list of ranked interval
variables starting from the end of the sequence.
"""
return _pywrapcp.SolutionCollector_BackwardSequence(self, n, var)
def Unperformed(self, n: "int", var: "SequenceVar") -> "std::vector< int > const &":
r"""
This is a shortcut to get the list of unperformed of 'var' in the
nth solution.
"""
return _pywrapcp.SolutionCollector_Unperformed(self, n, var)
# Register SolutionCollector in _pywrapcp:
_pywrapcp.SolutionCollector_swigregister(SolutionCollector)
class OptimizeVar(SearchMonitor):
r"""
This class encapsulates an objective. It requires the direction
(minimize or maximize), the variable to optimize, and the
improvement step.
"""
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
def __init__(self, *args, **kwargs):
raise AttributeError("No constructor defined")
__repr__ = _swig_repr
def Best(self) -> "int64_t":
r""" Returns the best value found during search."""
return _pywrapcp.OptimizeVar_Best(self)
def Var(self) -> "operations_research::IntVar *":
r""" Returns the variable that is optimized."""
return _pywrapcp.OptimizeVar_Var(self)
def AcceptDelta(self, delta: "Assignment", deltadelta: "Assignment") -> "bool":
r""" Internal methods."""
return _pywrapcp.OptimizeVar_AcceptDelta(self, delta, deltadelta)
def EnterSearch(self) -> "void":
return _pywrapcp.OptimizeVar_EnterSearch(self)
def BeginNextDecision(self, db: "DecisionBuilder") -> "void":
return _pywrapcp.OptimizeVar_BeginNextDecision(self, db)
def RefuteDecision(self, d: "Decision") -> "void":
return _pywrapcp.OptimizeVar_RefuteDecision(self, d)
def AtSolution(self) -> "bool":
return _pywrapcp.OptimizeVar_AtSolution(self)
def AcceptSolution(self) -> "bool":
return _pywrapcp.OptimizeVar_AcceptSolution(self)
def DebugString(self) -> "std::string":
return _pywrapcp.OptimizeVar_DebugString(self)
# Register OptimizeVar in _pywrapcp:
_pywrapcp.OptimizeVar_swigregister(OptimizeVar)
class SearchLimit(SearchMonitor):
r""" Base class of all search limits."""
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
def __init__(self, *args, **kwargs):
raise AttributeError("No constructor defined - class is abstract")
__repr__ = _swig_repr
__swig_destroy__ = _pywrapcp.delete_SearchLimit
def Crossed(self) -> "bool":
r""" Returns true if the limit has been crossed."""
return _pywrapcp.SearchLimit_Crossed(self)
def Check(self) -> "bool":
r"""
This method is called to check the status of the limit. A return
value of true indicates that we have indeed crossed the limit. In
that case, this method will not be called again and the remaining
search will be discarded.
"""
return _pywrapcp.SearchLimit_Check(self)
def Init(self) -> "void":
r""" This method is called when the search limit is initialized."""
return _pywrapcp.SearchLimit_Init(self)
def EnterSearch(self) -> "void":
r""" Internal methods."""
return _pywrapcp.SearchLimit_EnterSearch(self)
def BeginNextDecision(self, b: "DecisionBuilder") -> "void":
return _pywrapcp.SearchLimit_BeginNextDecision(self, b)
def RefuteDecision(self, d: "Decision") -> "void":
return _pywrapcp.SearchLimit_RefuteDecision(self, d)
def DebugString(self) -> "std::string":
return _pywrapcp.SearchLimit_DebugString(self)
# Register SearchLimit in _pywrapcp:
_pywrapcp.SearchLimit_swigregister(SearchLimit)
class IntervalVar(PropagationBaseObject):
r"""
Interval variables are often used in scheduling. The main characteristics
of an IntervalVar are the start position, duration, and end
date. All these characteristics can be queried and set, and demons can
be posted on their modifications.
An important aspect is optionality: an IntervalVar can be performed or not.
If unperformed, then it simply does not exist, and its characteristics
cannot be accessed any more. An interval var is automatically marked
as unperformed when it is not consistent anymore (start greater
than end, duration < 0...)
"""
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
def __init__(self, *args, **kwargs):
raise AttributeError("No constructor defined - class is abstract")
def StartMin(self) -> "int64_t":
r"""
These methods query, set, and watch the start position of the
interval var.
"""
return _pywrapcp.IntervalVar_StartMin(self)
def StartMax(self) -> "int64_t":
return _pywrapcp.IntervalVar_StartMax(self)
def SetStartMin(self, m: "int64_t") -> "void":
return _pywrapcp.IntervalVar_SetStartMin(self, m)
def SetStartMax(self, m: "int64_t") -> "void":
return _pywrapcp.IntervalVar_SetStartMax(self, m)
def SetStartRange(self, mi: "int64_t", ma: "int64_t") -> "void":
return _pywrapcp.IntervalVar_SetStartRange(self, mi, ma)
def OldStartMin(self) -> "int64_t":
return _pywrapcp.IntervalVar_OldStartMin(self)
def OldStartMax(self) -> "int64_t":
return _pywrapcp.IntervalVar_OldStartMax(self)
def WhenStartRange(self, *args) -> "void":
return _pywrapcp.IntervalVar_WhenStartRange(self, *args)
def WhenStartBound(self, *args) -> "void":
return _pywrapcp.IntervalVar_WhenStartBound(self, *args)
def DurationMin(self) -> "int64_t":
r""" These methods query, set, and watch the duration of the interval var."""
return _pywrapcp.IntervalVar_DurationMin(self)
def DurationMax(self) -> "int64_t":
return _pywrapcp.IntervalVar_DurationMax(self)
def SetDurationMin(self, m: "int64_t") -> "void":
return _pywrapcp.IntervalVar_SetDurationMin(self, m)
def SetDurationMax(self, m: "int64_t") -> "void":
return _pywrapcp.IntervalVar_SetDurationMax(self, m)
def SetDurationRange(self, mi: "int64_t", ma: "int64_t") -> "void":
return _pywrapcp.IntervalVar_SetDurationRange(self, mi, ma)
def OldDurationMin(self) -> "int64_t":
return _pywrapcp.IntervalVar_OldDurationMin(self)
def OldDurationMax(self) -> "int64_t":
return _pywrapcp.IntervalVar_OldDurationMax(self)
def WhenDurationRange(self, *args) -> "void":
return _pywrapcp.IntervalVar_WhenDurationRange(self, *args)
def WhenDurationBound(self, *args) -> "void":
return _pywrapcp.IntervalVar_WhenDurationBound(self, *args)
def EndMin(self) -> "int64_t":
r""" These methods query, set, and watch the end position of the interval var."""
return _pywrapcp.IntervalVar_EndMin(self)
def EndMax(self) -> "int64_t":
return _pywrapcp.IntervalVar_EndMax(self)
def SetEndMin(self, m: "int64_t") -> "void":
return _pywrapcp.IntervalVar_SetEndMin(self, m)
def SetEndMax(self, m: "int64_t") -> "void":
return _pywrapcp.IntervalVar_SetEndMax(self, m)
def SetEndRange(self, mi: "int64_t", ma: "int64_t") -> "void":
return _pywrapcp.IntervalVar_SetEndRange(self, mi, ma)
def OldEndMin(self) -> "int64_t":
return _pywrapcp.IntervalVar_OldEndMin(self)
def OldEndMax(self) -> "int64_t":
return _pywrapcp.IntervalVar_OldEndMax(self)
def WhenEndRange(self, *args) -> "void":
return _pywrapcp.IntervalVar_WhenEndRange(self, *args)
def WhenEndBound(self, *args) -> "void":
return _pywrapcp.IntervalVar_WhenEndBound(self, *args)
def MustBePerformed(self) -> "bool":
r"""
These methods query, set, and watch the performed status of the
interval var.
"""
return _pywrapcp.IntervalVar_MustBePerformed(self)
def MayBePerformed(self) -> "bool":
return _pywrapcp.IntervalVar_MayBePerformed(self)
def CannotBePerformed(self) -> "bool":
return _pywrapcp.IntervalVar_CannotBePerformed(self)
def IsPerformedBound(self) -> "bool":
return _pywrapcp.IntervalVar_IsPerformedBound(self)
def SetPerformed(self, val: "bool") -> "void":
return _pywrapcp.IntervalVar_SetPerformed(self, val)
def WasPerformedBound(self) -> "bool":
return _pywrapcp.IntervalVar_WasPerformedBound(self)
def WhenPerformedBound(self, *args) -> "void":
return _pywrapcp.IntervalVar_WhenPerformedBound(self, *args)
def WhenAnything(self, *args) -> "void":
r"""
*Overload 1:*
Attaches a demon awakened when anything about this interval changes.
|
*Overload 2:*
Attaches a closure awakened when anything about this interval changes.
"""
return _pywrapcp.IntervalVar_WhenAnything(self, *args)
def StartExpr(self) -> "operations_research::IntExpr *":
r"""
These methods create expressions encapsulating the start, end
and duration of the interval var. Please note that these must not
be used if the interval var is unperformed.
"""
return _pywrapcp.IntervalVar_StartExpr(self)
def DurationExpr(self) -> "operations_research::IntExpr *":
return _pywrapcp.IntervalVar_DurationExpr(self)
def EndExpr(self) -> "operations_research::IntExpr *":
return _pywrapcp.IntervalVar_EndExpr(self)
def PerformedExpr(self) -> "operations_research::IntExpr *":
return _pywrapcp.IntervalVar_PerformedExpr(self)
def SafeStartExpr(self, unperformed_value: "int64_t") -> "operations_research::IntExpr *":
r"""
These methods create expressions encapsulating the start, end
and duration of the interval var. If the interval var is
unperformed, they will return the unperformed_value.
"""
return _pywrapcp.IntervalVar_SafeStartExpr(self, unperformed_value)
def SafeDurationExpr(self, unperformed_value: "int64_t") -> "operations_research::IntExpr *":
return _pywrapcp.IntervalVar_SafeDurationExpr(self, unperformed_value)
def SafeEndExpr(self, unperformed_value: "int64_t") -> "operations_research::IntExpr *":
return _pywrapcp.IntervalVar_SafeEndExpr(self, unperformed_value)
def EndsAfterEnd(self, other: "IntervalVar") -> "operations_research::Constraint *":
return _pywrapcp.IntervalVar_EndsAfterEnd(self, other)
def EndsAfterEndWithDelay(self, other: "IntervalVar", delay: "int64_t") -> "operations_research::Constraint *":
return _pywrapcp.IntervalVar_EndsAfterEndWithDelay(self, other, delay)
def EndsAfterStart(self, other: "IntervalVar") -> "operations_research::Constraint *":
return _pywrapcp.IntervalVar_EndsAfterStart(self, other)
def EndsAfterStartWithDelay(self, other: "IntervalVar", delay: "int64_t") -> "operations_research::Constraint *":
return _pywrapcp.IntervalVar_EndsAfterStartWithDelay(self, other, delay)
def EndsAtEnd(self, other: "IntervalVar") -> "operations_research::Constraint *":
return _pywrapcp.IntervalVar_EndsAtEnd(self, other)
def EndsAtEndWithDelay(self, other: "IntervalVar", delay: "int64_t") -> "operations_research::Constraint *":
return _pywrapcp.IntervalVar_EndsAtEndWithDelay(self, other, delay)
def EndsAtStart(self, other: "IntervalVar") -> "operations_research::Constraint *":
return _pywrapcp.IntervalVar_EndsAtStart(self, other)
def EndsAtStartWithDelay(self, other: "IntervalVar", delay: "int64_t") -> "operations_research::Constraint *":
return _pywrapcp.IntervalVar_EndsAtStartWithDelay(self, other, delay)
def StartsAfterEnd(self, other: "IntervalVar") -> "operations_research::Constraint *":
return _pywrapcp.IntervalVar_StartsAfterEnd(self, other)
def StartsAfterEndWithDelay(self, other: "IntervalVar", delay: "int64_t") -> "operations_research::Constraint *":
return _pywrapcp.IntervalVar_StartsAfterEndWithDelay(self, other, delay)
def StartsAfterStart(self, other: "IntervalVar") -> "operations_research::Constraint *":
return _pywrapcp.IntervalVar_StartsAfterStart(self, other)
def StartsAfterStartWithDelay(self, other: "IntervalVar", delay: "int64_t") -> "operations_research::Constraint *":
return _pywrapcp.IntervalVar_StartsAfterStartWithDelay(self, other, delay)
def StartsAtEnd(self, other: "IntervalVar") -> "operations_research::Constraint *":
return _pywrapcp.IntervalVar_StartsAtEnd(self, other)
def StartsAtEndWithDelay(self, other: "IntervalVar", delay: "int64_t") -> "operations_research::Constraint *":
return _pywrapcp.IntervalVar_StartsAtEndWithDelay(self, other, delay)
def StartsAtStart(self, other: "IntervalVar") -> "operations_research::Constraint *":
return _pywrapcp.IntervalVar_StartsAtStart(self, other)
def StartsAtStartWithDelay(self, other: "IntervalVar", delay: "int64_t") -> "operations_research::Constraint *":
return _pywrapcp.IntervalVar_StartsAtStartWithDelay(self, other, delay)
def StaysInSync(self, other: "IntervalVar") -> "operations_research::Constraint *":
return _pywrapcp.IntervalVar_StaysInSync(self, other)
def StaysInSyncWithDelay(self, other: "IntervalVar", delay: "int64_t") -> "operations_research::Constraint *":
return _pywrapcp.IntervalVar_StaysInSyncWithDelay(self, other, delay)
def EndsAfter(self, date: "int64_t") -> "operations_research::Constraint *":
return _pywrapcp.IntervalVar_EndsAfter(self, date)
def EndsAt(self, date: "int64_t") -> "operations_research::Constraint *":
return _pywrapcp.IntervalVar_EndsAt(self, date)
def EndsBefore(self, date: "int64_t") -> "operations_research::Constraint *":
return _pywrapcp.IntervalVar_EndsBefore(self, date)
def StartsAfter(self, date: "int64_t") -> "operations_research::Constraint *":
return _pywrapcp.IntervalVar_StartsAfter(self, date)
def StartsAt(self, date: "int64_t") -> "operations_research::Constraint *":
return _pywrapcp.IntervalVar_StartsAt(self, date)
def StartsBefore(self, date: "int64_t") -> "operations_research::Constraint *":
return _pywrapcp.IntervalVar_StartsBefore(self, date)
def CrossesDate(self, date: "int64_t") -> "operations_research::Constraint *":
return _pywrapcp.IntervalVar_CrossesDate(self, date)
def AvoidsDate(self, date: "int64_t") -> "operations_research::Constraint *":
return _pywrapcp.IntervalVar_AvoidsDate(self, date)
def __repr__(self) -> "std::string":
return _pywrapcp.IntervalVar___repr__(self)
def __str__(self) -> "std::string":
return _pywrapcp.IntervalVar___str__(self)
# Register IntervalVar in _pywrapcp:
_pywrapcp.IntervalVar_swigregister(IntervalVar)
class SequenceVar(PropagationBaseObject):
r"""
A sequence variable is a variable whose domain is a set of possible
orderings of the interval variables. It allows ordering of tasks. It
has two sets of methods: ComputePossibleFirstsAndLasts(), which
returns the list of interval variables that can be ranked first or
last; and RankFirst/RankNotFirst/RankLast/RankNotLast, which can be
used to create the search decision.
"""
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
def __init__(self, *args, **kwargs):
raise AttributeError("No constructor defined")
def DebugString(self) -> "std::string":
return _pywrapcp.SequenceVar_DebugString(self)
def RankFirst(self, index: "int") -> "void":
r"""
Ranks the index_th interval var first of all unranked interval
vars. After that, it will no longer be considered ranked.
"""
return _pywrapcp.SequenceVar_RankFirst(self, index)
def RankNotFirst(self, index: "int") -> "void":
r"""
Indicates that the index_th interval var will not be ranked first
of all currently unranked interval vars.
"""
return _pywrapcp.SequenceVar_RankNotFirst(self, index)
def RankLast(self, index: "int") -> "void":
r"""
Ranks the index_th interval var first of all unranked interval
vars. After that, it will no longer be considered ranked.
"""
return _pywrapcp.SequenceVar_RankLast(self, index)
def RankNotLast(self, index: "int") -> "void":
r"""
Indicates that the index_th interval var will not be ranked first
of all currently unranked interval vars.
"""
return _pywrapcp.SequenceVar_RankNotLast(self, index)
def Interval(self, index: "int") -> "operations_research::IntervalVar *":
r""" Returns the index_th interval of the sequence."""
return _pywrapcp.SequenceVar_Interval(self, index)
def Next(self, index: "int") -> "operations_research::IntVar *":
r""" Returns the next of the index_th interval of the sequence."""
return _pywrapcp.SequenceVar_Next(self, index)
def Size(self) -> "int64_t":
r""" Returns the number of interval vars in the sequence."""
return _pywrapcp.SequenceVar_Size(self)
def __repr__(self) -> "std::string":
return _pywrapcp.SequenceVar___repr__(self)
def __str__(self) -> "std::string":
return _pywrapcp.SequenceVar___str__(self)
# Register SequenceVar in _pywrapcp:
_pywrapcp.SequenceVar_swigregister(SequenceVar)
class AssignmentElement(object):
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
def __init__(self, *args, **kwargs):
raise AttributeError("No constructor defined")
__repr__ = _swig_repr
def Activate(self) -> "void":
return _pywrapcp.AssignmentElement_Activate(self)
def Deactivate(self) -> "void":
return _pywrapcp.AssignmentElement_Deactivate(self)
def Activated(self) -> "bool":
return _pywrapcp.AssignmentElement_Activated(self)
__swig_destroy__ = _pywrapcp.delete_AssignmentElement
# Register AssignmentElement in _pywrapcp:
_pywrapcp.AssignmentElement_swigregister(AssignmentElement)
class IntVarElement(AssignmentElement):
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
def __init__(self, *args, **kwargs):
raise AttributeError("No constructor defined")
__repr__ = _swig_repr
def Var(self) -> "operations_research::IntVar *":
return _pywrapcp.IntVarElement_Var(self)
def Min(self) -> "int64_t":
return _pywrapcp.IntVarElement_Min(self)
def SetMin(self, m: "int64_t") -> "void":
return _pywrapcp.IntVarElement_SetMin(self, m)
def Max(self) -> "int64_t":
return _pywrapcp.IntVarElement_Max(self)
def SetMax(self, m: "int64_t") -> "void":
return _pywrapcp.IntVarElement_SetMax(self, m)
def Value(self) -> "int64_t":
return _pywrapcp.IntVarElement_Value(self)
def Bound(self) -> "bool":
return _pywrapcp.IntVarElement_Bound(self)
def SetRange(self, l: "int64_t", u: "int64_t") -> "void":
return _pywrapcp.IntVarElement_SetRange(self, l, u)
def SetValue(self, v: "int64_t") -> "void":
return _pywrapcp.IntVarElement_SetValue(self, v)
def __eq__(self, element: "IntVarElement") -> "bool":
return _pywrapcp.IntVarElement___eq__(self, element)
def __ne__(self, element: "IntVarElement") -> "bool":
return _pywrapcp.IntVarElement___ne__(self, element)
__swig_destroy__ = _pywrapcp.delete_IntVarElement
# Register IntVarElement in _pywrapcp:
_pywrapcp.IntVarElement_swigregister(IntVarElement)
class IntervalVarElement(AssignmentElement):
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
def __init__(self, *args, **kwargs):
raise AttributeError("No constructor defined")
__repr__ = _swig_repr
def Var(self) -> "operations_research::IntervalVar *":
return _pywrapcp.IntervalVarElement_Var(self)
def StartMin(self) -> "int64_t":
return _pywrapcp.IntervalVarElement_StartMin(self)
def StartMax(self) -> "int64_t":
return _pywrapcp.IntervalVarElement_StartMax(self)
def StartValue(self) -> "int64_t":
return _pywrapcp.IntervalVarElement_StartValue(self)
def DurationMin(self) -> "int64_t":
return _pywrapcp.IntervalVarElement_DurationMin(self)
def DurationMax(self) -> "int64_t":
return _pywrapcp.IntervalVarElement_DurationMax(self)
def DurationValue(self) -> "int64_t":
return _pywrapcp.IntervalVarElement_DurationValue(self)
def EndMin(self) -> "int64_t":
return _pywrapcp.IntervalVarElement_EndMin(self)
def EndMax(self) -> "int64_t":
return _pywrapcp.IntervalVarElement_EndMax(self)
def EndValue(self) -> "int64_t":
return _pywrapcp.IntervalVarElement_EndValue(self)
def PerformedMin(self) -> "int64_t":
return _pywrapcp.IntervalVarElement_PerformedMin(self)
def PerformedMax(self) -> "int64_t":
return _pywrapcp.IntervalVarElement_PerformedMax(self)
def PerformedValue(self) -> "int64_t":
return _pywrapcp.IntervalVarElement_PerformedValue(self)
def SetStartMin(self, m: "int64_t") -> "void":
return _pywrapcp.IntervalVarElement_SetStartMin(self, m)
def SetStartMax(self, m: "int64_t") -> "void":
return _pywrapcp.IntervalVarElement_SetStartMax(self, m)
def SetStartRange(self, mi: "int64_t", ma: "int64_t") -> "void":
return _pywrapcp.IntervalVarElement_SetStartRange(self, mi, ma)
def SetStartValue(self, v: "int64_t") -> "void":
return _pywrapcp.IntervalVarElement_SetStartValue(self, v)
def SetDurationMin(self, m: "int64_t") -> "void":
return _pywrapcp.IntervalVarElement_SetDurationMin(self, m)
def SetDurationMax(self, m: "int64_t") -> "void":
return _pywrapcp.IntervalVarElement_SetDurationMax(self, m)
def SetDurationRange(self, mi: "int64_t", ma: "int64_t") -> "void":
return _pywrapcp.IntervalVarElement_SetDurationRange(self, mi, ma)
def SetDurationValue(self, v: "int64_t") -> "void":
return _pywrapcp.IntervalVarElement_SetDurationValue(self, v)
def SetEndMin(self, m: "int64_t") -> "void":
return _pywrapcp.IntervalVarElement_SetEndMin(self, m)
def SetEndMax(self, m: "int64_t") -> "void":
return _pywrapcp.IntervalVarElement_SetEndMax(self, m)
def SetEndRange(self, mi: "int64_t", ma: "int64_t") -> "void":
return _pywrapcp.IntervalVarElement_SetEndRange(self, mi, ma)
def SetEndValue(self, v: "int64_t") -> "void":
return _pywrapcp.IntervalVarElement_SetEndValue(self, v)
def SetPerformedMin(self, m: "int64_t") -> "void":
return _pywrapcp.IntervalVarElement_SetPerformedMin(self, m)
def SetPerformedMax(self, m: "int64_t") -> "void":
return _pywrapcp.IntervalVarElement_SetPerformedMax(self, m)
def SetPerformedRange(self, mi: "int64_t", ma: "int64_t") -> "void":
return _pywrapcp.IntervalVarElement_SetPerformedRange(self, mi, ma)
def SetPerformedValue(self, v: "int64_t") -> "void":
return _pywrapcp.IntervalVarElement_SetPerformedValue(self, v)
def __eq__(self, element: "IntervalVarElement") -> "bool":
return _pywrapcp.IntervalVarElement___eq__(self, element)
def __ne__(self, element: "IntervalVarElement") -> "bool":
return _pywrapcp.IntervalVarElement___ne__(self, element)
__swig_destroy__ = _pywrapcp.delete_IntervalVarElement
# Register IntervalVarElement in _pywrapcp:
_pywrapcp.IntervalVarElement_swigregister(IntervalVarElement)
class SequenceVarElement(AssignmentElement):
r"""
The SequenceVarElement stores a partial representation of ranked
interval variables in the underlying sequence variable.
This representation consists of three vectors:
- the forward sequence. That is the list of interval variables
ranked first in the sequence. The first element of the backward
sequence is the first interval in the sequence variable.
- the backward sequence. That is the list of interval variables
ranked last in the sequence. The first element of the backward
sequence is the last interval in the sequence variable.
- The list of unperformed interval variables.
Furthermore, if all performed variables are ranked, then by
convention, the forward_sequence will contain all such variables
and the backward_sequence will be empty.
"""
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
def __init__(self, *args, **kwargs):
raise AttributeError("No constructor defined")
__repr__ = _swig_repr
def Var(self) -> "operations_research::SequenceVar *":
return _pywrapcp.SequenceVarElement_Var(self)
def ForwardSequence(self) -> "std::vector< int > const &":
return _pywrapcp.SequenceVarElement_ForwardSequence(self)
def BackwardSequence(self) -> "std::vector< int > const &":
return _pywrapcp.SequenceVarElement_BackwardSequence(self)
def Unperformed(self) -> "std::vector< int > const &":
return _pywrapcp.SequenceVarElement_Unperformed(self)
def SetSequence(self, forward_sequence: "std::vector< int > const &", backward_sequence: "std::vector< int > const &", unperformed: "std::vector< int > const &") -> "void":
return _pywrapcp.SequenceVarElement_SetSequence(self, forward_sequence, backward_sequence, unperformed)
def SetForwardSequence(self, forward_sequence: "std::vector< int > const &") -> "void":
return _pywrapcp.SequenceVarElement_SetForwardSequence(self, forward_sequence)
def SetBackwardSequence(self, backward_sequence: "std::vector< int > const &") -> "void":
return _pywrapcp.SequenceVarElement_SetBackwardSequence(self, backward_sequence)
def SetUnperformed(self, unperformed: "std::vector< int > const &") -> "void":
return _pywrapcp.SequenceVarElement_SetUnperformed(self, unperformed)
def __eq__(self, element: "SequenceVarElement") -> "bool":
return _pywrapcp.SequenceVarElement___eq__(self, element)
def __ne__(self, element: "SequenceVarElement") -> "bool":
return _pywrapcp.SequenceVarElement___ne__(self, element)
__swig_destroy__ = _pywrapcp.delete_SequenceVarElement
# Register SequenceVarElement in _pywrapcp:
_pywrapcp.SequenceVarElement_swigregister(SequenceVarElement)
class Assignment(PropagationBaseObject):
r"""
An Assignment is a variable -> domains mapping, used
to report solutions to the user.
"""
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
def __init__(self, *args, **kwargs):
raise AttributeError("No constructor defined")
__repr__ = _swig_repr
def Clear(self) -> "void":
return _pywrapcp.Assignment_Clear(self)
def Empty(self) -> "bool":
return _pywrapcp.Assignment_Empty(self)
def Size(self) -> "int":
return _pywrapcp.Assignment_Size(self)
def NumIntVars(self) -> "int":
return _pywrapcp.Assignment_NumIntVars(self)
def NumIntervalVars(self) -> "int":
return _pywrapcp.Assignment_NumIntervalVars(self)
def NumSequenceVars(self) -> "int":
return _pywrapcp.Assignment_NumSequenceVars(self)
def Store(self) -> "void":
return _pywrapcp.Assignment_Store(self)
def Restore(self) -> "void":
return _pywrapcp.Assignment_Restore(self)
def Load(self, *args) -> "void":
return _pywrapcp.Assignment_Load(self, *args)
def Save(self, *args) -> "void":
return _pywrapcp.Assignment_Save(self, *args)
def AddObjective(self, v: "IntVar") -> "void":
return _pywrapcp.Assignment_AddObjective(self, v)
def Objective(self) -> "operations_research::IntVar *":
return _pywrapcp.Assignment_Objective(self)
def HasObjective(self) -> "bool":
return _pywrapcp.Assignment_HasObjective(self)
def ObjectiveMin(self) -> "int64_t":
return _pywrapcp.Assignment_ObjectiveMin(self)
def ObjectiveMax(self) -> "int64_t":
return _pywrapcp.Assignment_ObjectiveMax(self)
def ObjectiveValue(self) -> "int64_t":
return _pywrapcp.Assignment_ObjectiveValue(self)
def ObjectiveBound(self) -> "bool":
return _pywrapcp.Assignment_ObjectiveBound(self)
def SetObjectiveMin(self, m: "int64_t") -> "void":
return _pywrapcp.Assignment_SetObjectiveMin(self, m)
def SetObjectiveMax(self, m: "int64_t") -> "void":
return _pywrapcp.Assignment_SetObjectiveMax(self, m)
def SetObjectiveValue(self, value: "int64_t") -> "void":
return _pywrapcp.Assignment_SetObjectiveValue(self, value)
def SetObjectiveRange(self, l: "int64_t", u: "int64_t") -> "void":
return _pywrapcp.Assignment_SetObjectiveRange(self, l, u)
def Min(self, var: "IntVar") -> "int64_t":
return _pywrapcp.Assignment_Min(self, var)
def Max(self, var: "IntVar") -> "int64_t":
return _pywrapcp.Assignment_Max(self, var)
def Value(self, var: "IntVar") -> "int64_t":
return _pywrapcp.Assignment_Value(self, var)
def Bound(self, var: "IntVar") -> "bool":
return _pywrapcp.Assignment_Bound(self, var)
def SetMin(self, var: "IntVar", m: "int64_t") -> "void":
return _pywrapcp.Assignment_SetMin(self, var, m)
def SetMax(self, var: "IntVar", m: "int64_t") -> "void":
return _pywrapcp.Assignment_SetMax(self, var, m)
def SetRange(self, var: "IntVar", l: "int64_t", u: "int64_t") -> "void":
return _pywrapcp.Assignment_SetRange(self, var, l, u)
def SetValue(self, var: "IntVar", value: "int64_t") -> "void":
return _pywrapcp.Assignment_SetValue(self, var, value)
def StartMin(self, var: "IntervalVar") -> "int64_t":
return _pywrapcp.Assignment_StartMin(self, var)
def StartMax(self, var: "IntervalVar") -> "int64_t":
return _pywrapcp.Assignment_StartMax(self, var)
def StartValue(self, var: "IntervalVar") -> "int64_t":
return _pywrapcp.Assignment_StartValue(self, var)
def DurationMin(self, var: "IntervalVar") -> "int64_t":
return _pywrapcp.Assignment_DurationMin(self, var)
def DurationMax(self, var: "IntervalVar") -> "int64_t":
return _pywrapcp.Assignment_DurationMax(self, var)
def DurationValue(self, var: "IntervalVar") -> "int64_t":
return _pywrapcp.Assignment_DurationValue(self, var)
def EndMin(self, var: "IntervalVar") -> "int64_t":
return _pywrapcp.Assignment_EndMin(self, var)
def EndMax(self, var: "IntervalVar") -> "int64_t":
return _pywrapcp.Assignment_EndMax(self, var)
def EndValue(self, var: "IntervalVar") -> "int64_t":
return _pywrapcp.Assignment_EndValue(self, var)
def PerformedMin(self, var: "IntervalVar") -> "int64_t":
return _pywrapcp.Assignment_PerformedMin(self, var)
def PerformedMax(self, var: "IntervalVar") -> "int64_t":
return _pywrapcp.Assignment_PerformedMax(self, var)
def PerformedValue(self, var: "IntervalVar") -> "int64_t":
return _pywrapcp.Assignment_PerformedValue(self, var)
def SetStartMin(self, var: "IntervalVar", m: "int64_t") -> "void":
return _pywrapcp.Assignment_SetStartMin(self, var, m)
def SetStartMax(self, var: "IntervalVar", m: "int64_t") -> "void":
return _pywrapcp.Assignment_SetStartMax(self, var, m)
def SetStartRange(self, var: "IntervalVar", mi: "int64_t", ma: "int64_t") -> "void":
return _pywrapcp.Assignment_SetStartRange(self, var, mi, ma)
def SetStartValue(self, var: "IntervalVar", value: "int64_t") -> "void":
return _pywrapcp.Assignment_SetStartValue(self, var, value)
def SetDurationMin(self, var: "IntervalVar", m: "int64_t") -> "void":
return _pywrapcp.Assignment_SetDurationMin(self, var, m)
def SetDurationMax(self, var: "IntervalVar", m: "int64_t") -> "void":
return _pywrapcp.Assignment_SetDurationMax(self, var, m)
def SetDurationRange(self, var: "IntervalVar", mi: "int64_t", ma: "int64_t") -> "void":
return _pywrapcp.Assignment_SetDurationRange(self, var, mi, ma)
def SetDurationValue(self, var: "IntervalVar", value: "int64_t") -> "void":
return _pywrapcp.Assignment_SetDurationValue(self, var, value)
def SetEndMin(self, var: "IntervalVar", m: "int64_t") -> "void":
return _pywrapcp.Assignment_SetEndMin(self, var, m)
def SetEndMax(self, var: "IntervalVar", m: "int64_t") -> "void":
return _pywrapcp.Assignment_SetEndMax(self, var, m)
def SetEndRange(self, var: "IntervalVar", mi: "int64_t", ma: "int64_t") -> "void":
return _pywrapcp.Assignment_SetEndRange(self, var, mi, ma)
def SetEndValue(self, var: "IntervalVar", value: "int64_t") -> "void":
return _pywrapcp.Assignment_SetEndValue(self, var, value)
def SetPerformedMin(self, var: "IntervalVar", m: "int64_t") -> "void":
return _pywrapcp.Assignment_SetPerformedMin(self, var, m)
def SetPerformedMax(self, var: "IntervalVar", m: "int64_t") -> "void":
return _pywrapcp.Assignment_SetPerformedMax(self, var, m)
def SetPerformedRange(self, var: "IntervalVar", mi: "int64_t", ma: "int64_t") -> "void":
return _pywrapcp.Assignment_SetPerformedRange(self, var, mi, ma)
def SetPerformedValue(self, var: "IntervalVar", value: "int64_t") -> "void":
return _pywrapcp.Assignment_SetPerformedValue(self, var, value)
def Add(self, *args) -> "void":
return _pywrapcp.Assignment_Add(self, *args)
def ForwardSequence(self, var: "SequenceVar") -> "std::vector< int > const &":
return _pywrapcp.Assignment_ForwardSequence(self, var)
def BackwardSequence(self, var: "SequenceVar") -> "std::vector< int > const &":
return _pywrapcp.Assignment_BackwardSequence(self, var)
def Unperformed(self, var: "SequenceVar") -> "std::vector< int > const &":
return _pywrapcp.Assignment_Unperformed(self, var)
def SetSequence(self, var: "SequenceVar", forward_sequence: "std::vector< int > const &", backward_sequence: "std::vector< int > const &", unperformed: "std::vector< int > const &") -> "void":
return _pywrapcp.Assignment_SetSequence(self, var, forward_sequence, backward_sequence, unperformed)
def SetForwardSequence(self, var: "SequenceVar", forward_sequence: "std::vector< int > const &") -> "void":
return _pywrapcp.Assignment_SetForwardSequence(self, var, forward_sequence)
def SetBackwardSequence(self, var: "SequenceVar", backward_sequence: "std::vector< int > const &") -> "void":
return _pywrapcp.Assignment_SetBackwardSequence(self, var, backward_sequence)
def SetUnperformed(self, var: "SequenceVar", unperformed: "std::vector< int > const &") -> "void":
return _pywrapcp.Assignment_SetUnperformed(self, var, unperformed)
def Activate(self, *args) -> "void":
return _pywrapcp.Assignment_Activate(self, *args)
def Deactivate(self, *args) -> "void":
return _pywrapcp.Assignment_Deactivate(self, *args)
def Activated(self, *args) -> "bool":
return _pywrapcp.Assignment_Activated(self, *args)
def DebugString(self) -> "std::string":
return _pywrapcp.Assignment_DebugString(self)
def IntVarContainer(self) -> "operations_research::Assignment::IntContainer const &":
return _pywrapcp.Assignment_IntVarContainer(self)
def MutableIntVarContainer(self) -> "operations_research::Assignment::IntContainer *":
return _pywrapcp.Assignment_MutableIntVarContainer(self)
def IntervalVarContainer(self) -> "operations_research::Assignment::IntervalContainer const &":
return _pywrapcp.Assignment_IntervalVarContainer(self)
def MutableIntervalVarContainer(self) -> "operations_research::Assignment::IntervalContainer *":
return _pywrapcp.Assignment_MutableIntervalVarContainer(self)
def SequenceVarContainer(self) -> "operations_research::Assignment::SequenceContainer const &":
return _pywrapcp.Assignment_SequenceVarContainer(self)
def MutableSequenceVarContainer(self) -> "operations_research::Assignment::SequenceContainer *":
return _pywrapcp.Assignment_MutableSequenceVarContainer(self)
def __eq__(self, assignment: "Assignment") -> "bool":
return _pywrapcp.Assignment___eq__(self, assignment)
def __ne__(self, assignment: "Assignment") -> "bool":
return _pywrapcp.Assignment___ne__(self, assignment)
# Register Assignment in _pywrapcp:
_pywrapcp.Assignment_swigregister(Assignment)
def __lshift__(*args) -> "std::ostream &":
return _pywrapcp.__lshift__(*args)
class Pack(Constraint):
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
def __init__(self, *args, **kwargs):
raise AttributeError("No constructor defined")
__repr__ = _swig_repr
def AddWeightedSumLessOrEqualConstantDimension(self, *args) -> "void":
r"""
*Overload 1:*
Dimensions are additional constraints than can restrict what is
possible with the pack constraint. It can be used to set capacity
limits, to count objects per bin, to compute unassigned
penalties...
This dimension imposes that for all bins b, the weighted sum
(weights[i]) of all objects i assigned to 'b' is less or equal
'bounds[b]'.
|
*Overload 2:*
This dimension imposes that for all bins b, the weighted sum
(weights->Run(i)) of all objects i assigned to 'b' is less or
equal to 'bounds[b]'. Ownership of the callback is transferred to
the pack constraint.
|
*Overload 3:*
This dimension imposes that for all bins b, the weighted sum
(weights->Run(i, b) of all objects i assigned to 'b' is less or
equal to 'bounds[b]'. Ownership of the callback is transferred to
the pack constraint.
"""
return _pywrapcp.Pack_AddWeightedSumLessOrEqualConstantDimension(self, *args)
def AddWeightedSumEqualVarDimension(self, *args) -> "void":
r"""
*Overload 1:*
This dimension imposes that for all bins b, the weighted sum
(weights[i]) of all objects i assigned to 'b' is equal to loads[b].
|
*Overload 2:*
This dimension imposes that for all bins b, the weighted sum
(weights->Run(i, b)) of all objects i assigned to 'b' is equal to
loads[b].
"""
return _pywrapcp.Pack_AddWeightedSumEqualVarDimension(self, *args)
def AddSumVariableWeightsLessOrEqualConstantDimension(self, usage: "std::vector< operations_research::IntVar * > const &", capacity: "std::vector< int64_t > const &") -> "void":
r"""
This dimension imposes:
forall b in bins,
sum (i in items: usage[i] * is_assigned(i, b)) <= capacity[b]
where is_assigned(i, b) is true if and only if item i is assigned
to the bin b.
This can be used to model shapes of items by linking variables of
the same item on parallel dimensions with an allowed assignment
constraint.
"""
return _pywrapcp.Pack_AddSumVariableWeightsLessOrEqualConstantDimension(self, usage, capacity)
def AddWeightedSumOfAssignedDimension(self, weights: "std::vector< int64_t > const &", cost_var: "IntVar") -> "void":
r"""
This dimension enforces that cost_var == sum of weights[i] for
all objects 'i' assigned to a bin.
"""
return _pywrapcp.Pack_AddWeightedSumOfAssignedDimension(self, weights, cost_var)
def AddCountUsedBinDimension(self, count_var: "IntVar") -> "void":
r"""
This dimension links 'count_var' to the actual number of bins used in the
pack.
"""
return _pywrapcp.Pack_AddCountUsedBinDimension(self, count_var)
def AddCountAssignedItemsDimension(self, count_var: "IntVar") -> "void":
r"""
This dimension links 'count_var' to the actual number of items
assigned to a bin in the pack.
"""
return _pywrapcp.Pack_AddCountAssignedItemsDimension(self, count_var)
def Post(self) -> "void":
return _pywrapcp.Pack_Post(self)
def InitialPropagateWrapper(self) -> "void":
return _pywrapcp.Pack_InitialPropagateWrapper(self)
def DebugString(self) -> "std::string":
return _pywrapcp.Pack_DebugString(self)
# Register Pack in _pywrapcp:
_pywrapcp.Pack_swigregister(Pack)
class DisjunctiveConstraint(Constraint):
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
def __init__(self, *args, **kwargs):
raise AttributeError("No constructor defined - class is abstract")
__repr__ = _swig_repr
def SequenceVar(self) -> "operations_research::SequenceVar *":
r""" Creates a sequence variable from the constraint."""
return _pywrapcp.DisjunctiveConstraint_SequenceVar(self)
def SetTransitionTime(self, transition_time: "operations_research::Solver::IndexEvaluator2") -> "void":
r"""
Add a transition time between intervals. It forces the distance between
the end of interval a and start of interval b that follows it to be at
least transition_time(a, b). This function must always return
a positive or null value.
"""
return _pywrapcp.DisjunctiveConstraint_SetTransitionTime(self, transition_time)
def TransitionTime(self, before_index: "int", after_index: "int") -> "int64_t":
return _pywrapcp.DisjunctiveConstraint_TransitionTime(self, before_index, after_index)
# Register DisjunctiveConstraint in _pywrapcp:
_pywrapcp.DisjunctiveConstraint_swigregister(DisjunctiveConstraint)
class RevInteger(object):
r"""
This class adds reversibility to a POD type.
It contains the stamp optimization. i.e. the SaveValue call is done
only once per node of the search tree. Please note that actual
stamps always starts at 1, thus an initial value of 0 will always
trigger the first SaveValue.
"""
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
__repr__ = _swig_repr
def __init__(self, val: "long const &"):
_pywrapcp.RevInteger_swiginit(self, _pywrapcp.new_RevInteger(val))
def Value(self) -> "long const &":
return _pywrapcp.RevInteger_Value(self)
def SetValue(self, s: "Solver", val: "long const &") -> "void":
return _pywrapcp.RevInteger_SetValue(self, s, val)
__swig_destroy__ = _pywrapcp.delete_RevInteger
# Register RevInteger in _pywrapcp:
_pywrapcp.RevInteger_swigregister(RevInteger)
class NumericalRevInteger(RevInteger):
r""" Subclass of Rev<T> which adds numerical operations."""
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
__repr__ = _swig_repr
def __init__(self, val: "long const &"):
_pywrapcp.NumericalRevInteger_swiginit(self, _pywrapcp.new_NumericalRevInteger(val))
def Add(self, s: "Solver", to_add: "long const &") -> "void":
return _pywrapcp.NumericalRevInteger_Add(self, s, to_add)
def Incr(self, s: "Solver") -> "void":
return _pywrapcp.NumericalRevInteger_Incr(self, s)
def Decr(self, s: "Solver") -> "void":
return _pywrapcp.NumericalRevInteger_Decr(self, s)
__swig_destroy__ = _pywrapcp.delete_NumericalRevInteger
# Register NumericalRevInteger in _pywrapcp:
_pywrapcp.NumericalRevInteger_swigregister(NumericalRevInteger)
class RevBool(object):
r"""
This class adds reversibility to a POD type.
It contains the stamp optimization. i.e. the SaveValue call is done
only once per node of the search tree. Please note that actual
stamps always starts at 1, thus an initial value of 0 will always
trigger the first SaveValue.
"""
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
__repr__ = _swig_repr
def __init__(self, val: "bool const &"):
_pywrapcp.RevBool_swiginit(self, _pywrapcp.new_RevBool(val))
def Value(self) -> "bool const &":
return _pywrapcp.RevBool_Value(self)
def SetValue(self, s: "Solver", val: "bool const &") -> "void":
return _pywrapcp.RevBool_SetValue(self, s, val)
__swig_destroy__ = _pywrapcp.delete_RevBool
# Register RevBool in _pywrapcp:
_pywrapcp.RevBool_swigregister(RevBool)
class IntVarContainer(object):
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
def __init__(self, *args, **kwargs):
raise AttributeError("No constructor defined")
__repr__ = _swig_repr
def Contains(self, var: "IntVar") -> "bool":
return _pywrapcp.IntVarContainer_Contains(self, var)
def Element(self, index: "int") -> "operations_research::IntVarElement *":
return _pywrapcp.IntVarContainer_Element(self, index)
def Size(self) -> "int":
return _pywrapcp.IntVarContainer_Size(self)
def Store(self) -> "void":
return _pywrapcp.IntVarContainer_Store(self)
def Restore(self) -> "void":
return _pywrapcp.IntVarContainer_Restore(self)
def __eq__(self, container: "IntVarContainer") -> "bool":
r"""
Returns true if this and 'container' both represent the same V* -> E map.
Runs in linear time; requires that the == operator on the type E is well
defined.
"""
return _pywrapcp.IntVarContainer___eq__(self, container)
def __ne__(self, container: "IntVarContainer") -> "bool":
return _pywrapcp.IntVarContainer___ne__(self, container)
__swig_destroy__ = _pywrapcp.delete_IntVarContainer
# Register IntVarContainer in _pywrapcp:
_pywrapcp.IntVarContainer_swigregister(IntVarContainer)
class IntervalVarContainer(object):
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
def __init__(self, *args, **kwargs):
raise AttributeError("No constructor defined")
__repr__ = _swig_repr
def Contains(self, var: "IntervalVar") -> "bool":
return _pywrapcp.IntervalVarContainer_Contains(self, var)
def Element(self, index: "int") -> "operations_research::IntervalVarElement *":
return _pywrapcp.IntervalVarContainer_Element(self, index)
def Size(self) -> "int":
return _pywrapcp.IntervalVarContainer_Size(self)
def Store(self) -> "void":
return _pywrapcp.IntervalVarContainer_Store(self)
def Restore(self) -> "void":
return _pywrapcp.IntervalVarContainer_Restore(self)
def __eq__(self, container: "IntervalVarContainer") -> "bool":
r"""
Returns true if this and 'container' both represent the same V* -> E map.
Runs in linear time; requires that the == operator on the type E is well
defined.
"""
return _pywrapcp.IntervalVarContainer___eq__(self, container)
def __ne__(self, container: "IntervalVarContainer") -> "bool":
return _pywrapcp.IntervalVarContainer___ne__(self, container)
__swig_destroy__ = _pywrapcp.delete_IntervalVarContainer
# Register IntervalVarContainer in _pywrapcp:
_pywrapcp.IntervalVarContainer_swigregister(IntervalVarContainer)
class SequenceVarContainer(object):
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
def __init__(self, *args, **kwargs):
raise AttributeError("No constructor defined")
__repr__ = _swig_repr
def Contains(self, var: "SequenceVar") -> "bool":
return _pywrapcp.SequenceVarContainer_Contains(self, var)
def Element(self, index: "int") -> "operations_research::SequenceVarElement *":
return _pywrapcp.SequenceVarContainer_Element(self, index)
def Size(self) -> "int":
return _pywrapcp.SequenceVarContainer_Size(self)
def Store(self) -> "void":
return _pywrapcp.SequenceVarContainer_Store(self)
def Restore(self) -> "void":
return _pywrapcp.SequenceVarContainer_Restore(self)
def __eq__(self, container: "SequenceVarContainer") -> "bool":
r"""
Returns true if this and 'container' both represent the same V* -> E map.
Runs in linear time; requires that the == operator on the type E is well
defined.
"""
return _pywrapcp.SequenceVarContainer___eq__(self, container)
def __ne__(self, container: "SequenceVarContainer") -> "bool":
return _pywrapcp.SequenceVarContainer___ne__(self, container)
__swig_destroy__ = _pywrapcp.delete_SequenceVarContainer
# Register SequenceVarContainer in _pywrapcp:
_pywrapcp.SequenceVarContainer_swigregister(SequenceVarContainer)
class LocalSearchOperator(BaseObject):
r"""
This class represent a reversible FIFO structure.
The main difference w.r.t a standard FIFO structure is that a Solver is
given as parameter to the modifiers such that the solver can store the
backtrack information
Iterator's traversing order should not be changed, as some algorithm
depend on it to be consistent.
It's main use is to store a list of demons in the various classes of
variables.
The base class for all local search operators.
A local search operator is an object that defines the neighborhood of a
solution. In other words, a neighborhood is the set of solutions which can
be reached from a given solution using an operator.
The behavior of the LocalSearchOperator class is similar to iterators.
The operator is synchronized with an assignment (gives the
current values of the variables); this is done in the Start() method.
Then one can iterate over the neighbors using the MakeNextNeighbor method.
This method returns an assignment which represents the incremental changes
to the current solution. It also returns a second assignment representing
the changes to the last solution defined by the neighborhood operator; this
assignment is empty if the neighborhood operator cannot track this
information.
"""
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
def __init__(self, *args, **kwargs):
raise AttributeError("No constructor defined - class is abstract")
__repr__ = _swig_repr
def NextNeighbor(self, delta: "Assignment", deltadelta: "Assignment") -> "bool":
return _pywrapcp.LocalSearchOperator_NextNeighbor(self, delta, deltadelta)
def Start(self, assignment: "Assignment") -> "void":
return _pywrapcp.LocalSearchOperator_Start(self, assignment)
def __disown__(self):
self.this.disown()
_pywrapcp.disown_LocalSearchOperator(self)
return weakref.proxy(self)
# Register LocalSearchOperator in _pywrapcp:
_pywrapcp.LocalSearchOperator_swigregister(LocalSearchOperator)
class IntVarLocalSearchOperatorTemplate(LocalSearchOperator):
r""" Base operator class for operators manipulating variables."""
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
def __init__(self, *args, **kwargs):
raise AttributeError("No constructor defined - class is abstract")
__repr__ = _swig_repr
def Start(self, assignment: "Assignment") -> "void":
r"""
This method should not be overridden. Override OnStart() instead which is
called before exiting this method.
"""
return _pywrapcp.IntVarLocalSearchOperatorTemplate_Start(self, assignment)
def IsIncremental(self) -> "bool":
return _pywrapcp.IntVarLocalSearchOperatorTemplate_IsIncremental(self)
def Size(self) -> "int":
return _pywrapcp.IntVarLocalSearchOperatorTemplate_Size(self)
def Value(self, index: "int64_t") -> "long const &":
r"""
Returns the value in the current assignment of the variable of given
index.
"""
return _pywrapcp.IntVarLocalSearchOperatorTemplate_Value(self, index)
def OldValue(self, index: "int64_t") -> "long const &":
return _pywrapcp.IntVarLocalSearchOperatorTemplate_OldValue(self, index)
def SetValue(self, index: "int64_t", value: "long const &") -> "void":
return _pywrapcp.IntVarLocalSearchOperatorTemplate_SetValue(self, index, value)
def OnStart(self) -> "void":
r"""
Called by Start() after synchronizing the operator with the current
assignment. Should be overridden instead of Start() to avoid calling
VarLocalSearchOperator::Start explicitly.
"""
return _pywrapcp.IntVarLocalSearchOperatorTemplate_OnStart(self)
# Register IntVarLocalSearchOperatorTemplate in _pywrapcp:
_pywrapcp.IntVarLocalSearchOperatorTemplate_swigregister(IntVarLocalSearchOperatorTemplate)
class IntVarLocalSearchOperator(IntVarLocalSearchOperatorTemplate):
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
__repr__ = _swig_repr
def __init__(self, *args):
if self.__class__ == IntVarLocalSearchOperator:
_self = None
else:
_self = self
_pywrapcp.IntVarLocalSearchOperator_swiginit(self, _pywrapcp.new_IntVarLocalSearchOperator(_self, *args))
__swig_destroy__ = _pywrapcp.delete_IntVarLocalSearchOperator
def NextNeighbor(self, delta: "Assignment", deltadelta: "Assignment") -> "bool":
r"""
Redefines MakeNextNeighbor to export a simpler interface. The calls to
ApplyChanges() and RevertChanges() are factored in this method, hiding
both delta and deltadelta from subclasses which only need to override
MakeOneNeighbor().
Therefore this method should not be overridden. Override MakeOneNeighbor()
instead.
"""
return _pywrapcp.IntVarLocalSearchOperator_NextNeighbor(self, delta, deltadelta)
def OneNeighbor(self) -> "bool":
r"""
Creates a new neighbor. It returns false when the neighborhood is
completely explored.
MakeNextNeighbor() in a subclass of IntVarLocalSearchOperator.
"""
return _pywrapcp.IntVarLocalSearchOperator_OneNeighbor(self)
def __disown__(self):
self.this.disown()
_pywrapcp.disown_IntVarLocalSearchOperator(self)
return weakref.proxy(self)
# Register IntVarLocalSearchOperator in _pywrapcp:
_pywrapcp.IntVarLocalSearchOperator_swigregister(IntVarLocalSearchOperator)
class SequenceVarLocalSearchOperatorTemplate(LocalSearchOperator):
r""" Base operator class for operators manipulating variables."""
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
def __init__(self, *args, **kwargs):
raise AttributeError("No constructor defined - class is abstract")
__repr__ = _swig_repr
def Start(self, assignment: "Assignment") -> "void":
r"""
This method should not be overridden. Override OnStart() instead which is
called before exiting this method.
"""
return _pywrapcp.SequenceVarLocalSearchOperatorTemplate_Start(self, assignment)
def IsIncremental(self) -> "bool":
return _pywrapcp.SequenceVarLocalSearchOperatorTemplate_IsIncremental(self)
def Size(self) -> "int":
return _pywrapcp.SequenceVarLocalSearchOperatorTemplate_Size(self)
def Value(self, index: "int64_t") -> "std::vector< int > const &":
r"""
Returns the value in the current assignment of the variable of given
index.
"""
return _pywrapcp.SequenceVarLocalSearchOperatorTemplate_Value(self, index)
def OldValue(self, index: "int64_t") -> "std::vector< int > const &":
return _pywrapcp.SequenceVarLocalSearchOperatorTemplate_OldValue(self, index)
def SetValue(self, index: "int64_t", value: "std::vector< int > const &") -> "void":
return _pywrapcp.SequenceVarLocalSearchOperatorTemplate_SetValue(self, index, value)
def OnStart(self) -> "void":
r"""
Called by Start() after synchronizing the operator with the current
assignment. Should be overridden instead of Start() to avoid calling
VarLocalSearchOperator::Start explicitly.
"""
return _pywrapcp.SequenceVarLocalSearchOperatorTemplate_OnStart(self)
# Register SequenceVarLocalSearchOperatorTemplate in _pywrapcp:
_pywrapcp.SequenceVarLocalSearchOperatorTemplate_swigregister(SequenceVarLocalSearchOperatorTemplate)
class SequenceVarLocalSearchOperator(SequenceVarLocalSearchOperatorTemplate):
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
def __init__(self, *args, **kwargs):
raise AttributeError("No constructor defined - class is abstract")
__repr__ = _swig_repr
# Register SequenceVarLocalSearchOperator in _pywrapcp:
_pywrapcp.SequenceVarLocalSearchOperator_swigregister(SequenceVarLocalSearchOperator)
class BaseLns(IntVarLocalSearchOperator):
r"""
This is the base class for building an Lns operator. An Lns fragment is a
collection of variables which will be relaxed. Fragments are built with
NextFragment(), which returns false if there are no more fragments to build.
Optionally one can override InitFragments, which is called from
LocalSearchOperator::Start to initialize fragment data.
Here's a sample relaxing one variable at a time:
class OneVarLns : public BaseLns {
public:
OneVarLns(const std::vector<IntVar*>& vars) : BaseLns(vars), index_(0) {}
virtual ~OneVarLns() {}
virtual void InitFragments() { index_ = 0; }
virtual bool NextFragment() {
const int size = Size();
if (index_ < size) {
AppendToFragment(index_);
++index_;
return true;
} else {
return false;
}
}
private:
int index_;
};
"""
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
__repr__ = _swig_repr
def __init__(self, vars: "std::vector< operations_research::IntVar * > const &"):
if self.__class__ == BaseLns:
_self = None
else:
_self = self
_pywrapcp.BaseLns_swiginit(self, _pywrapcp.new_BaseLns(_self, vars))
__swig_destroy__ = _pywrapcp.delete_BaseLns
def InitFragments(self) -> "void":
return _pywrapcp.BaseLns_InitFragments(self)
def NextFragment(self) -> "bool":
return _pywrapcp.BaseLns_NextFragment(self)
def AppendToFragment(self, index: "int") -> "void":
return _pywrapcp.BaseLns_AppendToFragment(self, index)
def FragmentSize(self) -> "int":
return _pywrapcp.BaseLns_FragmentSize(self)
def __getitem__(self, index: "int") -> "int64_t":
return _pywrapcp.BaseLns___getitem__(self, index)
def __len__(self) -> "int":
return _pywrapcp.BaseLns___len__(self)
def __disown__(self):
self.this.disown()
_pywrapcp.disown_BaseLns(self)
return weakref.proxy(self)
# Register BaseLns in _pywrapcp:
_pywrapcp.BaseLns_swigregister(BaseLns)
class ChangeValue(IntVarLocalSearchOperator):
r"""
Defines operators which change the value of variables;
each neighbor corresponds to *one* modified variable.
Sub-classes have to define ModifyValue which determines what the new
variable value is going to be (given the current value and the variable).
"""
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
__repr__ = _swig_repr
def __init__(self, vars: "std::vector< operations_research::IntVar * > const &"):
if self.__class__ == ChangeValue:
_self = None
else:
_self = self
_pywrapcp.ChangeValue_swiginit(self, _pywrapcp.new_ChangeValue(_self, vars))
__swig_destroy__ = _pywrapcp.delete_ChangeValue
def ModifyValue(self, index: "int64_t", value: "int64_t") -> "int64_t":
return _pywrapcp.ChangeValue_ModifyValue(self, index, value)
def OneNeighbor(self) -> "bool":
r""" This method should not be overridden. Override ModifyValue() instead."""
return _pywrapcp.ChangeValue_OneNeighbor(self)
def __disown__(self):
self.this.disown()
_pywrapcp.disown_ChangeValue(self)
return weakref.proxy(self)
# Register ChangeValue in _pywrapcp:
_pywrapcp.ChangeValue_swigregister(ChangeValue)
class PathOperator(IntVarLocalSearchOperator):
r"""
Base class of the local search operators dedicated to path modifications
(a path is a set of nodes linked together by arcs).
This family of neighborhoods supposes they are handling next variables
representing the arcs (var[i] represents the node immediately after i on
a path).
Several services are provided:
- arc manipulators (SetNext(), ReverseChain(), MoveChain())
- path inspectors (Next(), Prev(), IsPathEnd())
- path iterators: operators need a given number of nodes to define a
neighbor; this class provides the iteration on a given number of (base)
nodes which can be used to define a neighbor (through the BaseNode method)
Subclasses only need to override MakeNeighbor to create neighbors using
the services above (no direct manipulation of assignments).
"""
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
def __init__(self, *args, **kwargs):
raise AttributeError("No constructor defined - class is abstract")
__repr__ = _swig_repr
def Neighbor(self) -> "bool":
return _pywrapcp.PathOperator_Neighbor(self)
# Register PathOperator in _pywrapcp:
_pywrapcp.PathOperator_swigregister(PathOperator)
class LocalSearchFilter(BaseObject):
r"""
Classes to which this template function can be applied to as of 04/2014.
Usage: LocalSearchOperator* op = MakeLocalSearchOperator<Relocate>(...);
class TwoOpt;
class Relocate;
class Exchange;
class Cross;
class MakeActiveOperator;
class MakeInactiveOperator;
class MakeChainInactiveOperator;
class SwapActiveOperator;
class ExtendedSwapActiveOperator;
class MakeActiveAndRelocate;
class RelocateAndMakeActiveOperator;
class RelocateAndMakeInactiveOperator;
Local Search Filters are used for fast neighbor pruning.
Filtering a move is done in several phases:
- in the Relax phase, filters determine which parts of their internals
will be changed by the candidate, and modify intermediary State
- in the Accept phase, filters check that the candidate is feasible,
- if the Accept phase succeeds, the solver may decide to trigger a
Synchronize phase that makes filters change their internal representation
to the last candidate,
- otherwise (Accept fails or the solver does not want to synchronize),
a Revert phase makes filters erase any intermediary State generated by the
Relax and Accept phases.
A given filter has phases called with the following pattern:
(Relax.Accept.Synchronize | Relax.Accept.Revert | Relax.Revert)*.
Filters's Revert() is always called in the reverse order their Accept() was
called, to allow late filters to use state done/undone by early filters'
Accept()/Revert().
"""
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
def __init__(self, *args, **kwargs):
raise AttributeError("No constructor defined - class is abstract")
__repr__ = _swig_repr
def Accept(self, delta: "Assignment", deltadelta: "Assignment", objective_min: "int64_t", objective_max: "int64_t") -> "bool":
r"""
Accepts a "delta" given the assignment with which the filter has been
synchronized; the delta holds the variables which have been modified and
their new value.
If the filter represents a part of the global objective, its contribution
must be between objective_min and objective_max.
Sample: supposing one wants to maintain a[0,1] + b[0,1] <= 1,
for the assignment (a,1), (b,0), the delta (b,1) will be rejected
but the delta (a,0) will be accepted.
TODO(user): Remove arguments when there are no more need for those.
"""
return _pywrapcp.LocalSearchFilter_Accept(self, delta, deltadelta, objective_min, objective_max)
def IsIncremental(self) -> "bool":
return _pywrapcp.LocalSearchFilter_IsIncremental(self)
def Synchronize(self, assignment: "Assignment", delta: "Assignment") -> "void":
r"""
Synchronizes the filter with the current solution, delta being the
difference with the solution passed to the previous call to Synchronize()
or IncrementalSynchronize(). 'delta' can be used to incrementally
synchronizing the filter with the new solution by only considering the
changes in delta.
"""
return _pywrapcp.LocalSearchFilter_Synchronize(self, assignment, delta)
__swig_destroy__ = _pywrapcp.delete_LocalSearchFilter
# Register LocalSearchFilter in _pywrapcp:
_pywrapcp.LocalSearchFilter_swigregister(LocalSearchFilter)
class LocalSearchFilterManager(BaseObject):
r"""
Filter manager: when a move is made, filters are executed to decide whether
the solution is feasible and compute parts of the new cost. This class
schedules filter execution and composes costs as a sum.
"""
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
__repr__ = _swig_repr
def DebugString(self) -> "std::string":
return _pywrapcp.LocalSearchFilterManager_DebugString(self)
def __init__(self, *args):
_pywrapcp.LocalSearchFilterManager_swiginit(self, _pywrapcp.new_LocalSearchFilterManager(*args))
def Accept(self, monitor: "operations_research::LocalSearchMonitor *const", delta: "Assignment", deltadelta: "Assignment", objective_min: "int64_t", objective_max: "int64_t") -> "bool":
r"""
Returns true iff all filters return true, and the sum of their accepted
objectives is between objective_min and objective_max.
The monitor has its Begin/EndFiltering events triggered.
"""
return _pywrapcp.LocalSearchFilterManager_Accept(self, monitor, delta, deltadelta, objective_min, objective_max)
def Synchronize(self, assignment: "Assignment", delta: "Assignment") -> "void":
r""" Synchronizes all filters to assignment."""
return _pywrapcp.LocalSearchFilterManager_Synchronize(self, assignment, delta)
__swig_destroy__ = _pywrapcp.delete_LocalSearchFilterManager
# Register LocalSearchFilterManager in _pywrapcp:
_pywrapcp.LocalSearchFilterManager_swigregister(LocalSearchFilterManager)
class IntVarLocalSearchFilter(LocalSearchFilter):
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
__repr__ = _swig_repr
def __init__(self, vars: "std::vector< operations_research::IntVar * > const &"):
if self.__class__ == IntVarLocalSearchFilter:
_self = None
else:
_self = self
_pywrapcp.IntVarLocalSearchFilter_swiginit(self, _pywrapcp.new_IntVarLocalSearchFilter(_self, vars))
__swig_destroy__ = _pywrapcp.delete_IntVarLocalSearchFilter
def Synchronize(self, assignment: "Assignment", delta: "Assignment") -> "void":
r"""
This method should not be overridden. Override OnSynchronize() instead
which is called before exiting this method.
"""
return _pywrapcp.IntVarLocalSearchFilter_Synchronize(self, assignment, delta)
def Size(self) -> "int":
return _pywrapcp.IntVarLocalSearchFilter_Size(self)
def Value(self, index: "int") -> "int64_t":
return _pywrapcp.IntVarLocalSearchFilter_Value(self, index)
def IndexFromVar(self, var: "IntVar") -> "int64_t":
return _pywrapcp.IntVarLocalSearchFilter_IndexFromVar(self, var)
def __disown__(self):
self.this.disown()
_pywrapcp.disown_IntVarLocalSearchFilter(self)
return weakref.proxy(self)
# Register IntVarLocalSearchFilter in _pywrapcp:
_pywrapcp.IntVarLocalSearchFilter_swigregister(IntVarLocalSearchFilter)
class BooleanVar(IntVar):
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
def __init__(self, *args, **kwargs):
raise AttributeError("No constructor defined - class is abstract")
__repr__ = _swig_repr
def Min(self) -> "int64_t":
return _pywrapcp.BooleanVar_Min(self)
def SetMin(self, m: "int64_t") -> "void":
return _pywrapcp.BooleanVar_SetMin(self, m)
def Max(self) -> "int64_t":
return _pywrapcp.BooleanVar_Max(self)
def SetMax(self, m: "int64_t") -> "void":
return _pywrapcp.BooleanVar_SetMax(self, m)
def SetRange(self, mi: "int64_t", ma: "int64_t") -> "void":
return _pywrapcp.BooleanVar_SetRange(self, mi, ma)
def Bound(self) -> "bool":
return _pywrapcp.BooleanVar_Bound(self)
def Value(self) -> "int64_t":
return _pywrapcp.BooleanVar_Value(self)
def RemoveValue(self, v: "int64_t") -> "void":
return _pywrapcp.BooleanVar_RemoveValue(self, v)
def RemoveInterval(self, l: "int64_t", u: "int64_t") -> "void":
return _pywrapcp.BooleanVar_RemoveInterval(self, l, u)
def WhenBound(self, d: "Demon") -> "void":
return _pywrapcp.BooleanVar_WhenBound(self, d)
def WhenRange(self, d: "Demon") -> "void":
return _pywrapcp.BooleanVar_WhenRange(self, d)
def WhenDomain(self, d: "Demon") -> "void":
return _pywrapcp.BooleanVar_WhenDomain(self, d)
def Size(self) -> "uint64_t":
return _pywrapcp.BooleanVar_Size(self)
def Contains(self, v: "int64_t") -> "bool":
return _pywrapcp.BooleanVar_Contains(self, v)
def HoleIteratorAux(self, reversible: "bool") -> "operations_research::IntVarIterator *":
return _pywrapcp.BooleanVar_HoleIteratorAux(self, reversible)
def DomainIteratorAux(self, reversible: "bool") -> "operations_research::IntVarIterator *":
return _pywrapcp.BooleanVar_DomainIteratorAux(self, reversible)
def DebugString(self) -> "std::string":
return _pywrapcp.BooleanVar_DebugString(self)
# Register BooleanVar in _pywrapcp:
_pywrapcp.BooleanVar_swigregister(BooleanVar)
class PyDecision(Decision):
def __init__(self):
Decision.__init__(self)
def ApplyWrapper(self, solver):
try:
self.Apply(solver)
except Exception as e:
if 'CP Solver fail' in str(e):
solver.ShouldFail()
else:
raise
def RefuteWrapper(self, solver):
try:
self.Refute(solver)
except Exception as e:
if 'CP Solver fail' in str(e):
solver.ShouldFail()
else:
raise
def DebugString(self):
return "PyDecision"
class PyDecisionBuilder(DecisionBuilder):
def __init__(self):
DecisionBuilder.__init__(self)
def NextWrapper(self, solver):
try:
return self.Next(solver)
except Exception as e:
if 'CP Solver fail' in str(e):
return solver.FailDecision()
else:
raise
def DebugString(self):
return "PyDecisionBuilder"
class PyDemon(Demon):
def RunWrapper(self, solver):
try:
self.Run(solver)
except Exception as e:
if 'CP Solver fail' in str(e):
solver.ShouldFail()
else:
raise
def DebugString(self):
return "PyDemon"
class PyConstraintDemon(PyDemon):
def __init__(self, ct, method, delayed, *args):
PyDemon.__init__(self)
self.__constraint = ct
self.__method = method
self.__delayed = delayed
self.__args = args
def Run(self, solver):
self.__method(self.__constraint, *self.__args)
def Priority(self):
return Solver.DELAYED_PRIORITY if self.__delayed else Solver.NORMAL_PRIORITY
def DebugString(self):
return 'PyConstraintDemon'
class PyConstraint(Constraint):
def __init__(self, solver):
Constraint.__init__(self, solver)
self.__demons = []
def Demon(self, method, *args):
demon = PyConstraintDemon(self, method, False, *args)
self.__demons.append(demon)
return demon
def DelayedDemon(self, method, *args):
demon = PyConstraintDemon(self, method, True, *args)
self.__demons.append(demon)
return demon
def InitialPropagateDemon(self):
return self.solver().ConstraintInitialPropagateCallback(self)
def DelayedInitialPropagateDemon(self):
return self.solver().DelayedConstraintInitialPropagateCallback(self)
def InitialPropagateWrapper(self):
try:
self.InitialPropagate()
except Exception as e:
if 'CP Solver fail' in str(e):
self.solver().ShouldFail()
else:
raise
def DebugString(self):
return "PyConstraint"
class RoutingIndexManager(object):
r"""
Manager for any NodeIndex <-> variable index conversion. The routing solver
uses variable indices internally and through its API. These variable indices
are tricky to manage directly because one Node can correspond to a multitude
of variables, depending on the number of times they appear in the model, and
if they're used as start and/or end points. This class aims to simplify
variable index usage, allowing users to use NodeIndex instead.
Usage:
.. code-block:: c++
auto starts_ends = ...; /// These are NodeIndex.
RoutingIndexManager manager(10, 4, starts_ends); // 10 nodes, 4 vehicles.
RoutingModel model(manager);
Then, use 'manager.NodeToIndex(node)' whenever model requires a variable
index.
Note: the mapping between node indices and variables indices is subject to
change so no assumption should be made on it. The only guarantee is that
indices range between 0 and n-1, where n = number of vehicles * 2 (for start
and end nodes) + number of non-start or end nodes.
"""
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
__repr__ = _swig_repr
def __init__(self, *args):
_pywrapcp.RoutingIndexManager_swiginit(self, _pywrapcp.new_RoutingIndexManager(*args))
__swig_destroy__ = _pywrapcp.delete_RoutingIndexManager
def GetNumberOfNodes(self) -> "int":
return _pywrapcp.RoutingIndexManager_GetNumberOfNodes(self)
def GetNumberOfVehicles(self) -> "int":
return _pywrapcp.RoutingIndexManager_GetNumberOfVehicles(self)
def GetNumberOfIndices(self) -> "int":
return _pywrapcp.RoutingIndexManager_GetNumberOfIndices(self)
def GetStartIndex(self, vehicle: "int") -> "int64_t":
return _pywrapcp.RoutingIndexManager_GetStartIndex(self, vehicle)
def GetEndIndex(self, vehicle: "int") -> "int64_t":
return _pywrapcp.RoutingIndexManager_GetEndIndex(self, vehicle)
def NodeToIndex(self, node: "operations_research::RoutingIndexManager::NodeIndex") -> "int64_t":
return _pywrapcp.RoutingIndexManager_NodeToIndex(self, node)
def IndexToNode(self, index: "int64_t") -> "operations_research::RoutingIndexManager::NodeIndex":
return _pywrapcp.RoutingIndexManager_IndexToNode(self, index)
# Register RoutingIndexManager in _pywrapcp:
_pywrapcp.RoutingIndexManager_swigregister(RoutingIndexManager)
def DefaultRoutingModelParameters() -> "operations_research::RoutingModelParameters":
return _pywrapcp.DefaultRoutingModelParameters()
def DefaultRoutingSearchParameters() -> "operations_research::RoutingSearchParameters":
return _pywrapcp.DefaultRoutingSearchParameters()
def FindErrorInRoutingSearchParameters(search_parameters: "operations_research::RoutingSearchParameters const &") -> "std::string":
r"""
Returns an empty std::string if the routing search parameters are valid, and
a non-empty, human readable error description if they're not.
"""
return _pywrapcp.FindErrorInRoutingSearchParameters(search_parameters)
BOOL_UNSPECIFIED = _pywrapcp.BOOL_UNSPECIFIED
BOOL_FALSE = _pywrapcp.BOOL_FALSE
BOOL_TRUE = _pywrapcp.BOOL_TRUE
class RoutingModel(object):
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
__repr__ = _swig_repr
ROUTING_NOT_SOLVED = _pywrapcp.RoutingModel_ROUTING_NOT_SOLVED
r""" Problem not solved yet (before calling RoutingModel::Solve())."""
ROUTING_SUCCESS = _pywrapcp.RoutingModel_ROUTING_SUCCESS
r""" Problem solved successfully after calling RoutingModel::Solve()."""
ROUTING_FAIL = _pywrapcp.RoutingModel_ROUTING_FAIL
r""" No solution found to the problem after calling RoutingModel::Solve()."""
ROUTING_FAIL_TIMEOUT = _pywrapcp.RoutingModel_ROUTING_FAIL_TIMEOUT
r""" Time limit reached before finding a solution with RoutingModel::Solve()."""
ROUTING_INVALID = _pywrapcp.RoutingModel_ROUTING_INVALID
r""" Model, model parameters or flags are not valid."""
PICKUP_AND_DELIVERY_NO_ORDER = _pywrapcp.RoutingModel_PICKUP_AND_DELIVERY_NO_ORDER
r""" Any precedence is accepted."""
PICKUP_AND_DELIVERY_LIFO = _pywrapcp.RoutingModel_PICKUP_AND_DELIVERY_LIFO
r""" Deliveries must be performed in reverse order of pickups."""
PICKUP_AND_DELIVERY_FIFO = _pywrapcp.RoutingModel_PICKUP_AND_DELIVERY_FIFO
r""" Deliveries must be performed in the same order as pickups."""
def __init__(self, *args):
_pywrapcp.RoutingModel_swiginit(self, _pywrapcp.new_RoutingModel(*args))
__swig_destroy__ = _pywrapcp.delete_RoutingModel
def RegisterUnaryTransitVector(self, values: "std::vector< int64_t >") -> "int":
r""" Registers 'callback' and returns its index."""
return _pywrapcp.RoutingModel_RegisterUnaryTransitVector(self, values)
def RegisterUnaryTransitCallback(self, callback: "operations_research::RoutingModel::TransitCallback1") -> "int":
return _pywrapcp.RoutingModel_RegisterUnaryTransitCallback(self, callback)
def RegisterPositiveUnaryTransitCallback(self, callback: "operations_research::RoutingModel::TransitCallback1") -> "int":
return _pywrapcp.RoutingModel_RegisterPositiveUnaryTransitCallback(self, callback)
def RegisterTransitMatrix(self, values: "std::vector< std::vector< int64_t > >") -> "int":
return _pywrapcp.RoutingModel_RegisterTransitMatrix(self, values)
def RegisterTransitCallback(self, callback: "operations_research::RoutingModel::TransitCallback2") -> "int":
return _pywrapcp.RoutingModel_RegisterTransitCallback(self, callback)
def RegisterPositiveTransitCallback(self, callback: "operations_research::RoutingModel::TransitCallback2") -> "int":
return _pywrapcp.RoutingModel_RegisterPositiveTransitCallback(self, callback)
def TransitCallback(self, callback_index: "int") -> "operations_research::RoutingModel::TransitCallback2 const &":
return _pywrapcp.RoutingModel_TransitCallback(self, callback_index)
def UnaryTransitCallbackOrNull(self, callback_index: "int") -> "operations_research::RoutingModel::TransitCallback1 const &":
return _pywrapcp.RoutingModel_UnaryTransitCallbackOrNull(self, callback_index)
def AddDimension(self, evaluator_index: "int", slack_max: "int64_t", capacity: "int64_t", fix_start_cumul_to_zero: "bool", name: "std::string const &") -> "bool":
r"""
Model creation
Methods to add dimensions to routes; dimensions represent quantities
accumulated at nodes along the routes. They represent quantities such as
weights or volumes carried along the route, or distance or times.
Quantities at a node are represented by "cumul" variables and the increase
or decrease of quantities between nodes are represented by "transit"
variables. These variables are linked as follows:
if j == next(i), cumul(j) = cumul(i) + transit(i) + slack(i)
where slack is a positive slack variable (can represent waiting times for
a time dimension).
Setting the value of fix_start_cumul_to_zero to true will force the
"cumul" variable of the start node of all vehicles to be equal to 0.
Creates a dimension where the transit variable is constrained to be
equal to evaluator(i, next(i)); 'slack_max' is the upper bound of the
slack variable and 'capacity' is the upper bound of the cumul variables.
'name' is the name used to reference the dimension; this name is used to
get cumul and transit variables from the routing model.
Returns false if a dimension with the same name has already been created
(and doesn't create the new dimension).
Takes ownership of the callback 'evaluator'.
"""
return _pywrapcp.RoutingModel_AddDimension(self, evaluator_index, slack_max, capacity, fix_start_cumul_to_zero, name)
def AddDimensionWithVehicleTransits(self, evaluator_indices: "std::vector< int > const &", slack_max: "int64_t", capacity: "int64_t", fix_start_cumul_to_zero: "bool", name: "std::string const &") -> "bool":
return _pywrapcp.RoutingModel_AddDimensionWithVehicleTransits(self, evaluator_indices, slack_max, capacity, fix_start_cumul_to_zero, name)
def AddDimensionWithVehicleCapacity(self, evaluator_index: "int", slack_max: "int64_t", vehicle_capacities: "std::vector< int64_t >", fix_start_cumul_to_zero: "bool", name: "std::string const &") -> "bool":
return _pywrapcp.RoutingModel_AddDimensionWithVehicleCapacity(self, evaluator_index, slack_max, vehicle_capacities, fix_start_cumul_to_zero, name)
def AddDimensionWithVehicleTransitAndCapacity(self, evaluator_indices: "std::vector< int > const &", slack_max: "int64_t", vehicle_capacities: "std::vector< int64_t >", fix_start_cumul_to_zero: "bool", name: "std::string const &") -> "bool":
return _pywrapcp.RoutingModel_AddDimensionWithVehicleTransitAndCapacity(self, evaluator_indices, slack_max, vehicle_capacities, fix_start_cumul_to_zero, name)
def AddConstantDimensionWithSlack(self, value: "int64_t", capacity: "int64_t", slack_max: "int64_t", fix_start_cumul_to_zero: "bool", name: "std::string const &") -> "std::pair< int,bool >":
r"""
Creates a dimension where the transit variable is constrained to be
equal to 'value'; 'capacity' is the upper bound of the cumul variables.
'name' is the name used to reference the dimension; this name is used to
get cumul and transit variables from the routing model.
Returns a pair consisting of an index to the registered unary transit
callback and a bool denoting whether the dimension has been created.
It is false if a dimension with the same name has already been created
(and doesn't create the new dimension but still register a new callback).
"""
return _pywrapcp.RoutingModel_AddConstantDimensionWithSlack(self, value, capacity, slack_max, fix_start_cumul_to_zero, name)
def AddConstantDimension(self, value: "int64_t", capacity: "int64_t", fix_start_cumul_to_zero: "bool", name: "std::string const &") -> "std::pair< int,bool >":
return _pywrapcp.RoutingModel_AddConstantDimension(self, value, capacity, fix_start_cumul_to_zero, name)
def AddVectorDimension(self, values: "std::vector< int64_t >", capacity: "int64_t", fix_start_cumul_to_zero: "bool", name: "std::string const &") -> "std::pair< int,bool >":
r"""
Creates a dimension where the transit variable is constrained to be
equal to 'values[i]' for node i; 'capacity' is the upper bound of
the cumul variables. 'name' is the name used to reference the dimension;
this name is used to get cumul and transit variables from the routing
model.
Returns a pair consisting of an index to the registered unary transit
callback and a bool denoting whether the dimension has been created.
It is false if a dimension with the same name has already been created
(and doesn't create the new dimension but still register a new callback).
"""
return _pywrapcp.RoutingModel_AddVectorDimension(self, values, capacity, fix_start_cumul_to_zero, name)
def AddMatrixDimension(self, values: "std::vector< std::vector< int64_t > >", capacity: "int64_t", fix_start_cumul_to_zero: "bool", name: "std::string const &") -> "std::pair< int,bool >":
r"""
Creates a dimension where the transit variable is constrained to be
equal to 'values[i][next(i)]' for node i; 'capacity' is the upper bound of
the cumul variables. 'name' is the name used to reference the dimension;
this name is used to get cumul and transit variables from the routing
model.
Returns a pair consisting of an index to the registered transit callback
and a bool denoting whether the dimension has been created.
It is false if a dimension with the same name has already been created
(and doesn't create the new dimension but still register a new callback).
"""
return _pywrapcp.RoutingModel_AddMatrixDimension(self, values, capacity, fix_start_cumul_to_zero, name)
def MakePathSpansAndTotalSlacks(self, dimension: "RoutingDimension", spans: "std::vector< operations_research::IntVar * >", total_slacks: "std::vector< operations_research::IntVar * >") -> "operations_research::Constraint *":
r"""
For every vehicle of the routing model:
- if total_slacks[vehicle] is not nullptr, constrains it to be the sum of
slacks on that vehicle, that is,
dimension->CumulVar(end) - dimension->CumulVar(start) -
sum_{node in path of vehicle} dimension->FixedTransitVar(node).
- if spans[vehicle] is not nullptr, constrains it to be
dimension->CumulVar(end) - dimension->CumulVar(start)
This does stronger propagation than a decomposition, and takes breaks into
account.
"""
return _pywrapcp.RoutingModel_MakePathSpansAndTotalSlacks(self, dimension, spans, total_slacks)
def GetAllDimensionNames(self) -> "std::vector< std::string >":
r""" Outputs the names of all dimensions added to the routing engine."""
return _pywrapcp.RoutingModel_GetAllDimensionNames(self)
def GetDimensions(self) -> "std::vector< operations_research::RoutingDimension * > const &":
r""" Returns all dimensions of the model."""
return _pywrapcp.RoutingModel_GetDimensions(self)
def GetDimensionsWithSoftOrSpanCosts(self) -> "std::vector< operations_research::RoutingDimension * >":
r""" Returns dimensions with soft or vehicle span costs."""
return _pywrapcp.RoutingModel_GetDimensionsWithSoftOrSpanCosts(self)
def GetGlobalDimensionCumulOptimizers(self) -> "std::vector< std::unique_ptr< operations_research::GlobalDimensionCumulOptimizer > > const &":
r"""
Returns [global|local]_dimension_optimizers_, which are empty if the model
has not been closed.
"""
return _pywrapcp.RoutingModel_GetGlobalDimensionCumulOptimizers(self)
def GetLocalDimensionCumulOptimizers(self) -> "std::vector< std::unique_ptr< operations_research::LocalDimensionCumulOptimizer > > const &":
return _pywrapcp.RoutingModel_GetLocalDimensionCumulOptimizers(self)
def GetLocalDimensionCumulMPOptimizers(self) -> "std::vector< std::unique_ptr< operations_research::LocalDimensionCumulOptimizer > > const &":
return _pywrapcp.RoutingModel_GetLocalDimensionCumulMPOptimizers(self)
def GetMutableGlobalCumulOptimizer(self, dimension: "RoutingDimension") -> "operations_research::GlobalDimensionCumulOptimizer *":
r"""
Returns the global/local dimension cumul optimizer for a given dimension,
or nullptr if there is none.
"""
return _pywrapcp.RoutingModel_GetMutableGlobalCumulOptimizer(self, dimension)
def GetMutableLocalCumulOptimizer(self, dimension: "RoutingDimension") -> "operations_research::LocalDimensionCumulOptimizer *":
return _pywrapcp.RoutingModel_GetMutableLocalCumulOptimizer(self, dimension)
def GetMutableLocalCumulMPOptimizer(self, dimension: "RoutingDimension") -> "operations_research::LocalDimensionCumulOptimizer *":
return _pywrapcp.RoutingModel_GetMutableLocalCumulMPOptimizer(self, dimension)
def HasDimension(self, dimension_name: "std::string const &") -> "bool":
r""" Returns true if a dimension exists for a given dimension name."""
return _pywrapcp.RoutingModel_HasDimension(self, dimension_name)
def GetDimensionOrDie(self, dimension_name: "std::string const &") -> "operations_research::RoutingDimension const &":
r""" Returns a dimension from its name. Dies if the dimension does not exist."""
return _pywrapcp.RoutingModel_GetDimensionOrDie(self, dimension_name)
def GetMutableDimension(self, dimension_name: "std::string const &") -> "operations_research::RoutingDimension *":
r"""
Returns a dimension from its name. Returns nullptr if the dimension does
not exist.
"""
return _pywrapcp.RoutingModel_GetMutableDimension(self, dimension_name)
def SetPrimaryConstrainedDimension(self, dimension_name: "std::string const &") -> "void":
r"""
Set the given dimension as "primary constrained". As of August 2013, this
is only used by ArcIsMoreConstrainedThanArc().
"dimension" must be the name of an existing dimension, or be empty, in
which case there will not be a primary dimension after this call.
"""
return _pywrapcp.RoutingModel_SetPrimaryConstrainedDimension(self, dimension_name)
def GetPrimaryConstrainedDimension(self) -> "std::string const &":
r""" Get the primary constrained dimension, or an empty string if it is unset."""
return _pywrapcp.RoutingModel_GetPrimaryConstrainedDimension(self)
def AddDisjunction(self, *args) -> "operations_research::RoutingModel::DisjunctionIndex":
r"""
Adds a disjunction constraint on the indices: exactly 'max_cardinality' of
the indices are active. Start and end indices of any vehicle cannot be
part of a disjunction.
If a penalty is given, at most 'max_cardinality' of the indices can be
active, and if less are active, 'penalty' is payed per inactive index.
This is equivalent to adding the constraint:
p + Sum(i)active[i] == max_cardinality
where p is an integer variable, and the following cost to the cost
function:
p * penalty.
'penalty' must be positive to make the disjunction optional; a negative
penalty will force 'max_cardinality' indices of the disjunction to be
performed, and therefore p == 0.
Note: passing a vector with a single index will model an optional index
with a penalty cost if it is not visited.
"""
return _pywrapcp.RoutingModel_AddDisjunction(self, *args)
def GetDisjunctionIndices(self, index: "int64_t") -> "std::vector< operations_research::RoutingModel::DisjunctionIndex > const &":
r""" Returns the indices of the disjunctions to which an index belongs."""
return _pywrapcp.RoutingModel_GetDisjunctionIndices(self, index)
def GetDisjunctionPenalty(self, index: "operations_research::RoutingModel::DisjunctionIndex") -> "int64_t":
r""" Returns the penalty of the node disjunction of index 'index'."""
return _pywrapcp.RoutingModel_GetDisjunctionPenalty(self, index)
def GetDisjunctionMaxCardinality(self, index: "operations_research::RoutingModel::DisjunctionIndex") -> "int64_t":
r"""
Returns the maximum number of possible active nodes of the node
disjunction of index 'index'.
"""
return _pywrapcp.RoutingModel_GetDisjunctionMaxCardinality(self, index)
def GetNumberOfDisjunctions(self) -> "int":
r""" Returns the number of node disjunctions in the model."""
return _pywrapcp.RoutingModel_GetNumberOfDisjunctions(self)
def GetPerfectBinaryDisjunctions(self) -> "std::vector< std::pair< int64_t,int64_t > >":
r"""
Returns the list of all perfect binary disjunctions, as pairs of variable
indices: a disjunction is "perfect" when its variables do not appear in
any other disjunction. Each pair is sorted (lowest variable index first),
and the output vector is also sorted (lowest pairs first).
"""
return _pywrapcp.RoutingModel_GetPerfectBinaryDisjunctions(self)
def IgnoreDisjunctionsAlreadyForcedToZero(self) -> "void":
r"""
SPECIAL: Makes the solver ignore all the disjunctions whose active
variables are all trivially zero (i.e. Max() == 0), by setting their
max_cardinality to 0.
This can be useful when using the BaseBinaryDisjunctionNeighborhood
operators, in the context of arc-based routing.
"""
return _pywrapcp.RoutingModel_IgnoreDisjunctionsAlreadyForcedToZero(self)
def AddSoftSameVehicleConstraint(self, indices: "std::vector< int64_t > const &", cost: "int64_t") -> "void":
r"""
Adds a soft constraint to force a set of variable indices to be on the
same vehicle. If all nodes are not on the same vehicle, each extra vehicle
used adds 'cost' to the cost function.
"""
return _pywrapcp.RoutingModel_AddSoftSameVehicleConstraint(self, indices, cost)
def SetAllowedVehiclesForIndex(self, vehicles: "std::vector< int > const &", index: "int64_t") -> "void":
r"""
Sets the vehicles which can visit a given node. If the node is in a
disjunction, this will not prevent it from being unperformed.
Specifying an empty vector of vehicles has no effect (all vehicles
will be allowed to visit the node).
"""
return _pywrapcp.RoutingModel_SetAllowedVehiclesForIndex(self, vehicles, index)
def IsVehicleAllowedForIndex(self, vehicle: "int", index: "int64_t") -> "bool":
r""" Returns true if a vehicle is allowed to visit a given node."""
return _pywrapcp.RoutingModel_IsVehicleAllowedForIndex(self, vehicle, index)
def AddPickupAndDelivery(self, pickup: "int64_t", delivery: "int64_t") -> "void":
r"""
Notifies that index1 and index2 form a pair of nodes which should belong
to the same route. This methods helps the search find better solutions,
especially in the local search phase.
It should be called each time you have an equality constraint linking
the vehicle variables of two node (including for instance pickup and
delivery problems):
Solver* const solver = routing.solver();
int64_t index1 = manager.NodeToIndex(node1);
int64_t index2 = manager.NodeToIndex(node2);
solver->AddConstraint(solver->MakeEquality(
routing.VehicleVar(index1),
routing.VehicleVar(index2)));
routing.AddPickupAndDelivery(index1, index2);
"""
return _pywrapcp.RoutingModel_AddPickupAndDelivery(self, pickup, delivery)
def AddPickupAndDeliverySets(self, pickup_disjunction: "operations_research::RoutingModel::DisjunctionIndex", delivery_disjunction: "operations_research::RoutingModel::DisjunctionIndex") -> "void":
r"""
Same as AddPickupAndDelivery but notifying that the performed node from
the disjunction of index 'pickup_disjunction' is on the same route as the
performed node from the disjunction of index 'delivery_disjunction'.
"""
return _pywrapcp.RoutingModel_AddPickupAndDeliverySets(self, pickup_disjunction, delivery_disjunction)
def GetPickupIndexPairs(self, node_index: "int64_t") -> "std::vector< std::pair< int,int > > const &":
r"""
Returns pairs for which the node is a pickup; the first element of each
pair is the index in the pickup and delivery pairs list in which the
pickup appears, the second element is its index in the pickups list.
"""
return _pywrapcp.RoutingModel_GetPickupIndexPairs(self, node_index)
def GetDeliveryIndexPairs(self, node_index: "int64_t") -> "std::vector< std::pair< int,int > > const &":
r""" Same as above for deliveries."""
return _pywrapcp.RoutingModel_GetDeliveryIndexPairs(self, node_index)
def SetPickupAndDeliveryPolicyOfAllVehicles(self, policy: "operations_research::RoutingModel::PickupAndDeliveryPolicy") -> "void":
r"""
Sets the Pickup and delivery policy of all vehicles. It is equivalent to
calling SetPickupAndDeliveryPolicyOfVehicle on all vehicles.
"""
return _pywrapcp.RoutingModel_SetPickupAndDeliveryPolicyOfAllVehicles(self, policy)
def SetPickupAndDeliveryPolicyOfVehicle(self, policy: "operations_research::RoutingModel::PickupAndDeliveryPolicy", vehicle: "int") -> "void":
return _pywrapcp.RoutingModel_SetPickupAndDeliveryPolicyOfVehicle(self, policy, vehicle)
def GetPickupAndDeliveryPolicyOfVehicle(self, vehicle: "int") -> "operations_research::RoutingModel::PickupAndDeliveryPolicy":
return _pywrapcp.RoutingModel_GetPickupAndDeliveryPolicyOfVehicle(self, vehicle)
def GetNumOfSingletonNodes(self) -> "int":
r"""
Returns the number of non-start/end nodes which do not appear in a
pickup/delivery pair.
"""
return _pywrapcp.RoutingModel_GetNumOfSingletonNodes(self)
TYPE_ADDED_TO_VEHICLE = _pywrapcp.RoutingModel_TYPE_ADDED_TO_VEHICLE
r""" When visited, the number of types 'T' on the vehicle increases by one."""
ADDED_TYPE_REMOVED_FROM_VEHICLE = _pywrapcp.RoutingModel_ADDED_TYPE_REMOVED_FROM_VEHICLE
r"""
When visited, one instance of type 'T' previously added to the route
(TYPE_ADDED_TO_VEHICLE), if any, is removed from the vehicle.
If the type was not previously added to the route or all added instances
have already been removed, this visit has no effect on the types.
"""
TYPE_ON_VEHICLE_UP_TO_VISIT = _pywrapcp.RoutingModel_TYPE_ON_VEHICLE_UP_TO_VISIT
r"""
With the following policy, the visit enforces that type 'T' is
considered on the route from its start until this node is visited.
"""
TYPE_SIMULTANEOUSLY_ADDED_AND_REMOVED = _pywrapcp.RoutingModel_TYPE_SIMULTANEOUSLY_ADDED_AND_REMOVED
r"""
The visit doesn't have an impact on the number of types 'T' on the
route, as it's (virtually) added and removed directly.
This policy can be used for visits which are part of an incompatibility
or requirement set without affecting the type count on the route.
"""
def SetVisitType(self, index: "int64_t", type: "int", type_policy: "operations_research::RoutingModel::VisitTypePolicy") -> "void":
return _pywrapcp.RoutingModel_SetVisitType(self, index, type, type_policy)
def GetVisitType(self, index: "int64_t") -> "int":
return _pywrapcp.RoutingModel_GetVisitType(self, index)
def GetSingleNodesOfType(self, type: "int") -> "std::vector< int > const &":
return _pywrapcp.RoutingModel_GetSingleNodesOfType(self, type)
def GetPairIndicesOfType(self, type: "int") -> "std::vector< int > const &":
return _pywrapcp.RoutingModel_GetPairIndicesOfType(self, type)
def GetVisitTypePolicy(self, index: "int64_t") -> "operations_research::RoutingModel::VisitTypePolicy":
return _pywrapcp.RoutingModel_GetVisitTypePolicy(self, index)
def CloseVisitTypes(self) -> "void":
r"""
This function should be called once all node visit types have been set and
prior to adding any incompatibilities/requirements.
"close" types.
"""
return _pywrapcp.RoutingModel_CloseVisitTypes(self)
def GetNumberOfVisitTypes(self) -> "int":
return _pywrapcp.RoutingModel_GetNumberOfVisitTypes(self)
def AddHardTypeIncompatibility(self, type1: "int", type2: "int") -> "void":
r"""
Incompatibilities:
Two nodes with "hard" incompatible types cannot share the same route at
all, while with a "temporal" incompatibility they can't be on the same
route at the same time.
"""
return _pywrapcp.RoutingModel_AddHardTypeIncompatibility(self, type1, type2)
def AddTemporalTypeIncompatibility(self, type1: "int", type2: "int") -> "void":
return _pywrapcp.RoutingModel_AddTemporalTypeIncompatibility(self, type1, type2)
def GetHardTypeIncompatibilitiesOfType(self, type: "int") -> "absl::flat_hash_set< int > const &":
r""" Returns visit types incompatible with a given type."""
return _pywrapcp.RoutingModel_GetHardTypeIncompatibilitiesOfType(self, type)
def GetTemporalTypeIncompatibilitiesOfType(self, type: "int") -> "absl::flat_hash_set< int > const &":
return _pywrapcp.RoutingModel_GetTemporalTypeIncompatibilitiesOfType(self, type)
def HasHardTypeIncompatibilities(self) -> "bool":
r"""
Returns true iff any hard (resp. temporal) type incompatibilities have
been added to the model.
"""
return _pywrapcp.RoutingModel_HasHardTypeIncompatibilities(self)
def HasTemporalTypeIncompatibilities(self) -> "bool":
return _pywrapcp.RoutingModel_HasTemporalTypeIncompatibilities(self)
def AddSameVehicleRequiredTypeAlternatives(self, dependent_type: "int", required_type_alternatives: "absl::flat_hash_set< int >") -> "void":
r"""
Requirements:
NOTE: As of 2019-04, cycles in the requirement graph are not supported,
and lead to the dependent nodes being skipped if possible (otherwise
the model is considered infeasible).
The following functions specify that "dependent_type" requires at least
one of the types in "required_type_alternatives".
For same-vehicle requirements, a node of dependent type type_D requires at
least one node of type type_R among the required alternatives on the same
route.
"""
return _pywrapcp.RoutingModel_AddSameVehicleRequiredTypeAlternatives(self, dependent_type, required_type_alternatives)
def AddRequiredTypeAlternativesWhenAddingType(self, dependent_type: "int", required_type_alternatives: "absl::flat_hash_set< int >") -> "void":
r"""
If type_D depends on type_R when adding type_D, any node_D of type_D and
VisitTypePolicy TYPE_ADDED_TO_VEHICLE or
TYPE_SIMULTANEOUSLY_ADDED_AND_REMOVED requires at least one type_R on its
vehicle at the time node_D is visited.
"""
return _pywrapcp.RoutingModel_AddRequiredTypeAlternativesWhenAddingType(self, dependent_type, required_type_alternatives)
def AddRequiredTypeAlternativesWhenRemovingType(self, dependent_type: "int", required_type_alternatives: "absl::flat_hash_set< int >") -> "void":
r"""
The following requirements apply when visiting dependent nodes that remove
their type from the route, i.e. type_R must be on the vehicle when type_D
of VisitTypePolicy ADDED_TYPE_REMOVED_FROM_VEHICLE,
TYPE_ON_VEHICLE_UP_TO_VISIT or TYPE_SIMULTANEOUSLY_ADDED_AND_REMOVED is
visited.
"""
return _pywrapcp.RoutingModel_AddRequiredTypeAlternativesWhenRemovingType(self, dependent_type, required_type_alternatives)
def GetSameVehicleRequiredTypeAlternativesOfType(self, type: "int") -> "std::vector< absl::flat_hash_set< int > > const &":
r"""
Returns the set of same-vehicle requirement alternatives for the given
type.
"""
return _pywrapcp.RoutingModel_GetSameVehicleRequiredTypeAlternativesOfType(self, type)
def GetRequiredTypeAlternativesWhenAddingType(self, type: "int") -> "std::vector< absl::flat_hash_set< int > > const &":
r""" Returns the set of requirement alternatives when adding the given type."""
return _pywrapcp.RoutingModel_GetRequiredTypeAlternativesWhenAddingType(self, type)
def GetRequiredTypeAlternativesWhenRemovingType(self, type: "int") -> "std::vector< absl::flat_hash_set< int > > const &":
r""" Returns the set of requirement alternatives when removing the given type."""
return _pywrapcp.RoutingModel_GetRequiredTypeAlternativesWhenRemovingType(self, type)
def HasSameVehicleTypeRequirements(self) -> "bool":
r"""
Returns true iff any same-route (resp. temporal) type requirements have
been added to the model.
"""
return _pywrapcp.RoutingModel_HasSameVehicleTypeRequirements(self)
def HasTemporalTypeRequirements(self) -> "bool":
return _pywrapcp.RoutingModel_HasTemporalTypeRequirements(self)
def HasTypeRegulations(self) -> "bool":
r"""
Returns true iff the model has any incompatibilities or requirements set
on node types.
"""
return _pywrapcp.RoutingModel_HasTypeRegulations(self)
def UnperformedPenalty(self, var_index: "int64_t") -> "int64_t":
r"""
Get the "unperformed" penalty of a node. This is only well defined if the
node is only part of a single Disjunction involving only itself, and that
disjunction has a penalty. In all other cases, including forced active
nodes, this returns 0.
"""
return _pywrapcp.RoutingModel_UnperformedPenalty(self, var_index)
def UnperformedPenaltyOrValue(self, default_value: "int64_t", var_index: "int64_t") -> "int64_t":
r"""
Same as above except that it returns default_value instead of 0 when
penalty is not well defined (default value is passed as first argument to
simplify the usage of the method in a callback).
"""
return _pywrapcp.RoutingModel_UnperformedPenaltyOrValue(self, default_value, var_index)
def GetDepot(self) -> "int64_t":
r"""
Returns the variable index of the first starting or ending node of all
routes. If all routes start and end at the same node (single depot), this
is the node returned.
"""
return _pywrapcp.RoutingModel_GetDepot(self)
def SetMaximumNumberOfActiveVehicles(self, max_active_vehicles: "int") -> "void":
r"""
Constrains the maximum number of active vehicles, aka the number of
vehicles which do not have an empty route. For instance, this can be used
to limit the number of routes in the case where there are fewer drivers
than vehicles and that the fleet of vehicle is heterogeneous.
"""
return _pywrapcp.RoutingModel_SetMaximumNumberOfActiveVehicles(self, max_active_vehicles)
def GetMaximumNumberOfActiveVehicles(self) -> "int":
r""" Returns the maximum number of active vehicles."""
return _pywrapcp.RoutingModel_GetMaximumNumberOfActiveVehicles(self)
def SetArcCostEvaluatorOfAllVehicles(self, evaluator_index: "int") -> "void":
r"""
Sets the cost function of the model such that the cost of a segment of a
route between node 'from' and 'to' is evaluator(from, to), whatever the
route or vehicle performing the route.
"""
return _pywrapcp.RoutingModel_SetArcCostEvaluatorOfAllVehicles(self, evaluator_index)
def SetArcCostEvaluatorOfVehicle(self, evaluator_index: "int", vehicle: "int") -> "void":
r""" Sets the cost function for a given vehicle route."""
return _pywrapcp.RoutingModel_SetArcCostEvaluatorOfVehicle(self, evaluator_index, vehicle)
def SetFixedCostOfAllVehicles(self, cost: "int64_t") -> "void":
r"""
Sets the fixed cost of all vehicle routes. It is equivalent to calling
SetFixedCostOfVehicle on all vehicle routes.
"""
return _pywrapcp.RoutingModel_SetFixedCostOfAllVehicles(self, cost)
def SetFixedCostOfVehicle(self, cost: "int64_t", vehicle: "int") -> "void":
r""" Sets the fixed cost of one vehicle route."""
return _pywrapcp.RoutingModel_SetFixedCostOfVehicle(self, cost, vehicle)
def GetFixedCostOfVehicle(self, vehicle: "int") -> "int64_t":
r"""
Returns the route fixed cost taken into account if the route of the
vehicle is not empty, aka there's at least one node on the route other
than the first and last nodes.
"""
return _pywrapcp.RoutingModel_GetFixedCostOfVehicle(self, vehicle)
def SetAmortizedCostFactorsOfAllVehicles(self, linear_cost_factor: "int64_t", quadratic_cost_factor: "int64_t") -> "void":
r"""
The following methods set the linear and quadratic cost factors of
vehicles (must be positive values). The default value of these parameters
is zero for all vehicles.
When set, the cost_ of the model will contain terms aiming at reducing the
number of vehicles used in the model, by adding the following to the
objective for every vehicle v:
INDICATOR(v used in the model) *
[linear_cost_factor_of_vehicle_[v]
- quadratic_cost_factor_of_vehicle_[v]*(square of length of route v)]
i.e. for every used vehicle, we add the linear factor as fixed cost, and
subtract the square of the route length multiplied by the quadratic
factor. This second term aims at making the routes as dense as possible.
Sets the linear and quadratic cost factor of all vehicles.
"""
return _pywrapcp.RoutingModel_SetAmortizedCostFactorsOfAllVehicles(self, linear_cost_factor, quadratic_cost_factor)
def SetAmortizedCostFactorsOfVehicle(self, linear_cost_factor: "int64_t", quadratic_cost_factor: "int64_t", vehicle: "int") -> "void":
r""" Sets the linear and quadratic cost factor of the given vehicle."""
return _pywrapcp.RoutingModel_SetAmortizedCostFactorsOfVehicle(self, linear_cost_factor, quadratic_cost_factor, vehicle)
def GetAmortizedLinearCostFactorOfVehicles(self) -> "std::vector< int64_t > const &":
return _pywrapcp.RoutingModel_GetAmortizedLinearCostFactorOfVehicles(self)
def GetAmortizedQuadraticCostFactorOfVehicles(self) -> "std::vector< int64_t > const &":
return _pywrapcp.RoutingModel_GetAmortizedQuadraticCostFactorOfVehicles(self)
def ConsiderEmptyRouteCostsForVehicle(self, consider_costs: "bool", vehicle: "int") -> "void":
return _pywrapcp.RoutingModel_ConsiderEmptyRouteCostsForVehicle(self, consider_costs, vehicle)
def AreEmptyRouteCostsConsideredForVehicle(self, vehicle: "int") -> "bool":
return _pywrapcp.RoutingModel_AreEmptyRouteCostsConsideredForVehicle(self, vehicle)
def SetFirstSolutionEvaluator(self, evaluator: "operations_research::Solver::IndexEvaluator2") -> "void":
r"""
Gets/sets the evaluator used during the search. Only relevant when
RoutingSearchParameters.first_solution_strategy = EVALUATOR_STRATEGY.
Takes ownership of evaluator.
"""
return _pywrapcp.RoutingModel_SetFirstSolutionEvaluator(self, evaluator)
def AddLocalSearchOperator(self, ls_operator: "LocalSearchOperator") -> "void":
r"""
Adds a local search operator to the set of operators used to solve the
vehicle routing problem.
"""
return _pywrapcp.RoutingModel_AddLocalSearchOperator(self, ls_operator)
def AddSearchMonitor(self, monitor: "SearchMonitor") -> "void":
r""" Adds a search monitor to the search used to solve the routing model."""
return _pywrapcp.RoutingModel_AddSearchMonitor(self, monitor)
def AddAtSolutionCallback(self, callback: "std::function< void () >") -> "void":
r"""
Adds a callback called each time a solution is found during the search.
This is a shortcut to creating a monitor to call the callback on
AtSolution() and adding it with AddSearchMonitor.
"""
return _pywrapcp.RoutingModel_AddAtSolutionCallback(self, callback)
def AddVariableMinimizedByFinalizer(self, var: "IntVar") -> "void":
r"""
Adds a variable to minimize in the solution finalizer. The solution
finalizer is called each time a solution is found during the search and
allows to instantiate secondary variables (such as dimension cumul
variables).
"""
return _pywrapcp.RoutingModel_AddVariableMinimizedByFinalizer(self, var)
def AddVariableMaximizedByFinalizer(self, var: "IntVar") -> "void":
r"""
Adds a variable to maximize in the solution finalizer (see above for
information on the solution finalizer).
"""
return _pywrapcp.RoutingModel_AddVariableMaximizedByFinalizer(self, var)
def AddWeightedVariableMinimizedByFinalizer(self, var: "IntVar", cost: "int64_t") -> "void":
r"""
Adds a variable to minimize in the solution finalizer, with a weighted
priority: the higher the more priority it has.
"""
return _pywrapcp.RoutingModel_AddWeightedVariableMinimizedByFinalizer(self, var, cost)
def AddVariableTargetToFinalizer(self, var: "IntVar", target: "int64_t") -> "void":
r"""
Add a variable to set the closest possible to the target value in the
solution finalizer.
"""
return _pywrapcp.RoutingModel_AddVariableTargetToFinalizer(self, var, target)
def CloseModel(self) -> "void":
r"""
Closes the current routing model; after this method is called, no
modification to the model can be done, but RoutesToAssignment becomes
available. Note that CloseModel() is automatically called by Solve() and
other methods that produce solution.
This is equivalent to calling
CloseModelWithParameters(DefaultRoutingSearchParameters()).
"""
return _pywrapcp.RoutingModel_CloseModel(self)
def CloseModelWithParameters(self, search_parameters: "operations_research::RoutingSearchParameters const &") -> "void":
r"""
Same as above taking search parameters (as of 10/2015 some the parameters
have to be set when closing the model).
"""
return _pywrapcp.RoutingModel_CloseModelWithParameters(self, search_parameters)
def Solve(self, assignment: "Assignment"=None) -> "operations_research::Assignment const *":
r"""
Solves the current routing model; closes the current model.
This is equivalent to calling
SolveWithParameters(DefaultRoutingSearchParameters())
or
SolveFromAssignmentWithParameters(assignment,
DefaultRoutingSearchParameters()).
"""
return _pywrapcp.RoutingModel_Solve(self, assignment)
def SolveWithParameters(self, search_parameters: "operations_research::RoutingSearchParameters const &", solutions: "std::vector< operations_research::Assignment const * > *"=None) -> "operations_research::Assignment const *":
r"""
Solves the current routing model with the given parameters. If 'solutions'
is specified, it will contain the k best solutions found during the search
(from worst to best, including the one returned by this method), where k
corresponds to the 'number_of_solutions_to_collect' in
'search_parameters'. Note that the Assignment returned by the method and
the ones in solutions are owned by the underlying solver and should not be
deleted.
"""
return _pywrapcp.RoutingModel_SolveWithParameters(self, search_parameters, solutions)
def SolveFromAssignmentWithParameters(self, assignment: "Assignment", search_parameters: "operations_research::RoutingSearchParameters const &", solutions: "std::vector< operations_research::Assignment const * > *"=None) -> "operations_research::Assignment const *":
r"""
Same as above, except that if assignment is not null, it will be used as
the initial solution.
"""
return _pywrapcp.RoutingModel_SolveFromAssignmentWithParameters(self, assignment, search_parameters, solutions)
def SolveFromAssignmentsWithParameters(self, assignments: "std::vector< operations_research::Assignment const * > const &", search_parameters: "operations_research::RoutingSearchParameters const &", solutions: "std::vector< operations_research::Assignment const * > *"=None) -> "operations_research::Assignment const *":
r"""
Same as above but will try all assignments in order as first solutions
until one succeeds.
"""
return _pywrapcp.RoutingModel_SolveFromAssignmentsWithParameters(self, assignments, search_parameters, solutions)
def SetAssignmentFromOtherModelAssignment(self, target_assignment: "Assignment", source_model: "RoutingModel", source_assignment: "Assignment") -> "void":
r"""
Given a "source_model" and its "source_assignment", resets
"target_assignment" with the IntVar variables (nexts_, and vehicle_vars_
if costs aren't homogeneous across vehicles) of "this" model, with the
values set according to those in "other_assignment".
The objective_element of target_assignment is set to this->cost_.
"""
return _pywrapcp.RoutingModel_SetAssignmentFromOtherModelAssignment(self, target_assignment, source_model, source_assignment)
def ComputeLowerBound(self) -> "int64_t":
r"""
Computes a lower bound to the routing problem solving a linear assignment
problem. The routing model must be closed before calling this method.
Note that problems with node disjunction constraints (including optional
nodes) and non-homogenous costs are not supported (the method returns 0 in
these cases).
"""
return _pywrapcp.RoutingModel_ComputeLowerBound(self)
def status(self) -> "operations_research::RoutingModel::Status":
r""" Returns the current status of the routing model."""
return _pywrapcp.RoutingModel_status(self)
def ApplyLocks(self, locks: "std::vector< int64_t > const &") -> "operations_research::IntVar *":
r"""
Applies a lock chain to the next search. 'locks' represents an ordered
vector of nodes representing a partial route which will be fixed during
the next search; it will constrain next variables such that:
next[locks[i]] == locks[i+1].
Returns the next variable at the end of the locked chain; this variable is
not locked. An assignment containing the locks can be obtained by calling
PreAssignment().
"""
return _pywrapcp.RoutingModel_ApplyLocks(self, locks)
def ApplyLocksToAllVehicles(self, locks: "std::vector< std::vector< int64_t > > const &", close_routes: "bool") -> "bool":
r"""
Applies lock chains to all vehicles to the next search, such that locks[p]
is the lock chain for route p. Returns false if the locks do not contain
valid routes; expects that the routes do not contain the depots,
i.e. there are empty vectors in place of empty routes.
If close_routes is set to true, adds the end nodes to the route of each
vehicle and deactivates other nodes.
An assignment containing the locks can be obtained by calling
PreAssignment().
"""
return _pywrapcp.RoutingModel_ApplyLocksToAllVehicles(self, locks, close_routes)
def PreAssignment(self) -> "operations_research::Assignment const *const":
r"""
Returns an assignment used to fix some of the variables of the problem.
In practice, this assignment locks partial routes of the problem. This
can be used in the context of locking the parts of the routes which have
already been driven in online routing problems.
"""
return _pywrapcp.RoutingModel_PreAssignment(self)
def MutablePreAssignment(self) -> "operations_research::Assignment *":
return _pywrapcp.RoutingModel_MutablePreAssignment(self)
def WriteAssignment(self, file_name: "std::string const &") -> "bool":
r"""
Writes the current solution to a file containing an AssignmentProto.
Returns false if the file cannot be opened or if there is no current
solution.
"""
return _pywrapcp.RoutingModel_WriteAssignment(self, file_name)
def ReadAssignment(self, file_name: "std::string const &") -> "operations_research::Assignment *":
r"""
Reads an assignment from a file and returns the current solution.
Returns nullptr if the file cannot be opened or if the assignment is not
valid.
"""
return _pywrapcp.RoutingModel_ReadAssignment(self, file_name)
def RestoreAssignment(self, solution: "Assignment") -> "operations_research::Assignment *":
r"""
Restores an assignment as a solution in the routing model and returns the
new solution. Returns nullptr if the assignment is not valid.
"""
return _pywrapcp.RoutingModel_RestoreAssignment(self, solution)
def ReadAssignmentFromRoutes(self, routes: "std::vector< std::vector< int64_t > > const &", ignore_inactive_indices: "bool") -> "operations_research::Assignment *":
r"""
Restores the routes as the current solution. Returns nullptr if the
solution cannot be restored (routes do not contain a valid solution). Note
that calling this method will run the solver to assign values to the
dimension variables; this may take considerable amount of time, especially
when using dimensions with slack.
"""
return _pywrapcp.RoutingModel_ReadAssignmentFromRoutes(self, routes, ignore_inactive_indices)
def RoutesToAssignment(self, routes: "std::vector< std::vector< int64_t > > const &", ignore_inactive_indices: "bool", close_routes: "bool", assignment: "Assignment") -> "bool":
r"""
Fills an assignment from a specification of the routes of the
vehicles. The routes are specified as lists of variable indices that
appear on the routes of the vehicles. The indices of the outer vector in
'routes' correspond to vehicles IDs, the inner vector contains the
variable indices on the routes for the given vehicle. The inner vectors
must not contain the start and end indices, as these are determined by the
routing model. Sets the value of NextVars in the assignment, adding the
variables to the assignment if necessary. The method does not touch other
variables in the assignment. The method can only be called after the model
is closed. With ignore_inactive_indices set to false, this method will
fail (return nullptr) in case some of the route contain indices that are
deactivated in the model; when set to true, these indices will be
skipped. Returns true if routes were successfully
loaded. However, such assignment still might not be a valid
solution to the routing problem due to more complex constraints;
it is advisible to call solver()->CheckSolution() afterwards.
"""
return _pywrapcp.RoutingModel_RoutesToAssignment(self, routes, ignore_inactive_indices, close_routes, assignment)
def AssignmentToRoutes(self, assignment: "Assignment", routes: "std::vector< std::vector< int64_t > > *const") -> "void":
r"""
Converts the solution in the given assignment to routes for all vehicles.
Expects that assignment contains a valid solution (i.e. routes for all
vehicles end with an end index for that vehicle).
"""
return _pywrapcp.RoutingModel_AssignmentToRoutes(self, assignment, routes)
def CompactAssignment(self, assignment: "Assignment") -> "operations_research::Assignment *":
r"""
Converts the solution in the given assignment to routes for all vehicles.
If the returned vector is route_indices, route_indices[i][j] is the index
for jth location visited on route i. Note that contrary to
AssignmentToRoutes, the vectors do include start and end locations.
Returns a compacted version of the given assignment, in which all vehicles
with id lower or equal to some N have non-empty routes, and all vehicles
with id greater than N have empty routes. Does not take ownership of the
returned object.
If found, the cost of the compact assignment is the same as in the
original assignment and it preserves the values of 'active' variables.
Returns nullptr if a compact assignment was not found.
This method only works in homogenous mode, and it only swaps equivalent
vehicles (vehicles with the same start and end nodes). When creating the
compact assignment, the empty plan is replaced by the route assigned to
the compatible vehicle with the highest id. Note that with more complex
constraints on vehicle variables, this method might fail even if a compact
solution exists.
This method changes the vehicle and dimension variables as necessary.
While compacting the solution, only basic checks on vehicle variables are
performed; if one of these checks fails no attempts to repair it are made
(instead, the method returns nullptr).
"""
return _pywrapcp.RoutingModel_CompactAssignment(self, assignment)
def CompactAndCheckAssignment(self, assignment: "Assignment") -> "operations_research::Assignment *":
r"""
Same as CompactAssignment() but also checks the validity of the final
compact solution; if it is not valid, no attempts to repair it are made
(instead, the method returns nullptr).
"""
return _pywrapcp.RoutingModel_CompactAndCheckAssignment(self, assignment)
def AddToAssignment(self, var: "IntVar") -> "void":
r""" Adds an extra variable to the vehicle routing assignment."""
return _pywrapcp.RoutingModel_AddToAssignment(self, var)
def AddIntervalToAssignment(self, interval: "IntervalVar") -> "void":
return _pywrapcp.RoutingModel_AddIntervalToAssignment(self, interval)
def PackCumulsOfOptimizerDimensionsFromAssignment(self, original_assignment: "Assignment", duration_limit: "absl::Duration") -> "operations_research::Assignment const *":
r"""
For every dimension in the model with an optimizer in
local/global_dimension_optimizers_, this method tries to pack the cumul
values of the dimension, such that:
- The cumul costs (span costs, soft lower and upper bound costs, etc) are
minimized.
- The cumuls of the ends of the routes are minimized for this given
minimal cumul cost.
- Given these minimal end cumuls, the route start cumuls are maximized.
Returns the assignment resulting from allocating these packed cumuls with
the solver, and nullptr if these cumuls could not be set by the solver.
"""
return _pywrapcp.RoutingModel_PackCumulsOfOptimizerDimensionsFromAssignment(self, original_assignment, duration_limit)
def AddLocalSearchFilter(self, filter: "LocalSearchFilter") -> "void":
r"""
Adds a custom local search filter to the list of filters used to speed up
local search by pruning unfeasible variable assignments.
Calling this method after the routing model has been closed (CloseModel()
or Solve() has been called) has no effect.
The routing model does not take ownership of the filter.
"""
return _pywrapcp.RoutingModel_AddLocalSearchFilter(self, filter)
def Start(self, vehicle: "int") -> "int64_t":
r"""
Model inspection.
Returns the variable index of the starting node of a vehicle route.
"""
return _pywrapcp.RoutingModel_Start(self, vehicle)
def End(self, vehicle: "int") -> "int64_t":
r""" Returns the variable index of the ending node of a vehicle route."""
return _pywrapcp.RoutingModel_End(self, vehicle)
def IsStart(self, index: "int64_t") -> "bool":
r""" Returns true if 'index' represents the first node of a route."""
return _pywrapcp.RoutingModel_IsStart(self, index)
def IsEnd(self, index: "int64_t") -> "bool":
r""" Returns true if 'index' represents the last node of a route."""
return _pywrapcp.RoutingModel_IsEnd(self, index)
def VehicleIndex(self, index: "int64_t") -> "int":
r"""
Returns the vehicle of the given start/end index, and -1 if the given
index is not a vehicle start/end.
"""
return _pywrapcp.RoutingModel_VehicleIndex(self, index)
def Next(self, assignment: "Assignment", index: "int64_t") -> "int64_t":
r"""
Assignment inspection
Returns the variable index of the node directly after the node
corresponding to 'index' in 'assignment'.
"""
return _pywrapcp.RoutingModel_Next(self, assignment, index)
def IsVehicleUsed(self, assignment: "Assignment", vehicle: "int") -> "bool":
r""" Returns true if the route of 'vehicle' is non empty in 'assignment'."""
return _pywrapcp.RoutingModel_IsVehicleUsed(self, assignment, vehicle)
def NextVar(self, index: "int64_t") -> "operations_research::IntVar *":
r"""
Returns the next variable of the node corresponding to index. Note that
NextVar(index) == index is equivalent to ActiveVar(index) == 0.
"""
return _pywrapcp.RoutingModel_NextVar(self, index)
def ActiveVar(self, index: "int64_t") -> "operations_research::IntVar *":
r""" Returns the active variable of the node corresponding to index."""
return _pywrapcp.RoutingModel_ActiveVar(self, index)
def ActiveVehicleVar(self, vehicle: "int") -> "operations_research::IntVar *":
r"""
Returns the active variable of the vehicle. It will be equal to 1 iff the
route of the vehicle is not empty, 0 otherwise.
"""
return _pywrapcp.RoutingModel_ActiveVehicleVar(self, vehicle)
def VehicleCostsConsideredVar(self, vehicle: "int") -> "operations_research::IntVar *":
r"""
Returns the variable specifying whether or not costs are considered for
vehicle.
"""
return _pywrapcp.RoutingModel_VehicleCostsConsideredVar(self, vehicle)
def VehicleVar(self, index: "int64_t") -> "operations_research::IntVar *":
r"""
Returns the vehicle variable of the node corresponding to index. Note that
VehicleVar(index) == -1 is equivalent to ActiveVar(index) == 0.
"""
return _pywrapcp.RoutingModel_VehicleVar(self, index)
def CostVar(self) -> "operations_research::IntVar *":
r""" Returns the global cost variable which is being minimized."""
return _pywrapcp.RoutingModel_CostVar(self)
def GetArcCostForVehicle(self, from_index: "int64_t", to_index: "int64_t", vehicle: "int64_t") -> "int64_t":
r"""
Returns the cost of the transit arc between two nodes for a given vehicle.
Input are variable indices of node. This returns 0 if vehicle < 0.
"""
return _pywrapcp.RoutingModel_GetArcCostForVehicle(self, from_index, to_index, vehicle)
def CostsAreHomogeneousAcrossVehicles(self) -> "bool":
r""" Whether costs are homogeneous across all vehicles."""
return _pywrapcp.RoutingModel_CostsAreHomogeneousAcrossVehicles(self)
def GetHomogeneousCost(self, from_index: "int64_t", to_index: "int64_t") -> "int64_t":
r"""
Returns the cost of the segment between two nodes supposing all vehicle
costs are the same (returns the cost for the first vehicle otherwise).
"""
return _pywrapcp.RoutingModel_GetHomogeneousCost(self, from_index, to_index)
def GetArcCostForFirstSolution(self, from_index: "int64_t", to_index: "int64_t") -> "int64_t":
r"""
Returns the cost of the arc in the context of the first solution strategy.
This is typically a simplification of the actual cost; see the .cc.
"""
return _pywrapcp.RoutingModel_GetArcCostForFirstSolution(self, from_index, to_index)
def GetArcCostForClass(self, from_index: "int64_t", to_index: "int64_t", cost_class_index: "int64_t") -> "int64_t":
r"""
Returns the cost of the segment between two nodes for a given cost
class. Input are variable indices of nodes and the cost class.
Unlike GetArcCostForVehicle(), if cost_class is kNoCost, then the
returned cost won't necessarily be zero: only some of the components
of the cost that depend on the cost class will be omited. See the code
for details.
"""
return _pywrapcp.RoutingModel_GetArcCostForClass(self, from_index, to_index, cost_class_index)
def GetCostClassIndexOfVehicle(self, vehicle: "int64_t") -> "operations_research::RoutingModel::CostClassIndex":
r""" Get the cost class index of the given vehicle."""
return _pywrapcp.RoutingModel_GetCostClassIndexOfVehicle(self, vehicle)
def HasVehicleWithCostClassIndex(self, cost_class_index: "operations_research::RoutingModel::CostClassIndex") -> "bool":
r"""
Returns true iff the model contains a vehicle with the given
cost_class_index.
"""
return _pywrapcp.RoutingModel_HasVehicleWithCostClassIndex(self, cost_class_index)
def GetCostClassesCount(self) -> "int":
r""" Returns the number of different cost classes in the model."""
return _pywrapcp.RoutingModel_GetCostClassesCount(self)
def GetNonZeroCostClassesCount(self) -> "int":
r""" Ditto, minus the 'always zero', built-in cost class."""
return _pywrapcp.RoutingModel_GetNonZeroCostClassesCount(self)
def GetVehicleClassIndexOfVehicle(self, vehicle: "int64_t") -> "operations_research::RoutingModel::VehicleClassIndex":
return _pywrapcp.RoutingModel_GetVehicleClassIndexOfVehicle(self, vehicle)
def GetVehicleClassesCount(self) -> "int":
r""" Returns the number of different vehicle classes in the model."""
return _pywrapcp.RoutingModel_GetVehicleClassesCount(self)
def GetSameVehicleIndicesOfIndex(self, node: "int") -> "std::vector< int > const &":
r""" Returns variable indices of nodes constrained to be on the same route."""
return _pywrapcp.RoutingModel_GetSameVehicleIndicesOfIndex(self, node)
def GetVehicleTypeContainer(self) -> "operations_research::RoutingModel::VehicleTypeContainer const &":
return _pywrapcp.RoutingModel_GetVehicleTypeContainer(self)
def ArcIsMoreConstrainedThanArc(self, _from: "int64_t", to1: "int64_t", to2: "int64_t") -> "bool":
r"""
Returns whether the arc from->to1 is more constrained than from->to2,
taking into account, in order:
- whether the destination node isn't an end node
- whether the destination node is mandatory
- whether the destination node is bound to the same vehicle as the source
- the "primary constrained" dimension (see SetPrimaryConstrainedDimension)
It then breaks ties using, in order:
- the arc cost (taking unperformed penalties into account)
- the size of the vehicle vars of "to1" and "to2" (lowest size wins)
- the value: the lowest value of the indices to1 and to2 wins.
See the .cc for details.
The more constrained arc is typically preferable when building a
first solution. This method is intended to be used as a callback for the
BestValueByComparisonSelector value selector.
Args:
from: the variable index of the source node
to1: the variable index of the first candidate destination node.
to2: the variable index of the second candidate destination node.
"""
return _pywrapcp.RoutingModel_ArcIsMoreConstrainedThanArc(self, _from, to1, to2)
def DebugOutputAssignment(self, solution_assignment: "Assignment", dimension_to_print: "std::string const &") -> "std::string":
r"""
Print some debugging information about an assignment, including the
feasible intervals of the CumulVar for dimension "dimension_to_print"
at each step of the routes.
If "dimension_to_print" is omitted, all dimensions will be printed.
"""
return _pywrapcp.RoutingModel_DebugOutputAssignment(self, solution_assignment, dimension_to_print)
def solver(self) -> "operations_research::Solver *":
r"""
Returns a vector cumul_bounds, for which cumul_bounds[i][j] is a pair
containing the minimum and maximum of the CumulVar of the jth node on
route i.
- cumul_bounds[i][j].first is the minimum.
- cumul_bounds[i][j].second is the maximum.
Returns the underlying constraint solver. Can be used to add extra
constraints and/or modify search algoithms.
"""
return _pywrapcp.RoutingModel_solver(self)
def CheckLimit(self) -> "bool":
r""" Returns true if the search limit has been crossed."""
return _pywrapcp.RoutingModel_CheckLimit(self)
def RemainingTime(self) -> "absl::Duration":
r""" Returns the time left in the search limit."""
return _pywrapcp.RoutingModel_RemainingTime(self)
def nodes(self) -> "int":
r"""
Sizes and indices
Returns the number of nodes in the model.
"""
return _pywrapcp.RoutingModel_nodes(self)
def vehicles(self) -> "int":
r""" Returns the number of vehicle routes in the model."""
return _pywrapcp.RoutingModel_vehicles(self)
def Size(self) -> "int64_t":
r""" Returns the number of next variables in the model."""
return _pywrapcp.RoutingModel_Size(self)
def GetNumberOfDecisionsInFirstSolution(self, search_parameters: "operations_research::RoutingSearchParameters const &") -> "int64_t":
r"""
Returns statistics on first solution search, number of decisions sent to
filters, number of decisions rejected by filters.
"""
return _pywrapcp.RoutingModel_GetNumberOfDecisionsInFirstSolution(self, search_parameters)
def GetNumberOfRejectsInFirstSolution(self, search_parameters: "operations_research::RoutingSearchParameters const &") -> "int64_t":
return _pywrapcp.RoutingModel_GetNumberOfRejectsInFirstSolution(self, search_parameters)
def GetAutomaticFirstSolutionStrategy(self) -> "operations_research::FirstSolutionStrategy::Value":
r""" Returns the automatic first solution strategy selected."""
return _pywrapcp.RoutingModel_GetAutomaticFirstSolutionStrategy(self)
def IsMatchingModel(self) -> "bool":
r""" Returns true if a vehicle/node matching problem is detected."""
return _pywrapcp.RoutingModel_IsMatchingModel(self)
def MakeGuidedSlackFinalizer(self, dimension: "RoutingDimension", initializer: "std::function< int64_t (int64_t) >") -> "operations_research::DecisionBuilder *":
r"""
The next few members are in the public section only for testing purposes.
MakeGuidedSlackFinalizer creates a DecisionBuilder for the slacks of a
dimension using a callback to choose which values to start with.
The finalizer works only when all next variables in the model have
been fixed. It has the following two characteristics:
1. It follows the routes defined by the nexts variables when choosing a
variable to make a decision on.
2. When it comes to choose a value for the slack of node i, the decision
builder first calls the callback with argument i, and supposingly the
returned value is x it creates decisions slack[i] = x, slack[i] = x +
1, slack[i] = x - 1, slack[i] = x + 2, etc.
"""
return _pywrapcp.RoutingModel_MakeGuidedSlackFinalizer(self, dimension, initializer)
def MakeSelfDependentDimensionFinalizer(self, dimension: "RoutingDimension") -> "operations_research::DecisionBuilder *":
r"""
MakeSelfDependentDimensionFinalizer is a finalizer for the slacks of a
self-dependent dimension. It makes an extensive use of the caches of the
state dependent transits.
In detail, MakeSelfDependentDimensionFinalizer returns a composition of a
local search decision builder with a greedy descent operator for the cumul
of the start of each route and a guided slack finalizer. Provided there
are no time windows and the maximum slacks are large enough, once the
cumul of the start of route is fixed, the guided finalizer can find
optimal values of the slacks for the rest of the route in time
proportional to the length of the route. Therefore the composed finalizer
generally works in time O(log(t)*n*m), where t is the latest possible
departute time, n is the number of nodes in the network and m is the
number of vehicles.
"""
return _pywrapcp.RoutingModel_MakeSelfDependentDimensionFinalizer(self, dimension)
# Register RoutingModel in _pywrapcp:
_pywrapcp.RoutingModel_swigregister(RoutingModel)
cvar = _pywrapcp.cvar
RoutingModel.kNoPenalty = _pywrapcp.cvar.RoutingModel_kNoPenalty
RoutingModel.kNoDisjunction = _pywrapcp.cvar.RoutingModel_kNoDisjunction
RoutingModel.kNoDimension = _pywrapcp.cvar.RoutingModel_kNoDimension
class RoutingModelVisitor(BaseObject):
r""" Routing model visitor."""
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
__repr__ = _swig_repr
def __init__(self):
_pywrapcp.RoutingModelVisitor_swiginit(self, _pywrapcp.new_RoutingModelVisitor())
__swig_destroy__ = _pywrapcp.delete_RoutingModelVisitor
# Register RoutingModelVisitor in _pywrapcp:
_pywrapcp.RoutingModelVisitor_swigregister(RoutingModelVisitor)
RoutingModelVisitor.kLightElement = _pywrapcp.cvar.RoutingModelVisitor_kLightElement
RoutingModelVisitor.kLightElement2 = _pywrapcp.cvar.RoutingModelVisitor_kLightElement2
RoutingModelVisitor.kRemoveValues = _pywrapcp.cvar.RoutingModelVisitor_kRemoveValues
class GlobalVehicleBreaksConstraint(Constraint):
r"""
GlobalVehicleBreaksConstraint ensures breaks constraints are enforced on
all vehicles in the dimension passed to its constructor.
It is intended to be used for dimensions representing time.
A break constraint ensures break intervals fit on the route of a vehicle.
For a given vehicle, it forces break intervals to be disjoint from visit
intervals, where visit intervals start at CumulVar(node) and last for
node_visit_transit[node]. Moreover, it ensures that there is enough time
between two consecutive nodes of a route to do transit and vehicle breaks,
i.e. if Next(nodeA) = nodeB, CumulVar(nodeA) = tA and CumulVar(nodeB) = tB,
then SlackVar(nodeA) >= sum_{breaks [tA, tB)} duration(break).
"""
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
__repr__ = _swig_repr
def __init__(self, dimension: "RoutingDimension"):
_pywrapcp.GlobalVehicleBreaksConstraint_swiginit(self, _pywrapcp.new_GlobalVehicleBreaksConstraint(dimension))
def DebugString(self) -> "std::string":
return _pywrapcp.GlobalVehicleBreaksConstraint_DebugString(self)
def Post(self) -> "void":
return _pywrapcp.GlobalVehicleBreaksConstraint_Post(self)
def InitialPropagateWrapper(self) -> "void":
return _pywrapcp.GlobalVehicleBreaksConstraint_InitialPropagateWrapper(self)
__swig_destroy__ = _pywrapcp.delete_GlobalVehicleBreaksConstraint
# Register GlobalVehicleBreaksConstraint in _pywrapcp:
_pywrapcp.GlobalVehicleBreaksConstraint_swigregister(GlobalVehicleBreaksConstraint)
class TypeRegulationsChecker(object):
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
def __init__(self, *args, **kwargs):
raise AttributeError("No constructor defined - class is abstract")
__repr__ = _swig_repr
__swig_destroy__ = _pywrapcp.delete_TypeRegulationsChecker
def CheckVehicle(self, vehicle: "int", next_accessor: "std::function< int64_t (int64_t) > const &") -> "bool":
return _pywrapcp.TypeRegulationsChecker_CheckVehicle(self, vehicle, next_accessor)
# Register TypeRegulationsChecker in _pywrapcp:
_pywrapcp.TypeRegulationsChecker_swigregister(TypeRegulationsChecker)
class TypeIncompatibilityChecker(TypeRegulationsChecker):
r""" Checker for type incompatibilities."""
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
__repr__ = _swig_repr
def __init__(self, model: "RoutingModel", check_hard_incompatibilities: "bool"):
_pywrapcp.TypeIncompatibilityChecker_swiginit(self, _pywrapcp.new_TypeIncompatibilityChecker(model, check_hard_incompatibilities))
__swig_destroy__ = _pywrapcp.delete_TypeIncompatibilityChecker
# Register TypeIncompatibilityChecker in _pywrapcp:
_pywrapcp.TypeIncompatibilityChecker_swigregister(TypeIncompatibilityChecker)
class TypeRequirementChecker(TypeRegulationsChecker):
r""" Checker for type requirements."""
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
__repr__ = _swig_repr
def __init__(self, model: "RoutingModel"):
_pywrapcp.TypeRequirementChecker_swiginit(self, _pywrapcp.new_TypeRequirementChecker(model))
__swig_destroy__ = _pywrapcp.delete_TypeRequirementChecker
# Register TypeRequirementChecker in _pywrapcp:
_pywrapcp.TypeRequirementChecker_swigregister(TypeRequirementChecker)
class TypeRegulationsConstraint(Constraint):
r"""
The following constraint ensures that incompatibilities and requirements
between types are respected.
It verifies both "hard" and "temporal" incompatibilities.
Two nodes with hard incompatible types cannot be served by the same vehicle
at all, while with a temporal incompatibility they can't be on the same
route at the same time.
The VisitTypePolicy of a node determines how visiting it impacts the type
count on the route.
For example, for
- three temporally incompatible types T1 T2 and T3
- 2 pairs of nodes a1/r1 and a2/r2 of type T1 and T2 respectively, with
- a1 and a2 of VisitTypePolicy TYPE_ADDED_TO_VEHICLE
- r1 and r2 of policy ADDED_TYPE_REMOVED_FROM_VEHICLE
- 3 nodes A, UV and AR of type T3, respectively with type policies
TYPE_ADDED_TO_VEHICLE, TYPE_ON_VEHICLE_UP_TO_VISIT and
TYPE_SIMULTANEOUSLY_ADDED_AND_REMOVED
the configurations
UV --> a1 --> r1 --> a2 --> r2, a1 --> r1 --> a2 --> r2 --> A and
a1 --> r1 --> AR --> a2 --> r2 are acceptable, whereas the configurations
a1 --> a2 --> r1 --> ..., or A --> a1 --> r1 --> ..., or
a1 --> r1 --> UV --> ... are not feasible.
It also verifies same-vehicle and temporal type requirements.
A node of type T_d with a same-vehicle requirement for type T_r needs to be
served by the same vehicle as a node of type T_r.
Temporal requirements, on the other hand, can take effect either when the
dependent type is being added to the route or when it's removed from it,
which is determined by the dependent node's VisitTypePolicy.
In the above example:
- If T3 is required on the same vehicle as T1, A, AR or UV must be on the
same vehicle as a1.
- If T2 is required when adding T1, a2 must be visited *before* a1, and if
r2 is also visited on the route, it must be *after* a1, i.e. T2 must be on
the vehicle when a1 is visited:
... --> a2 --> ... --> a1 --> ... --> r2 --> ...
- If T3 is required when removing T1, T3 needs to be on the vehicle when
r1 is visited:
... --> A --> ... --> r1 --> ... OR ... --> r1 --> ... --> UV --> ...
"""
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
__repr__ = _swig_repr
def __init__(self, model: "RoutingModel"):
_pywrapcp.TypeRegulationsConstraint_swiginit(self, _pywrapcp.new_TypeRegulationsConstraint(model))
def Post(self) -> "void":
return _pywrapcp.TypeRegulationsConstraint_Post(self)
def InitialPropagateWrapper(self) -> "void":
return _pywrapcp.TypeRegulationsConstraint_InitialPropagateWrapper(self)
__swig_destroy__ = _pywrapcp.delete_TypeRegulationsConstraint
# Register TypeRegulationsConstraint in _pywrapcp:
_pywrapcp.TypeRegulationsConstraint_swigregister(TypeRegulationsConstraint)
class RoutingDimension(object):
r"""
Dimensions represent quantities accumulated at nodes along the routes. They
represent quantities such as weights or volumes carried along the route, or
distance or times.
Quantities at a node are represented by "cumul" variables and the increase
or decrease of quantities between nodes are represented by "transit"
variables. These variables are linked as follows:
if j == next(i),
cumuls(j) = cumuls(i) + transits(i) + slacks(i) +
state_dependent_transits(i)
where slack is a positive slack variable (can represent waiting times for
a time dimension), and state_dependent_transits is a non-purely functional
version of transits_. Favour transits over state_dependent_transits when
possible, because purely functional callbacks allow more optimisations and
make the model faster and easier to solve.
for a given vehicle, it is passed as an external vector, it would be better
to have this information here.
"""
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
def __init__(self, *args, **kwargs):
raise AttributeError("No constructor defined")
__repr__ = _swig_repr
__swig_destroy__ = _pywrapcp.delete_RoutingDimension
def model(self) -> "operations_research::RoutingModel *":
r""" Returns the model on which the dimension was created."""
return _pywrapcp.RoutingDimension_model(self)
def GetTransitValue(self, from_index: "int64_t", to_index: "int64_t", vehicle: "int64_t") -> "int64_t":
r"""
Returns the transition value for a given pair of nodes (as var index);
this value is the one taken by the corresponding transit variable when
the 'next' variable for 'from_index' is bound to 'to_index'.
"""
return _pywrapcp.RoutingDimension_GetTransitValue(self, from_index, to_index, vehicle)
def GetTransitValueFromClass(self, from_index: "int64_t", to_index: "int64_t", vehicle_class: "int64_t") -> "int64_t":
r"""
Same as above but taking a vehicle class of the dimension instead of a
vehicle (the class of a vehicle can be obtained with vehicle_to_class()).
"""
return _pywrapcp.RoutingDimension_GetTransitValueFromClass(self, from_index, to_index, vehicle_class)
def CumulVar(self, index: "int64_t") -> "operations_research::IntVar *":
r"""
Get the cumul, transit and slack variables for the given node (given as
int64_t var index).
"""
return _pywrapcp.RoutingDimension_CumulVar(self, index)
def TransitVar(self, index: "int64_t") -> "operations_research::IntVar *":
return _pywrapcp.RoutingDimension_TransitVar(self, index)
def FixedTransitVar(self, index: "int64_t") -> "operations_research::IntVar *":
return _pywrapcp.RoutingDimension_FixedTransitVar(self, index)
def SlackVar(self, index: "int64_t") -> "operations_research::IntVar *":
return _pywrapcp.RoutingDimension_SlackVar(self, index)
def SetSpanUpperBoundForVehicle(self, upper_bound: "int64_t", vehicle: "int") -> "void":
r"""
Sets an upper bound on the dimension span on a given vehicle. This is the
preferred way to limit the "length" of the route of a vehicle according to
a dimension.
"""
return _pywrapcp.RoutingDimension_SetSpanUpperBoundForVehicle(self, upper_bound, vehicle)
def SetSpanCostCoefficientForVehicle(self, coefficient: "int64_t", vehicle: "int") -> "void":
r"""
Sets a cost proportional to the dimension span on a given vehicle,
or on all vehicles at once. "coefficient" must be nonnegative.
This is handy to model costs proportional to idle time when the dimension
represents time.
The cost for a vehicle is
span_cost = coefficient * (dimension end value - dimension start value).
"""
return _pywrapcp.RoutingDimension_SetSpanCostCoefficientForVehicle(self, coefficient, vehicle)
def SetSpanCostCoefficientForAllVehicles(self, coefficient: "int64_t") -> "void":
return _pywrapcp.RoutingDimension_SetSpanCostCoefficientForAllVehicles(self, coefficient)
def SetGlobalSpanCostCoefficient(self, coefficient: "int64_t") -> "void":
r"""
Sets a cost proportional to the *global* dimension span, that is the
difference between the largest value of route end cumul variables and
the smallest value of route start cumul variables.
In other words:
global_span_cost =
coefficient * (Max(dimension end value) - Min(dimension start value)).
"""
return _pywrapcp.RoutingDimension_SetGlobalSpanCostCoefficient(self, coefficient)
def SetCumulVarSoftUpperBound(self, index: "int64_t", upper_bound: "int64_t", coefficient: "int64_t") -> "void":
r"""
Sets a soft upper bound to the cumul variable of a given variable index.
If the value of the cumul variable is greater than the bound, a cost
proportional to the difference between this value and the bound is added
to the cost function of the model:
cumulVar <= upper_bound -> cost = 0
cumulVar > upper_bound -> cost = coefficient * (cumulVar - upper_bound)
This is also handy to model tardiness costs when the dimension represents
time.
"""
return _pywrapcp.RoutingDimension_SetCumulVarSoftUpperBound(self, index, upper_bound, coefficient)
def HasCumulVarSoftUpperBound(self, index: "int64_t") -> "bool":
r"""
Returns true if a soft upper bound has been set for a given variable
index.
"""
return _pywrapcp.RoutingDimension_HasCumulVarSoftUpperBound(self, index)
def GetCumulVarSoftUpperBound(self, index: "int64_t") -> "int64_t":
r"""
Returns the soft upper bound of a cumul variable for a given variable
index. The "hard" upper bound of the variable is returned if no soft upper
bound has been set.
"""
return _pywrapcp.RoutingDimension_GetCumulVarSoftUpperBound(self, index)
def GetCumulVarSoftUpperBoundCoefficient(self, index: "int64_t") -> "int64_t":
r"""
Returns the cost coefficient of the soft upper bound of a cumul variable
for a given variable index. If no soft upper bound has been set, 0 is
returned.
"""
return _pywrapcp.RoutingDimension_GetCumulVarSoftUpperBoundCoefficient(self, index)
def SetCumulVarSoftLowerBound(self, index: "int64_t", lower_bound: "int64_t", coefficient: "int64_t") -> "void":
r"""
Sets a soft lower bound to the cumul variable of a given variable index.
If the value of the cumul variable is less than the bound, a cost
proportional to the difference between this value and the bound is added
to the cost function of the model:
cumulVar > lower_bound -> cost = 0
cumulVar <= lower_bound -> cost = coefficient * (lower_bound -
cumulVar).
This is also handy to model earliness costs when the dimension represents
time.
"""
return _pywrapcp.RoutingDimension_SetCumulVarSoftLowerBound(self, index, lower_bound, coefficient)
def HasCumulVarSoftLowerBound(self, index: "int64_t") -> "bool":
r"""
Returns true if a soft lower bound has been set for a given variable
index.
"""
return _pywrapcp.RoutingDimension_HasCumulVarSoftLowerBound(self, index)
def GetCumulVarSoftLowerBound(self, index: "int64_t") -> "int64_t":
r"""
Returns the soft lower bound of a cumul variable for a given variable
index. The "hard" lower bound of the variable is returned if no soft lower
bound has been set.
"""
return _pywrapcp.RoutingDimension_GetCumulVarSoftLowerBound(self, index)
def GetCumulVarSoftLowerBoundCoefficient(self, index: "int64_t") -> "int64_t":
r"""
Returns the cost coefficient of the soft lower bound of a cumul variable
for a given variable index. If no soft lower bound has been set, 0 is
returned.
"""
return _pywrapcp.RoutingDimension_GetCumulVarSoftLowerBoundCoefficient(self, index)
def SetBreakIntervalsOfVehicle(self, breaks: "std::vector< operations_research::IntervalVar * >", vehicle: "int", node_visit_transits: "std::vector< int64_t >") -> "void":
r"""
Sets the breaks for a given vehicle. Breaks are represented by
IntervalVars. They may interrupt transits between nodes and increase
the value of corresponding slack variables.
A break may take place before the start of a vehicle, after the end of
a vehicle, or during a travel i -> j.
In that case, the interval [break.Start(), break.End()) must be a subset
of [CumulVar(i) + pre_travel(i, j), CumulVar(j) - post_travel(i, j)). In
other words, a break may not overlap any node n's visit, given by
[CumulVar(n) - post_travel(_, n), CumulVar(n) + pre_travel(n, _)).
This formula considers post_travel(_, start) and pre_travel(end, _) to be
0; pre_travel will never be called on any (_, start) and post_travel will
never we called on any (end, _). If pre_travel_evaluator or
post_travel_evaluator is -1, it will be taken as a function that always
returns 0.
Deprecated, sets pre_travel(i, j) = node_visit_transit[i].
"""
return _pywrapcp.RoutingDimension_SetBreakIntervalsOfVehicle(self, breaks, vehicle, node_visit_transits)
def SetBreakDistanceDurationOfVehicle(self, distance: "int64_t", duration: "int64_t", vehicle: "int") -> "void":
r"""
With breaks supposed to be consecutive, this forces the distance between
breaks of size at least minimum_break_duration to be at most distance.
This supposes that the time until route start and after route end are
infinite breaks.
"""
return _pywrapcp.RoutingDimension_SetBreakDistanceDurationOfVehicle(self, distance, duration, vehicle)
def InitializeBreaks(self) -> "void":
r"""
Sets up vehicle_break_intervals_, vehicle_break_distance_duration_,
pre_travel_evaluators and post_travel_evaluators.
"""
return _pywrapcp.RoutingDimension_InitializeBreaks(self)
def HasBreakConstraints(self) -> "bool":
r""" Returns true if any break interval or break distance was defined."""
return _pywrapcp.RoutingDimension_HasBreakConstraints(self)
def GetPreTravelEvaluatorOfVehicle(self, vehicle: "int") -> "int":
return _pywrapcp.RoutingDimension_GetPreTravelEvaluatorOfVehicle(self, vehicle)
def GetPostTravelEvaluatorOfVehicle(self, vehicle: "int") -> "int":
return _pywrapcp.RoutingDimension_GetPostTravelEvaluatorOfVehicle(self, vehicle)
def base_dimension(self) -> "operations_research::RoutingDimension const *":
r""" Returns the parent in the dependency tree if any or nullptr otherwise."""
return _pywrapcp.RoutingDimension_base_dimension(self)
def ShortestTransitionSlack(self, node: "int64_t") -> "int64_t":
r"""
It makes sense to use the function only for self-dependent dimension.
For such dimensions the value of the slack of a node determines the
transition cost of the next transit. Provided that
1. cumul[node] is fixed,
2. next[node] and next[next[node]] (if exists) are fixed,
the value of slack[node] for which cumul[next[node]] + transit[next[node]]
is minimized can be found in O(1) using this function.
"""
return _pywrapcp.RoutingDimension_ShortestTransitionSlack(self, node)
def name(self) -> "std::string const &":
r""" Returns the name of the dimension."""
return _pywrapcp.RoutingDimension_name(self)
def SetPickupToDeliveryLimitFunctionForPair(self, limit_function: "operations_research::RoutingDimension::PickupToDeliveryLimitFunction", pair_index: "int") -> "void":
return _pywrapcp.RoutingDimension_SetPickupToDeliveryLimitFunctionForPair(self, limit_function, pair_index)
def HasPickupToDeliveryLimits(self) -> "bool":
return _pywrapcp.RoutingDimension_HasPickupToDeliveryLimits(self)
def AddNodePrecedence(self, first_node: "int64_t", second_node: "int64_t", offset: "int64_t") -> "void":
return _pywrapcp.RoutingDimension_AddNodePrecedence(self, first_node, second_node, offset)
def GetSpanUpperBoundForVehicle(self, vehicle: "int") -> "int64_t":
return _pywrapcp.RoutingDimension_GetSpanUpperBoundForVehicle(self, vehicle)
def GetSpanCostCoefficientForVehicle(self, vehicle: "int") -> "int64_t":
return _pywrapcp.RoutingDimension_GetSpanCostCoefficientForVehicle(self, vehicle)
def global_span_cost_coefficient(self) -> "int64_t":
return _pywrapcp.RoutingDimension_global_span_cost_coefficient(self)
def GetGlobalOptimizerOffset(self) -> "int64_t":
return _pywrapcp.RoutingDimension_GetGlobalOptimizerOffset(self)
def GetLocalOptimizerOffsetForVehicle(self, vehicle: "int") -> "int64_t":
return _pywrapcp.RoutingDimension_GetLocalOptimizerOffsetForVehicle(self, vehicle)
# Register RoutingDimension in _pywrapcp:
_pywrapcp.RoutingDimension_swigregister(RoutingDimension)
def MakeSetValuesFromTargets(solver: "Solver", variables: "std::vector< operations_research::IntVar * >", targets: "std::vector< int64_t >") -> "operations_research::DecisionBuilder *":
r"""
A decision builder which tries to assign values to variables as close as
possible to target values first.
"""
return _pywrapcp.MakeSetValuesFromTargets(solver, variables, targets)
def SolveModelWithSat(model: "RoutingModel", search_parameters: "operations_research::RoutingSearchParameters const &", initial_solution: "Assignment", solution: "Assignment") -> "bool":
r"""
Attempts to solve the model using the cp-sat solver. As of 5/2019, will
solve the TSP corresponding to the model if it has a single vehicle.
Therefore the resulting solution might not actually be feasible. Will return
false if a solution could not be found.
"""
return _pywrapcp.SolveModelWithSat(model, search_parameters, initial_solution, solution)Functions
def DefaultRoutingModelParameters() ‑> operations_research::RoutingModelParameters-
Expand source code
def DefaultRoutingModelParameters() -> "operations_research::RoutingModelParameters": return _pywrapcp.DefaultRoutingModelParameters() def DefaultRoutingSearchParameters() ‑> operations_research::RoutingSearchParameters-
Expand source code
def DefaultRoutingSearchParameters() -> "operations_research::RoutingSearchParameters": return _pywrapcp.DefaultRoutingSearchParameters() def FindErrorInRoutingSearchParameters(search_parameters: operations_research::RoutingSearchParameters const &) ‑> std::string-
Returns an empty std::string if the routing search parameters are valid, and a non-empty, human readable error description if they're not.
Expand source code
def FindErrorInRoutingSearchParameters(search_parameters: "operations_research::RoutingSearchParameters const &") -> "std::string": r""" Returns an empty std::string if the routing search parameters are valid, and a non-empty, human readable error description if they're not. """ return _pywrapcp.FindErrorInRoutingSearchParameters(search_parameters) def MakeSetValuesFromTargets(solver: Solver, variables: std::vector< operations_research::IntVar * >, targets: std::vector< int64_t >) ‑> operations_research::DecisionBuilder *-
A decision builder which tries to assign values to variables as close as possible to target values first.
Expand source code
def MakeSetValuesFromTargets(solver: "Solver", variables: "std::vector< operations_research::IntVar * >", targets: "std::vector< int64_t >") -> "operations_research::DecisionBuilder *": r""" A decision builder which tries to assign values to variables as close as possible to target values first. """ return _pywrapcp.MakeSetValuesFromTargets(solver, variables, targets) def SolveModelWithSat(model: RoutingModel, search_parameters: operations_research::RoutingSearchParameters const &, initial_solution: Assignment, solution: Assignment) ‑> bool-
Attempts to solve the model using the cp-sat solver. As of 5/2019, will solve the TSP corresponding to the model if it has a single vehicle. Therefore the resulting solution might not actually be feasible. Will return false if a solution could not be found.
Expand source code
def SolveModelWithSat(model: "RoutingModel", search_parameters: "operations_research::RoutingSearchParameters const &", initial_solution: "Assignment", solution: "Assignment") -> "bool": r""" Attempts to solve the model using the cp-sat solver. As of 5/2019, will solve the TSP corresponding to the model if it has a single vehicle. Therefore the resulting solution might not actually be feasible. Will return false if a solution could not be found. """ return _pywrapcp.SolveModelWithSat(model, search_parameters, initial_solution, solution) def Solver_DefaultSolverParameters() ‑> operations_research::ConstraintSolverParameters-
Create a ConstraintSolverParameters proto with all the default values.
Expand source code
def Solver_DefaultSolverParameters() -> "operations_research::ConstraintSolverParameters": r""" Create a ConstraintSolverParameters proto with all the default values.""" return _pywrapcp.Solver_DefaultSolverParameters() def Solver_MemoryUsage() ‑> int64_t-
Current memory usage in bytes
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def Solver_MemoryUsage() -> "int64_t": r""" Current memory usage in bytes""" return _pywrapcp.Solver_MemoryUsage()
Classes
class Assignment (*args, **kwargs)-
An Assignment is a variable -> domains mapping, used to report solutions to the user.
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class Assignment(PropagationBaseObject): r""" An Assignment is a variable -> domains mapping, used to report solutions to the user. """ thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined") __repr__ = _swig_repr def Clear(self) -> "void": return _pywrapcp.Assignment_Clear(self) def Empty(self) -> "bool": return _pywrapcp.Assignment_Empty(self) def Size(self) -> "int": return _pywrapcp.Assignment_Size(self) def NumIntVars(self) -> "int": return _pywrapcp.Assignment_NumIntVars(self) def NumIntervalVars(self) -> "int": return _pywrapcp.Assignment_NumIntervalVars(self) def NumSequenceVars(self) -> "int": return _pywrapcp.Assignment_NumSequenceVars(self) def Store(self) -> "void": return _pywrapcp.Assignment_Store(self) def Restore(self) -> "void": return _pywrapcp.Assignment_Restore(self) def Load(self, *args) -> "void": return _pywrapcp.Assignment_Load(self, *args) def Save(self, *args) -> "void": return _pywrapcp.Assignment_Save(self, *args) def AddObjective(self, v: "IntVar") -> "void": return _pywrapcp.Assignment_AddObjective(self, v) def Objective(self) -> "operations_research::IntVar *": return _pywrapcp.Assignment_Objective(self) def HasObjective(self) -> "bool": return _pywrapcp.Assignment_HasObjective(self) def ObjectiveMin(self) -> "int64_t": return _pywrapcp.Assignment_ObjectiveMin(self) def ObjectiveMax(self) -> "int64_t": return _pywrapcp.Assignment_ObjectiveMax(self) def ObjectiveValue(self) -> "int64_t": return _pywrapcp.Assignment_ObjectiveValue(self) def ObjectiveBound(self) -> "bool": return _pywrapcp.Assignment_ObjectiveBound(self) def SetObjectiveMin(self, m: "int64_t") -> "void": return _pywrapcp.Assignment_SetObjectiveMin(self, m) def SetObjectiveMax(self, m: "int64_t") -> "void": return _pywrapcp.Assignment_SetObjectiveMax(self, m) def SetObjectiveValue(self, value: "int64_t") -> "void": return _pywrapcp.Assignment_SetObjectiveValue(self, value) def SetObjectiveRange(self, l: "int64_t", u: "int64_t") -> "void": return _pywrapcp.Assignment_SetObjectiveRange(self, l, u) def Min(self, var: "IntVar") -> "int64_t": return _pywrapcp.Assignment_Min(self, var) def Max(self, var: "IntVar") -> "int64_t": return _pywrapcp.Assignment_Max(self, var) def Value(self, var: "IntVar") -> "int64_t": return _pywrapcp.Assignment_Value(self, var) def Bound(self, var: "IntVar") -> "bool": return _pywrapcp.Assignment_Bound(self, var) def SetMin(self, var: "IntVar", m: "int64_t") -> "void": return _pywrapcp.Assignment_SetMin(self, var, m) def SetMax(self, var: "IntVar", m: "int64_t") -> "void": return _pywrapcp.Assignment_SetMax(self, var, m) def SetRange(self, var: "IntVar", l: "int64_t", u: "int64_t") -> "void": return _pywrapcp.Assignment_SetRange(self, var, l, u) def SetValue(self, var: "IntVar", value: "int64_t") -> "void": return _pywrapcp.Assignment_SetValue(self, var, value) def StartMin(self, var: "IntervalVar") -> "int64_t": return _pywrapcp.Assignment_StartMin(self, var) def StartMax(self, var: "IntervalVar") -> "int64_t": return _pywrapcp.Assignment_StartMax(self, var) def StartValue(self, var: "IntervalVar") -> "int64_t": return _pywrapcp.Assignment_StartValue(self, var) def DurationMin(self, var: "IntervalVar") -> "int64_t": return _pywrapcp.Assignment_DurationMin(self, var) def DurationMax(self, var: "IntervalVar") -> "int64_t": return _pywrapcp.Assignment_DurationMax(self, var) def DurationValue(self, var: "IntervalVar") -> "int64_t": return _pywrapcp.Assignment_DurationValue(self, var) def EndMin(self, var: "IntervalVar") -> "int64_t": return _pywrapcp.Assignment_EndMin(self, var) def EndMax(self, var: "IntervalVar") -> "int64_t": return _pywrapcp.Assignment_EndMax(self, var) def EndValue(self, var: "IntervalVar") -> "int64_t": return _pywrapcp.Assignment_EndValue(self, var) def PerformedMin(self, var: "IntervalVar") -> "int64_t": return _pywrapcp.Assignment_PerformedMin(self, var) def PerformedMax(self, var: "IntervalVar") -> "int64_t": return _pywrapcp.Assignment_PerformedMax(self, var) def PerformedValue(self, var: "IntervalVar") -> "int64_t": return _pywrapcp.Assignment_PerformedValue(self, var) def SetStartMin(self, var: "IntervalVar", m: "int64_t") -> "void": return _pywrapcp.Assignment_SetStartMin(self, var, m) def SetStartMax(self, var: "IntervalVar", m: "int64_t") -> "void": return _pywrapcp.Assignment_SetStartMax(self, var, m) def SetStartRange(self, var: "IntervalVar", mi: "int64_t", ma: "int64_t") -> "void": return _pywrapcp.Assignment_SetStartRange(self, var, mi, ma) def SetStartValue(self, var: "IntervalVar", value: "int64_t") -> "void": return _pywrapcp.Assignment_SetStartValue(self, var, value) def SetDurationMin(self, var: "IntervalVar", m: "int64_t") -> "void": return _pywrapcp.Assignment_SetDurationMin(self, var, m) def SetDurationMax(self, var: "IntervalVar", m: "int64_t") -> "void": return _pywrapcp.Assignment_SetDurationMax(self, var, m) def SetDurationRange(self, var: "IntervalVar", mi: "int64_t", ma: "int64_t") -> "void": return _pywrapcp.Assignment_SetDurationRange(self, var, mi, ma) def SetDurationValue(self, var: "IntervalVar", value: "int64_t") -> "void": return _pywrapcp.Assignment_SetDurationValue(self, var, value) def SetEndMin(self, var: "IntervalVar", m: "int64_t") -> "void": return _pywrapcp.Assignment_SetEndMin(self, var, m) def SetEndMax(self, var: "IntervalVar", m: "int64_t") -> "void": return _pywrapcp.Assignment_SetEndMax(self, var, m) def SetEndRange(self, var: "IntervalVar", mi: "int64_t", ma: "int64_t") -> "void": return _pywrapcp.Assignment_SetEndRange(self, var, mi, ma) def SetEndValue(self, var: "IntervalVar", value: "int64_t") -> "void": return _pywrapcp.Assignment_SetEndValue(self, var, value) def SetPerformedMin(self, var: "IntervalVar", m: "int64_t") -> "void": return _pywrapcp.Assignment_SetPerformedMin(self, var, m) def SetPerformedMax(self, var: "IntervalVar", m: "int64_t") -> "void": return _pywrapcp.Assignment_SetPerformedMax(self, var, m) def SetPerformedRange(self, var: "IntervalVar", mi: "int64_t", ma: "int64_t") -> "void": return _pywrapcp.Assignment_SetPerformedRange(self, var, mi, ma) def SetPerformedValue(self, var: "IntervalVar", value: "int64_t") -> "void": return _pywrapcp.Assignment_SetPerformedValue(self, var, value) def Add(self, *args) -> "void": return _pywrapcp.Assignment_Add(self, *args) def ForwardSequence(self, var: "SequenceVar") -> "std::vector< int > const &": return _pywrapcp.Assignment_ForwardSequence(self, var) def BackwardSequence(self, var: "SequenceVar") -> "std::vector< int > const &": return _pywrapcp.Assignment_BackwardSequence(self, var) def Unperformed(self, var: "SequenceVar") -> "std::vector< int > const &": return _pywrapcp.Assignment_Unperformed(self, var) def SetSequence(self, var: "SequenceVar", forward_sequence: "std::vector< int > const &", backward_sequence: "std::vector< int > const &", unperformed: "std::vector< int > const &") -> "void": return _pywrapcp.Assignment_SetSequence(self, var, forward_sequence, backward_sequence, unperformed) def SetForwardSequence(self, var: "SequenceVar", forward_sequence: "std::vector< int > const &") -> "void": return _pywrapcp.Assignment_SetForwardSequence(self, var, forward_sequence) def SetBackwardSequence(self, var: "SequenceVar", backward_sequence: "std::vector< int > const &") -> "void": return _pywrapcp.Assignment_SetBackwardSequence(self, var, backward_sequence) def SetUnperformed(self, var: "SequenceVar", unperformed: "std::vector< int > const &") -> "void": return _pywrapcp.Assignment_SetUnperformed(self, var, unperformed) def Activate(self, *args) -> "void": return _pywrapcp.Assignment_Activate(self, *args) def Deactivate(self, *args) -> "void": return _pywrapcp.Assignment_Deactivate(self, *args) def Activated(self, *args) -> "bool": return _pywrapcp.Assignment_Activated(self, *args) def DebugString(self) -> "std::string": return _pywrapcp.Assignment_DebugString(self) def IntVarContainer(self) -> "operations_research::Assignment::IntContainer const &": return _pywrapcp.Assignment_IntVarContainer(self) def MutableIntVarContainer(self) -> "operations_research::Assignment::IntContainer *": return _pywrapcp.Assignment_MutableIntVarContainer(self) def IntervalVarContainer(self) -> "operations_research::Assignment::IntervalContainer const &": return _pywrapcp.Assignment_IntervalVarContainer(self) def MutableIntervalVarContainer(self) -> "operations_research::Assignment::IntervalContainer *": return _pywrapcp.Assignment_MutableIntervalVarContainer(self) def SequenceVarContainer(self) -> "operations_research::Assignment::SequenceContainer const &": return _pywrapcp.Assignment_SequenceVarContainer(self) def MutableSequenceVarContainer(self) -> "operations_research::Assignment::SequenceContainer *": return _pywrapcp.Assignment_MutableSequenceVarContainer(self) def __eq__(self, assignment: "Assignment") -> "bool": return _pywrapcp.Assignment___eq__(self, assignment) def __ne__(self, assignment: "Assignment") -> "bool": return _pywrapcp.Assignment___ne__(self, assignment)Ancestors
Methods
def Activate(self, *args) ‑> void-
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def Activate(self, *args) -> "void": return _pywrapcp.Assignment_Activate(self, *args) def Activated(self, *args) ‑> bool-
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def Activated(self, *args) -> "bool": return _pywrapcp.Assignment_Activated(self, *args) def Add(self, *args) ‑> void-
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def Add(self, *args) -> "void": return _pywrapcp.Assignment_Add(self, *args) def AddObjective(self, v: IntVar) ‑> void-
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def AddObjective(self, v: "IntVar") -> "void": return _pywrapcp.Assignment_AddObjective(self, v) def BackwardSequence(self, var: SequenceVar) ‑> std::vector< int > const &-
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def BackwardSequence(self, var: "SequenceVar") -> "std::vector< int > const &": return _pywrapcp.Assignment_BackwardSequence(self, var) def Bound(self, var: IntVar) ‑> bool-
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def Bound(self, var: "IntVar") -> "bool": return _pywrapcp.Assignment_Bound(self, var) def Clear(self) ‑> void-
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def Clear(self) -> "void": return _pywrapcp.Assignment_Clear(self) def Deactivate(self, *args) ‑> void-
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def Deactivate(self, *args) -> "void": return _pywrapcp.Assignment_Deactivate(self, *args) def DebugString(self) ‑> std::string-
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def DebugString(self) -> "std::string": return _pywrapcp.Assignment_DebugString(self) def DurationMax(self, var: IntervalVar) ‑> int64_t-
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def DurationMax(self, var: "IntervalVar") -> "int64_t": return _pywrapcp.Assignment_DurationMax(self, var) def DurationMin(self, var: IntervalVar) ‑> int64_t-
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def DurationMin(self, var: "IntervalVar") -> "int64_t": return _pywrapcp.Assignment_DurationMin(self, var) def DurationValue(self, var: IntervalVar) ‑> int64_t-
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def DurationValue(self, var: "IntervalVar") -> "int64_t": return _pywrapcp.Assignment_DurationValue(self, var) def Empty(self) ‑> bool-
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def Empty(self) -> "bool": return _pywrapcp.Assignment_Empty(self) def EndMax(self, var: IntervalVar) ‑> int64_t-
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def EndMax(self, var: "IntervalVar") -> "int64_t": return _pywrapcp.Assignment_EndMax(self, var) def EndMin(self, var: IntervalVar) ‑> int64_t-
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def EndMin(self, var: "IntervalVar") -> "int64_t": return _pywrapcp.Assignment_EndMin(self, var) def EndValue(self, var: IntervalVar) ‑> int64_t-
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def EndValue(self, var: "IntervalVar") -> "int64_t": return _pywrapcp.Assignment_EndValue(self, var) def ForwardSequence(self, var: SequenceVar) ‑> std::vector< int > const &-
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def ForwardSequence(self, var: "SequenceVar") -> "std::vector< int > const &": return _pywrapcp.Assignment_ForwardSequence(self, var) def HasObjective(self) ‑> bool-
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def HasObjective(self) -> "bool": return _pywrapcp.Assignment_HasObjective(self) def IntVarContainer(self) ‑> operations_research::Assignment::IntContainer const &-
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def IntVarContainer(self) -> "operations_research::Assignment::IntContainer const &": return _pywrapcp.Assignment_IntVarContainer(self) def IntervalVarContainer(self) ‑> operations_research::Assignment::IntervalContainer const &-
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def IntervalVarContainer(self) -> "operations_research::Assignment::IntervalContainer const &": return _pywrapcp.Assignment_IntervalVarContainer(self) def Load(self, *args) ‑> void-
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def Load(self, *args) -> "void": return _pywrapcp.Assignment_Load(self, *args) def Max(self, var: IntVar) ‑> int64_t-
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def Max(self, var: "IntVar") -> "int64_t": return _pywrapcp.Assignment_Max(self, var) def Min(self, var: IntVar) ‑> int64_t-
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def Min(self, var: "IntVar") -> "int64_t": return _pywrapcp.Assignment_Min(self, var) def MutableIntVarContainer(self) ‑> operations_research::Assignment::IntContainer *-
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def MutableIntVarContainer(self) -> "operations_research::Assignment::IntContainer *": return _pywrapcp.Assignment_MutableIntVarContainer(self) def MutableIntervalVarContainer(self) ‑> operations_research::Assignment::IntervalContainer *-
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def MutableIntervalVarContainer(self) -> "operations_research::Assignment::IntervalContainer *": return _pywrapcp.Assignment_MutableIntervalVarContainer(self) def MutableSequenceVarContainer(self) ‑> operations_research::Assignment::SequenceContainer *-
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def MutableSequenceVarContainer(self) -> "operations_research::Assignment::SequenceContainer *": return _pywrapcp.Assignment_MutableSequenceVarContainer(self) def NumIntVars(self) ‑> int-
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def NumIntVars(self) -> "int": return _pywrapcp.Assignment_NumIntVars(self) def NumIntervalVars(self) ‑> int-
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def NumIntervalVars(self) -> "int": return _pywrapcp.Assignment_NumIntervalVars(self) def NumSequenceVars(self) ‑> int-
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def NumSequenceVars(self) -> "int": return _pywrapcp.Assignment_NumSequenceVars(self) def Objective(self) ‑> operations_research::IntVar *-
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def Objective(self) -> "operations_research::IntVar *": return _pywrapcp.Assignment_Objective(self) def ObjectiveBound(self) ‑> bool-
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def ObjectiveBound(self) -> "bool": return _pywrapcp.Assignment_ObjectiveBound(self) def ObjectiveMax(self) ‑> int64_t-
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def ObjectiveMax(self) -> "int64_t": return _pywrapcp.Assignment_ObjectiveMax(self) def ObjectiveMin(self) ‑> int64_t-
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def ObjectiveMin(self) -> "int64_t": return _pywrapcp.Assignment_ObjectiveMin(self) def ObjectiveValue(self) ‑> int64_t-
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def ObjectiveValue(self) -> "int64_t": return _pywrapcp.Assignment_ObjectiveValue(self) def PerformedMax(self, var: IntervalVar) ‑> int64_t-
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def PerformedMax(self, var: "IntervalVar") -> "int64_t": return _pywrapcp.Assignment_PerformedMax(self, var) def PerformedMin(self, var: IntervalVar) ‑> int64_t-
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def PerformedMin(self, var: "IntervalVar") -> "int64_t": return _pywrapcp.Assignment_PerformedMin(self, var) def PerformedValue(self, var: IntervalVar) ‑> int64_t-
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def PerformedValue(self, var: "IntervalVar") -> "int64_t": return _pywrapcp.Assignment_PerformedValue(self, var) def Restore(self) ‑> void-
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def Restore(self) -> "void": return _pywrapcp.Assignment_Restore(self) def Save(self, *args) ‑> void-
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def Save(self, *args) -> "void": return _pywrapcp.Assignment_Save(self, *args) def SequenceVarContainer(self) ‑> operations_research::Assignment::SequenceContainer const &-
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def SequenceVarContainer(self) -> "operations_research::Assignment::SequenceContainer const &": return _pywrapcp.Assignment_SequenceVarContainer(self) def SetBackwardSequence(self, var: SequenceVar, backward_sequence: std::vector< int > const &) ‑> void-
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def SetBackwardSequence(self, var: "SequenceVar", backward_sequence: "std::vector< int > const &") -> "void": return _pywrapcp.Assignment_SetBackwardSequence(self, var, backward_sequence) def SetDurationMax(self, var: IntervalVar, m: int64_t) ‑> void-
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def SetDurationMax(self, var: "IntervalVar", m: "int64_t") -> "void": return _pywrapcp.Assignment_SetDurationMax(self, var, m) def SetDurationMin(self, var: IntervalVar, m: int64_t) ‑> void-
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def SetDurationMin(self, var: "IntervalVar", m: "int64_t") -> "void": return _pywrapcp.Assignment_SetDurationMin(self, var, m) def SetDurationRange(self, var: IntervalVar, mi: int64_t, ma: int64_t) ‑> void-
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def SetDurationRange(self, var: "IntervalVar", mi: "int64_t", ma: "int64_t") -> "void": return _pywrapcp.Assignment_SetDurationRange(self, var, mi, ma) def SetDurationValue(self, var: IntervalVar, value: int64_t) ‑> void-
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def SetDurationValue(self, var: "IntervalVar", value: "int64_t") -> "void": return _pywrapcp.Assignment_SetDurationValue(self, var, value) def SetEndMax(self, var: IntervalVar, m: int64_t) ‑> void-
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def SetEndMax(self, var: "IntervalVar", m: "int64_t") -> "void": return _pywrapcp.Assignment_SetEndMax(self, var, m) def SetEndMin(self, var: IntervalVar, m: int64_t) ‑> void-
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def SetEndMin(self, var: "IntervalVar", m: "int64_t") -> "void": return _pywrapcp.Assignment_SetEndMin(self, var, m) def SetEndRange(self, var: IntervalVar, mi: int64_t, ma: int64_t) ‑> void-
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def SetEndRange(self, var: "IntervalVar", mi: "int64_t", ma: "int64_t") -> "void": return _pywrapcp.Assignment_SetEndRange(self, var, mi, ma) def SetEndValue(self, var: IntervalVar, value: int64_t) ‑> void-
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def SetEndValue(self, var: "IntervalVar", value: "int64_t") -> "void": return _pywrapcp.Assignment_SetEndValue(self, var, value) def SetForwardSequence(self, var: SequenceVar, forward_sequence: std::vector< int > const &) ‑> void-
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def SetForwardSequence(self, var: "SequenceVar", forward_sequence: "std::vector< int > const &") -> "void": return _pywrapcp.Assignment_SetForwardSequence(self, var, forward_sequence) def SetMax(self, var: IntVar, m: int64_t) ‑> void-
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def SetMax(self, var: "IntVar", m: "int64_t") -> "void": return _pywrapcp.Assignment_SetMax(self, var, m) def SetMin(self, var: IntVar, m: int64_t) ‑> void-
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def SetMin(self, var: "IntVar", m: "int64_t") -> "void": return _pywrapcp.Assignment_SetMin(self, var, m) def SetObjectiveMax(self, m: int64_t) ‑> void-
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def SetObjectiveMax(self, m: "int64_t") -> "void": return _pywrapcp.Assignment_SetObjectiveMax(self, m) def SetObjectiveMin(self, m: int64_t) ‑> void-
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def SetObjectiveMin(self, m: "int64_t") -> "void": return _pywrapcp.Assignment_SetObjectiveMin(self, m) def SetObjectiveRange(self, l: int64_t, u: int64_t) ‑> void-
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def SetObjectiveRange(self, l: "int64_t", u: "int64_t") -> "void": return _pywrapcp.Assignment_SetObjectiveRange(self, l, u) def SetObjectiveValue(self, value: int64_t) ‑> void-
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def SetObjectiveValue(self, value: "int64_t") -> "void": return _pywrapcp.Assignment_SetObjectiveValue(self, value) def SetPerformedMax(self, var: IntervalVar, m: int64_t) ‑> void-
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def SetPerformedMax(self, var: "IntervalVar", m: "int64_t") -> "void": return _pywrapcp.Assignment_SetPerformedMax(self, var, m) def SetPerformedMin(self, var: IntervalVar, m: int64_t) ‑> void-
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def SetPerformedMin(self, var: "IntervalVar", m: "int64_t") -> "void": return _pywrapcp.Assignment_SetPerformedMin(self, var, m) def SetPerformedRange(self, var: IntervalVar, mi: int64_t, ma: int64_t) ‑> void-
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def SetPerformedRange(self, var: "IntervalVar", mi: "int64_t", ma: "int64_t") -> "void": return _pywrapcp.Assignment_SetPerformedRange(self, var, mi, ma) def SetPerformedValue(self, var: IntervalVar, value: int64_t) ‑> void-
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def SetPerformedValue(self, var: "IntervalVar", value: "int64_t") -> "void": return _pywrapcp.Assignment_SetPerformedValue(self, var, value) def SetRange(self, var: IntVar, l: int64_t, u: int64_t) ‑> void-
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def SetRange(self, var: "IntVar", l: "int64_t", u: "int64_t") -> "void": return _pywrapcp.Assignment_SetRange(self, var, l, u) def SetSequence(self, var: SequenceVar, forward_sequence: std::vector< int > const &, backward_sequence: std::vector< int > const &, unperformed: std::vector< int > const &) ‑> void-
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def SetSequence(self, var: "SequenceVar", forward_sequence: "std::vector< int > const &", backward_sequence: "std::vector< int > const &", unperformed: "std::vector< int > const &") -> "void": return _pywrapcp.Assignment_SetSequence(self, var, forward_sequence, backward_sequence, unperformed) def SetStartMax(self, var: IntervalVar, m: int64_t) ‑> void-
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def SetStartMax(self, var: "IntervalVar", m: "int64_t") -> "void": return _pywrapcp.Assignment_SetStartMax(self, var, m) def SetStartMin(self, var: IntervalVar, m: int64_t) ‑> void-
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def SetStartMin(self, var: "IntervalVar", m: "int64_t") -> "void": return _pywrapcp.Assignment_SetStartMin(self, var, m) def SetStartRange(self, var: IntervalVar, mi: int64_t, ma: int64_t) ‑> void-
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def SetStartRange(self, var: "IntervalVar", mi: "int64_t", ma: "int64_t") -> "void": return _pywrapcp.Assignment_SetStartRange(self, var, mi, ma) def SetStartValue(self, var: IntervalVar, value: int64_t) ‑> void-
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def SetStartValue(self, var: "IntervalVar", value: "int64_t") -> "void": return _pywrapcp.Assignment_SetStartValue(self, var, value) def SetUnperformed(self, var: SequenceVar, unperformed: std::vector< int > const &) ‑> void-
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def SetUnperformed(self, var: "SequenceVar", unperformed: "std::vector< int > const &") -> "void": return _pywrapcp.Assignment_SetUnperformed(self, var, unperformed) def SetValue(self, var: IntVar, value: int64_t) ‑> void-
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def SetValue(self, var: "IntVar", value: "int64_t") -> "void": return _pywrapcp.Assignment_SetValue(self, var, value) def Size(self) ‑> int-
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def Size(self) -> "int": return _pywrapcp.Assignment_Size(self) def StartMax(self, var: IntervalVar) ‑> int64_t-
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def StartMax(self, var: "IntervalVar") -> "int64_t": return _pywrapcp.Assignment_StartMax(self, var) def StartMin(self, var: IntervalVar) ‑> int64_t-
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def StartMin(self, var: "IntervalVar") -> "int64_t": return _pywrapcp.Assignment_StartMin(self, var) def StartValue(self, var: IntervalVar) ‑> int64_t-
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def StartValue(self, var: "IntervalVar") -> "int64_t": return _pywrapcp.Assignment_StartValue(self, var) def Store(self) ‑> void-
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def Store(self) -> "void": return _pywrapcp.Assignment_Store(self) def Unperformed(self, var: SequenceVar) ‑> std::vector< int > const &-
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def Unperformed(self, var: "SequenceVar") -> "std::vector< int > const &": return _pywrapcp.Assignment_Unperformed(self, var) def Value(self, var: IntVar) ‑> int64_t-
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def Value(self, var: "IntVar") -> "int64_t": return _pywrapcp.Assignment_Value(self, var)
Inherited members
class AssignmentElement (*args, **kwargs)-
Expand source code
class AssignmentElement(object): thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined") __repr__ = _swig_repr def Activate(self) -> "void": return _pywrapcp.AssignmentElement_Activate(self) def Deactivate(self) -> "void": return _pywrapcp.AssignmentElement_Deactivate(self) def Activated(self) -> "bool": return _pywrapcp.AssignmentElement_Activated(self) __swig_destroy__ = _pywrapcp.delete_AssignmentElementSubclasses
Instance variables
var thisown-
The membership flag
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thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
Methods
def Activate(self) ‑> void-
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def Activate(self) -> "void": return _pywrapcp.AssignmentElement_Activate(self) def Activated(self) ‑> bool-
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def Activated(self) -> "bool": return _pywrapcp.AssignmentElement_Activated(self) def Deactivate(self) ‑> void-
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def Deactivate(self) -> "void": return _pywrapcp.AssignmentElement_Deactivate(self)
class BaseLns (vars: std::vector< operations_research::IntVar * > const &)-
This is the base class for building an Lns operator. An Lns fragment is a collection of variables which will be relaxed. Fragments are built with NextFragment(), which returns false if there are no more fragments to build. Optionally one can override InitFragments, which is called from LocalSearchOperator::Start to initialize fragment data.
Here's a sample relaxing one variable at a time:
class OneVarLns : public BaseLns { public: OneVarLns(const std::vector
private: int index_; };
Expand source code
class BaseLns(IntVarLocalSearchOperator): r""" This is the base class for building an Lns operator. An Lns fragment is a collection of variables which will be relaxed. Fragments are built with NextFragment(), which returns false if there are no more fragments to build. Optionally one can override InitFragments, which is called from LocalSearchOperator::Start to initialize fragment data. Here's a sample relaxing one variable at a time: class OneVarLns : public BaseLns { public: OneVarLns(const std::vector<IntVar*>& vars) : BaseLns(vars), index_(0) {} virtual ~OneVarLns() {} virtual void InitFragments() { index_ = 0; } virtual bool NextFragment() { const int size = Size(); if (index_ < size) { AppendToFragment(index_); ++index_; return true; } else { return false; } } private: int index_; }; """ thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") __repr__ = _swig_repr def __init__(self, vars: "std::vector< operations_research::IntVar * > const &"): if self.__class__ == BaseLns: _self = None else: _self = self _pywrapcp.BaseLns_swiginit(self, _pywrapcp.new_BaseLns(_self, vars)) __swig_destroy__ = _pywrapcp.delete_BaseLns def InitFragments(self) -> "void": return _pywrapcp.BaseLns_InitFragments(self) def NextFragment(self) -> "bool": return _pywrapcp.BaseLns_NextFragment(self) def AppendToFragment(self, index: "int") -> "void": return _pywrapcp.BaseLns_AppendToFragment(self, index) def FragmentSize(self) -> "int": return _pywrapcp.BaseLns_FragmentSize(self) def __getitem__(self, index: "int") -> "int64_t": return _pywrapcp.BaseLns___getitem__(self, index) def __len__(self) -> "int": return _pywrapcp.BaseLns___len__(self) def __disown__(self): self.this.disown() _pywrapcp.disown_BaseLns(self) return weakref.proxy(self)Ancestors
Methods
def AppendToFragment(self, index: int) ‑> void-
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def AppendToFragment(self, index: "int") -> "void": return _pywrapcp.BaseLns_AppendToFragment(self, index) def FragmentSize(self) ‑> int-
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def FragmentSize(self) -> "int": return _pywrapcp.BaseLns_FragmentSize(self) def InitFragments(self) ‑> void-
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def InitFragments(self) -> "void": return _pywrapcp.BaseLns_InitFragments(self) def NextFragment(self) ‑> bool-
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def NextFragment(self) -> "bool": return _pywrapcp.BaseLns_NextFragment(self)
Inherited members
class BaseObject-
A BaseObject is the root of all reversibly allocated objects. A DebugString method and the associated << operator are implemented as a convenience.
Expand source code
class BaseObject(object): r""" A BaseObject is the root of all reversibly allocated objects. A DebugString method and the associated << operator are implemented as a convenience. """ thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") def __init__(self): if self.__class__ == BaseObject: _self = None else: _self = self _pywrapcp.BaseObject_swiginit(self, _pywrapcp.new_BaseObject(_self, )) __swig_destroy__ = _pywrapcp.delete_BaseObject def DebugString(self) -> "std::string": return _pywrapcp.BaseObject_DebugString(self) def __str__(self) -> "std::string": return _pywrapcp.BaseObject___str__(self) def __repr__(self) -> "std::string": return _pywrapcp.BaseObject___repr__(self) def __disown__(self): self.this.disown() _pywrapcp.disown_BaseObject(self) return weakref.proxy(self)Subclasses
- Decision
- DecisionBuilder
- Demon
- IntVarIterator
- LocalSearchFilter
- LocalSearchFilterManager
- LocalSearchOperator
- PropagationBaseObject
- RoutingModelVisitor
- SearchMonitor
Instance variables
var thisown-
The membership flag
Expand source code
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
Methods
def DebugString(self) ‑> std::string-
Expand source code
def DebugString(self) -> "std::string": return _pywrapcp.BaseObject_DebugString(self)
class BooleanVar (*args, **kwargs)-
The class IntVar is a subset of IntExpr. In addition to the IntExpr protocol, it offers persistence, removing values from the domains, and a finer model for events.
Expand source code
class BooleanVar(IntVar): thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined - class is abstract") __repr__ = _swig_repr def Min(self) -> "int64_t": return _pywrapcp.BooleanVar_Min(self) def SetMin(self, m: "int64_t") -> "void": return _pywrapcp.BooleanVar_SetMin(self, m) def Max(self) -> "int64_t": return _pywrapcp.BooleanVar_Max(self) def SetMax(self, m: "int64_t") -> "void": return _pywrapcp.BooleanVar_SetMax(self, m) def SetRange(self, mi: "int64_t", ma: "int64_t") -> "void": return _pywrapcp.BooleanVar_SetRange(self, mi, ma) def Bound(self) -> "bool": return _pywrapcp.BooleanVar_Bound(self) def Value(self) -> "int64_t": return _pywrapcp.BooleanVar_Value(self) def RemoveValue(self, v: "int64_t") -> "void": return _pywrapcp.BooleanVar_RemoveValue(self, v) def RemoveInterval(self, l: "int64_t", u: "int64_t") -> "void": return _pywrapcp.BooleanVar_RemoveInterval(self, l, u) def WhenBound(self, d: "Demon") -> "void": return _pywrapcp.BooleanVar_WhenBound(self, d) def WhenRange(self, d: "Demon") -> "void": return _pywrapcp.BooleanVar_WhenRange(self, d) def WhenDomain(self, d: "Demon") -> "void": return _pywrapcp.BooleanVar_WhenDomain(self, d) def Size(self) -> "uint64_t": return _pywrapcp.BooleanVar_Size(self) def Contains(self, v: "int64_t") -> "bool": return _pywrapcp.BooleanVar_Contains(self, v) def HoleIteratorAux(self, reversible: "bool") -> "operations_research::IntVarIterator *": return _pywrapcp.BooleanVar_HoleIteratorAux(self, reversible) def DomainIteratorAux(self, reversible: "bool") -> "operations_research::IntVarIterator *": return _pywrapcp.BooleanVar_DomainIteratorAux(self, reversible) def DebugString(self) -> "std::string": return _pywrapcp.BooleanVar_DebugString(self)Ancestors
Methods
def DebugString(self) ‑> std::string-
Expand source code
def DebugString(self) -> "std::string": return _pywrapcp.BooleanVar_DebugString(self) def Max(self) ‑> int64_t-
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def Max(self) -> "int64_t": return _pywrapcp.BooleanVar_Max(self) def Min(self) ‑> int64_t-
Expand source code
def Min(self) -> "int64_t": return _pywrapcp.BooleanVar_Min(self) def SetMax(self, m: int64_t) ‑> void-
Expand source code
def SetMax(self, m: "int64_t") -> "void": return _pywrapcp.BooleanVar_SetMax(self, m) def SetMin(self, m: int64_t) ‑> void-
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def SetMin(self, m: "int64_t") -> "void": return _pywrapcp.BooleanVar_SetMin(self, m)
Inherited members
class ChangeValue (vars: std::vector< operations_research::IntVar * > const &)-
Defines operators which change the value of variables; each neighbor corresponds to one modified variable. Sub-classes have to define ModifyValue which determines what the new variable value is going to be (given the current value and the variable).
Expand source code
class ChangeValue(IntVarLocalSearchOperator): r""" Defines operators which change the value of variables; each neighbor corresponds to *one* modified variable. Sub-classes have to define ModifyValue which determines what the new variable value is going to be (given the current value and the variable). """ thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") __repr__ = _swig_repr def __init__(self, vars: "std::vector< operations_research::IntVar * > const &"): if self.__class__ == ChangeValue: _self = None else: _self = self _pywrapcp.ChangeValue_swiginit(self, _pywrapcp.new_ChangeValue(_self, vars)) __swig_destroy__ = _pywrapcp.delete_ChangeValue def ModifyValue(self, index: "int64_t", value: "int64_t") -> "int64_t": return _pywrapcp.ChangeValue_ModifyValue(self, index, value) def OneNeighbor(self) -> "bool": r""" This method should not be overridden. Override ModifyValue() instead.""" return _pywrapcp.ChangeValue_OneNeighbor(self) def __disown__(self): self.this.disown() _pywrapcp.disown_ChangeValue(self) return weakref.proxy(self)Ancestors
Methods
def ModifyValue(self, index: int64_t, value: int64_t) ‑> int64_t-
Expand source code
def ModifyValue(self, index: "int64_t", value: "int64_t") -> "int64_t": return _pywrapcp.ChangeValue_ModifyValue(self, index, value) def OneNeighbor(self) ‑> bool-
This method should not be overridden. Override ModifyValue() instead.
Expand source code
def OneNeighbor(self) -> "bool": r""" This method should not be overridden. Override ModifyValue() instead.""" return _pywrapcp.ChangeValue_OneNeighbor(self)
Inherited members
class Constraint (solver:Â Solver)-
A constraint is the main modeling object. It provides two methods: - Post() is responsible for creating the demons and attaching them to immediate demons(). - InitialPropagate() is called once just after Post and performs the initial propagation. The subsequent propagations will be performed by the demons Posted during the post() method.
Expand source code
class Constraint(PropagationBaseObject): r""" A constraint is the main modeling object. It provides two methods: - Post() is responsible for creating the demons and attaching them to immediate demons(). - InitialPropagate() is called once just after Post and performs the initial propagation. The subsequent propagations will be performed by the demons Posted during the post() method. """ thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") def __init__(self, solver: "Solver"): if self.__class__ == Constraint: _self = None else: _self = self _pywrapcp.Constraint_swiginit(self, _pywrapcp.new_Constraint(_self, solver)) __swig_destroy__ = _pywrapcp.delete_Constraint def Post(self) -> "void": r""" This method is called when the constraint is processed by the solver. Its main usage is to attach demons to variables. """ return _pywrapcp.Constraint_Post(self) def InitialPropagateWrapper(self) -> "void": r""" This method performs the initial propagation of the constraint. It is called just after the post. """ return _pywrapcp.Constraint_InitialPropagateWrapper(self) def DebugString(self) -> "std::string": return _pywrapcp.Constraint_DebugString(self) def Var(self) -> "operations_research::IntVar *": r""" Creates a Boolean variable representing the status of the constraint (false = constraint is violated, true = constraint is satisfied). It returns nullptr if the constraint does not support this API. """ return _pywrapcp.Constraint_Var(self) def __repr__(self) -> "std::string": return _pywrapcp.Constraint___repr__(self) def __str__(self) -> "std::string": return _pywrapcp.Constraint___str__(self) def __add__(self, *args) -> "operations_research::IntExpr *": return _pywrapcp.Constraint___add__(self, *args) def __radd__(self, v: "int64_t") -> "operations_research::IntExpr *": return _pywrapcp.Constraint___radd__(self, v) def __sub__(self, *args) -> "operations_research::IntExpr *": return _pywrapcp.Constraint___sub__(self, *args) def __rsub__(self, v: "int64_t") -> "operations_research::IntExpr *": return _pywrapcp.Constraint___rsub__(self, v) def __mul__(self, *args) -> "operations_research::IntExpr *": return _pywrapcp.Constraint___mul__(self, *args) def __rmul__(self, v: "int64_t") -> "operations_research::IntExpr *": return _pywrapcp.Constraint___rmul__(self, v) def __floordiv__(self, v: "int64_t") -> "operations_research::IntExpr *": return _pywrapcp.Constraint___floordiv__(self, v) def __neg__(self) -> "operations_research::IntExpr *": return _pywrapcp.Constraint___neg__(self) def __abs__(self) -> "operations_research::IntExpr *": return _pywrapcp.Constraint___abs__(self) def Square(self) -> "operations_research::IntExpr *": return _pywrapcp.Constraint_Square(self) def __eq__(self, *args) -> "operations_research::Constraint *": return _pywrapcp.Constraint___eq__(self, *args) def __ne__(self, *args) -> "operations_research::Constraint *": return _pywrapcp.Constraint___ne__(self, *args) def __ge__(self, *args) -> "operations_research::Constraint *": return _pywrapcp.Constraint___ge__(self, *args) def __gt__(self, *args) -> "operations_research::Constraint *": return _pywrapcp.Constraint___gt__(self, *args) def __le__(self, *args) -> "operations_research::Constraint *": return _pywrapcp.Constraint___le__(self, *args) def __lt__(self, *args) -> "operations_research::Constraint *": return _pywrapcp.Constraint___lt__(self, *args) def MapTo(self, vars: "std::vector< operations_research::IntVar * > const &") -> "operations_research::Constraint *": return _pywrapcp.Constraint_MapTo(self, vars) def IndexOf(self, *args) -> "operations_research::IntExpr *": return _pywrapcp.Constraint_IndexOf(self, *args) def __disown__(self): self.this.disown() _pywrapcp.disown_Constraint(self) return weakref.proxy(self)Ancestors
Subclasses
Methods
def DebugString(self) ‑> std::string-
Expand source code
def DebugString(self) -> "std::string": return _pywrapcp.Constraint_DebugString(self) def IndexOf(self, *args) ‑> operations_research::IntExpr *-
Expand source code
def IndexOf(self, *args) -> "operations_research::IntExpr *": return _pywrapcp.Constraint_IndexOf(self, *args) def InitialPropagateWrapper(self) ‑> void-
This method performs the initial propagation of the constraint. It is called just after the post.
Expand source code
def InitialPropagateWrapper(self) -> "void": r""" This method performs the initial propagation of the constraint. It is called just after the post. """ return _pywrapcp.Constraint_InitialPropagateWrapper(self) def MapTo(self, vars: std::vector< operations_research::IntVar * > const &) ‑> operations_research::Constraint *-
Expand source code
def MapTo(self, vars: "std::vector< operations_research::IntVar * > const &") -> "operations_research::Constraint *": return _pywrapcp.Constraint_MapTo(self, vars) def Post(self) ‑> void-
This method is called when the constraint is processed by the solver. Its main usage is to attach demons to variables.
Expand source code
def Post(self) -> "void": r""" This method is called when the constraint is processed by the solver. Its main usage is to attach demons to variables. """ return _pywrapcp.Constraint_Post(self) def Square(self) ‑> operations_research::IntExpr *-
Expand source code
def Square(self) -> "operations_research::IntExpr *": return _pywrapcp.Constraint_Square(self) def Var(self) ‑> operations_research::IntVar *-
Creates a Boolean variable representing the status of the constraint (false = constraint is violated, true = constraint is satisfied). It returns nullptr if the constraint does not support this API.
Expand source code
def Var(self) -> "operations_research::IntVar *": r""" Creates a Boolean variable representing the status of the constraint (false = constraint is violated, true = constraint is satisfied). It returns nullptr if the constraint does not support this API. """ return _pywrapcp.Constraint_Var(self)
Inherited members
class Decision-
A Decision represents a choice point in the search tree. The two main methods are Apply() to go left, or Refute() to go right.
Expand source code
class Decision(BaseObject): r""" A Decision represents a choice point in the search tree. The two main methods are Apply() to go left, or Refute() to go right. """ thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") def __init__(self): if self.__class__ == Decision: _self = None else: _self = self _pywrapcp.Decision_swiginit(self, _pywrapcp.new_Decision(_self, )) __swig_destroy__ = _pywrapcp.delete_Decision def ApplyWrapper(self, s: "Solver") -> "void": r""" Apply will be called first when the decision is executed.""" return _pywrapcp.Decision_ApplyWrapper(self, s) def RefuteWrapper(self, s: "Solver") -> "void": r""" Refute will be called after a backtrack.""" return _pywrapcp.Decision_RefuteWrapper(self, s) def DebugString(self) -> "std::string": return _pywrapcp.Decision_DebugString(self) def __repr__(self) -> "std::string": return _pywrapcp.Decision___repr__(self) def __str__(self) -> "std::string": return _pywrapcp.Decision___str__(self) def __disown__(self): self.this.disown() _pywrapcp.disown_Decision(self) return weakref.proxy(self)Ancestors
Subclasses
Methods
def ApplyWrapper(self, s: Solver) ‑> void-
Apply will be called first when the decision is executed.
Expand source code
def ApplyWrapper(self, s: "Solver") -> "void": r""" Apply will be called first when the decision is executed.""" return _pywrapcp.Decision_ApplyWrapper(self, s) def DebugString(self) ‑> std::string-
Expand source code
def DebugString(self) -> "std::string": return _pywrapcp.Decision_DebugString(self) def RefuteWrapper(self, s: Solver) ‑> void-
Refute will be called after a backtrack.
Expand source code
def RefuteWrapper(self, s: "Solver") -> "void": r""" Refute will be called after a backtrack.""" return _pywrapcp.Decision_RefuteWrapper(self, s)
Inherited members
class DecisionBuilder-
A DecisionBuilder is responsible for creating the search tree. The important method is Next(), which returns the next decision to execute.
Expand source code
class DecisionBuilder(BaseObject): r""" A DecisionBuilder is responsible for creating the search tree. The important method is Next(), which returns the next decision to execute. """ thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") def __init__(self): if self.__class__ == DecisionBuilder: _self = None else: _self = self _pywrapcp.DecisionBuilder_swiginit(self, _pywrapcp.new_DecisionBuilder(_self, )) __swig_destroy__ = _pywrapcp.delete_DecisionBuilder def NextWrapper(self, s: "Solver") -> "operations_research::Decision *": r""" This is the main method of the decision builder class. It must return a decision (an instance of the class Decision). If it returns nullptr, this means that the decision builder has finished its work. """ return _pywrapcp.DecisionBuilder_NextWrapper(self, s) def DebugString(self) -> "std::string": return _pywrapcp.DecisionBuilder_DebugString(self) def __repr__(self) -> "std::string": return _pywrapcp.DecisionBuilder___repr__(self) def __str__(self) -> "std::string": return _pywrapcp.DecisionBuilder___str__(self) def __disown__(self): self.this.disown() _pywrapcp.disown_DecisionBuilder(self) return weakref.proxy(self)Ancestors
Subclasses
Methods
def DebugString(self) ‑> std::string-
Expand source code
def DebugString(self) -> "std::string": return _pywrapcp.DecisionBuilder_DebugString(self) def NextWrapper(self, s: Solver) ‑> operations_research::Decision *-
This is the main method of the decision builder class. It must return a decision (an instance of the class Decision). If it returns nullptr, this means that the decision builder has finished its work.
Expand source code
def NextWrapper(self, s: "Solver") -> "operations_research::Decision *": r""" This is the main method of the decision builder class. It must return a decision (an instance of the class Decision). If it returns nullptr, this means that the decision builder has finished its work. """ return _pywrapcp.DecisionBuilder_NextWrapper(self, s)
Inherited members
class DefaultPhaseParameters-
This struct holds all parameters for the default search. DefaultPhaseParameters is only used by Solver::MakeDefaultPhase methods. Note this is for advanced users only.
Expand source code
class DefaultPhaseParameters(object): r""" This struct holds all parameters for the default search. DefaultPhaseParameters is only used by Solver::MakeDefaultPhase methods. Note this is for advanced users only. """ thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") __repr__ = _swig_repr CHOOSE_MAX_SUM_IMPACT = _pywrapcp.DefaultPhaseParameters_CHOOSE_MAX_SUM_IMPACT CHOOSE_MAX_AVERAGE_IMPACT = _pywrapcp.DefaultPhaseParameters_CHOOSE_MAX_AVERAGE_IMPACT CHOOSE_MAX_VALUE_IMPACT = _pywrapcp.DefaultPhaseParameters_CHOOSE_MAX_VALUE_IMPACT SELECT_MIN_IMPACT = _pywrapcp.DefaultPhaseParameters_SELECT_MIN_IMPACT SELECT_MAX_IMPACT = _pywrapcp.DefaultPhaseParameters_SELECT_MAX_IMPACT NONE = _pywrapcp.DefaultPhaseParameters_NONE NORMAL = _pywrapcp.DefaultPhaseParameters_NORMAL VERBOSE = _pywrapcp.DefaultPhaseParameters_VERBOSE var_selection_schema = property(_pywrapcp.DefaultPhaseParameters_var_selection_schema_get, _pywrapcp.DefaultPhaseParameters_var_selection_schema_set, doc=r""" This parameter describes how the next variable to instantiate will be chosen. """) value_selection_schema = property(_pywrapcp.DefaultPhaseParameters_value_selection_schema_get, _pywrapcp.DefaultPhaseParameters_value_selection_schema_set, doc=r""" This parameter describes which value to select for a given var.""") initialization_splits = property(_pywrapcp.DefaultPhaseParameters_initialization_splits_get, _pywrapcp.DefaultPhaseParameters_initialization_splits_set, doc=r""" Maximum number of intervals that the initialization of impacts will scan per variable. """) run_all_heuristics = property(_pywrapcp.DefaultPhaseParameters_run_all_heuristics_get, _pywrapcp.DefaultPhaseParameters_run_all_heuristics_set, doc=r""" The default phase will run heuristics periodically. This parameter indicates if we should run all heuristics, or a randomly selected one. """) heuristic_period = property(_pywrapcp.DefaultPhaseParameters_heuristic_period_get, _pywrapcp.DefaultPhaseParameters_heuristic_period_set, doc=r""" The distance in nodes between each run of the heuristics. A negative or null value will mean that we will not run heuristics at all. """) heuristic_num_failures_limit = property(_pywrapcp.DefaultPhaseParameters_heuristic_num_failures_limit_get, _pywrapcp.DefaultPhaseParameters_heuristic_num_failures_limit_set, doc=r""" The failure limit for each heuristic that we run.""") persistent_impact = property(_pywrapcp.DefaultPhaseParameters_persistent_impact_get, _pywrapcp.DefaultPhaseParameters_persistent_impact_set, doc=r""" Whether to keep the impact from the first search for other searches, or to recompute the impact for each new search. """) random_seed = property(_pywrapcp.DefaultPhaseParameters_random_seed_get, _pywrapcp.DefaultPhaseParameters_random_seed_set, doc=r""" Seed used to initialize the random part in some heuristics.""") display_level = property(_pywrapcp.DefaultPhaseParameters_display_level_get, _pywrapcp.DefaultPhaseParameters_display_level_set, doc=r""" This represents the amount of information displayed by the default search. NONE means no display, VERBOSE means extra information. """) decision_builder = property(_pywrapcp.DefaultPhaseParameters_decision_builder_get, _pywrapcp.DefaultPhaseParameters_decision_builder_set, doc=r""" When defined, this overrides the default impact based decision builder.""") def __init__(self): _pywrapcp.DefaultPhaseParameters_swiginit(self, _pywrapcp.new_DefaultPhaseParameters()) __swig_destroy__ = _pywrapcp.delete_DefaultPhaseParametersClass variables
var CHOOSE_MAX_AVERAGE_IMPACTvar CHOOSE_MAX_SUM_IMPACTvar CHOOSE_MAX_VALUE_IMPACTvar NONEvar NORMALvar SELECT_MAX_IMPACTvar SELECT_MIN_IMPACTvar VERBOSE
Instance variables
var decision_builder-
When defined, this overrides the default impact based decision builder.
var display_level-
This represents the amount of information displayed by the default search. NONE means no display, VERBOSE means extra information.
var heuristic_num_failures_limit-
The failure limit for each heuristic that we run.
var heuristic_period-
The distance in nodes between each run of the heuristics. A negative or null value will mean that we will not run heuristics at all.
var initialization_splits-
Maximum number of intervals that the initialization of impacts will scan per variable.
var persistent_impact-
Whether to keep the impact from the first search for other searches, or to recompute the impact for each new search.
var random_seed-
Seed used to initialize the random part in some heuristics.
var run_all_heuristics-
The default phase will run heuristics periodically. This parameter indicates if we should run all heuristics, or a randomly selected one.
var thisown-
The membership flag
Expand source code
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") var value_selection_schema-
This parameter describes which value to select for a given var.
var var_selection_schema-
This parameter describes how the next variable to instantiate will be chosen.
class Demon-
A Demon is the base element of a propagation queue. It is the main object responsible for implementing the actual propagation of the constraint and pruning the inconsistent values in the domains of the variables. The main concept is that demons are listeners that are attached to the variables and listen to their modifications. There are two methods: - Run() is the actual method called when the demon is processed. - priority() returns its priority. Standard priorities are slow, normal or fast. "immediate" is reserved for variables and is treated separately.
This indicates the priority of a demon. Immediate demons are treated separately and corresponds to variables.
Expand source code
class Demon(BaseObject): r""" A Demon is the base element of a propagation queue. It is the main object responsible for implementing the actual propagation of the constraint and pruning the inconsistent values in the domains of the variables. The main concept is that demons are listeners that are attached to the variables and listen to their modifications. There are two methods: - Run() is the actual method called when the demon is processed. - priority() returns its priority. Standard priorities are slow, normal or fast. "immediate" is reserved for variables and is treated separately. """ thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") __repr__ = _swig_repr def __init__(self): r""" This indicates the priority of a demon. Immediate demons are treated separately and corresponds to variables. """ if self.__class__ == Demon: _self = None else: _self = self _pywrapcp.Demon_swiginit(self, _pywrapcp.new_Demon(_self, )) __swig_destroy__ = _pywrapcp.delete_Demon def RunWrapper(self, s: "Solver") -> "void": r""" This is the main callback of the demon.""" return _pywrapcp.Demon_RunWrapper(self, s) def Priority(self) -> "operations_research::Solver::DemonPriority": r""" This method returns the priority of the demon. Usually a demon is fast, slow or normal. Immediate demons are reserved for internal use to maintain variables. """ return _pywrapcp.Demon_Priority(self) def DebugString(self) -> "std::string": return _pywrapcp.Demon_DebugString(self) def Inhibit(self, s: "Solver") -> "void": r""" This method inhibits the demon in the search tree below the current position. """ return _pywrapcp.Demon_Inhibit(self, s) def Desinhibit(self, s: "Solver") -> "void": r""" This method un-inhibits the demon that was previously inhibited.""" return _pywrapcp.Demon_Desinhibit(self, s) def __disown__(self): self.this.disown() _pywrapcp.disown_Demon(self) return weakref.proxy(self)Ancestors
Subclasses
Methods
def DebugString(self) ‑> std::string-
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def DebugString(self) -> "std::string": return _pywrapcp.Demon_DebugString(self) def Desinhibit(self, s: Solver) ‑> void-
This method un-inhibits the demon that was previously inhibited.
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def Desinhibit(self, s: "Solver") -> "void": r""" This method un-inhibits the demon that was previously inhibited.""" return _pywrapcp.Demon_Desinhibit(self, s) def Inhibit(self, s: Solver) ‑> void-
This method inhibits the demon in the search tree below the current position.
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def Inhibit(self, s: "Solver") -> "void": r""" This method inhibits the demon in the search tree below the current position. """ return _pywrapcp.Demon_Inhibit(self, s) def Priority(self) ‑> operations_research::Solver::DemonPriority-
This method returns the priority of the demon. Usually a demon is fast, slow or normal. Immediate demons are reserved for internal use to maintain variables.
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def Priority(self) -> "operations_research::Solver::DemonPriority": r""" This method returns the priority of the demon. Usually a demon is fast, slow or normal. Immediate demons are reserved for internal use to maintain variables. """ return _pywrapcp.Demon_Priority(self) def RunWrapper(self, s: Solver) ‑> void-
This is the main callback of the demon.
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def RunWrapper(self, s: "Solver") -> "void": r""" This is the main callback of the demon.""" return _pywrapcp.Demon_RunWrapper(self, s)
Inherited members
class DisjunctiveConstraint (*args, **kwargs)-
A constraint is the main modeling object. It provides two methods: - Post() is responsible for creating the demons and attaching them to immediate demons(). - InitialPropagate() is called once just after Post and performs the initial propagation. The subsequent propagations will be performed by the demons Posted during the post() method.
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class DisjunctiveConstraint(Constraint): thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined - class is abstract") __repr__ = _swig_repr def SequenceVar(self) -> "operations_research::SequenceVar *": r""" Creates a sequence variable from the constraint.""" return _pywrapcp.DisjunctiveConstraint_SequenceVar(self) def SetTransitionTime(self, transition_time: "operations_research::Solver::IndexEvaluator2") -> "void": r""" Add a transition time between intervals. It forces the distance between the end of interval a and start of interval b that follows it to be at least transition_time(a, b). This function must always return a positive or null value. """ return _pywrapcp.DisjunctiveConstraint_SetTransitionTime(self, transition_time) def TransitionTime(self, before_index: "int", after_index: "int") -> "int64_t": return _pywrapcp.DisjunctiveConstraint_TransitionTime(self, before_index, after_index)Ancestors
Methods
def SequenceVar(self) ‑> operations_research::SequenceVar *-
Creates a sequence variable from the constraint.
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def SequenceVar(self) -> "operations_research::SequenceVar *": r""" Creates a sequence variable from the constraint.""" return _pywrapcp.DisjunctiveConstraint_SequenceVar(self) def SetTransitionTime(self, transition_time: operations_research::Solver::IndexEvaluator2) ‑> void-
Add a transition time between intervals. It forces the distance between the end of interval a and start of interval b that follows it to be at least transition_time(a, b). This function must always return a positive or null value.
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def SetTransitionTime(self, transition_time: "operations_research::Solver::IndexEvaluator2") -> "void": r""" Add a transition time between intervals. It forces the distance between the end of interval a and start of interval b that follows it to be at least transition_time(a, b). This function must always return a positive or null value. """ return _pywrapcp.DisjunctiveConstraint_SetTransitionTime(self, transition_time) def TransitionTime(self, before_index: int, after_index: int) ‑> int64_t-
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def TransitionTime(self, before_index: "int", after_index: "int") -> "int64_t": return _pywrapcp.DisjunctiveConstraint_TransitionTime(self, before_index, after_index)
Inherited members
class GlobalVehicleBreaksConstraint (dimension:Â RoutingDimension)-
GlobalVehicleBreaksConstraint ensures breaks constraints are enforced on all vehicles in the dimension passed to its constructor. It is intended to be used for dimensions representing time. A break constraint ensures break intervals fit on the route of a vehicle. For a given vehicle, it forces break intervals to be disjoint from visit intervals, where visit intervals start at CumulVar(node) and last for node_visit_transit[node]. Moreover, it ensures that there is enough time between two consecutive nodes of a route to do transit and vehicle breaks, i.e. if Next(nodeA) = nodeB, CumulVar(nodeA) = tA and CumulVar(nodeB) = tB, then SlackVar(nodeA) >= sum_{breaks [tA, tB)} duration(break).
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class GlobalVehicleBreaksConstraint(Constraint): r""" GlobalVehicleBreaksConstraint ensures breaks constraints are enforced on all vehicles in the dimension passed to its constructor. It is intended to be used for dimensions representing time. A break constraint ensures break intervals fit on the route of a vehicle. For a given vehicle, it forces break intervals to be disjoint from visit intervals, where visit intervals start at CumulVar(node) and last for node_visit_transit[node]. Moreover, it ensures that there is enough time between two consecutive nodes of a route to do transit and vehicle breaks, i.e. if Next(nodeA) = nodeB, CumulVar(nodeA) = tA and CumulVar(nodeB) = tB, then SlackVar(nodeA) >= sum_{breaks [tA, tB)} duration(break). """ thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") __repr__ = _swig_repr def __init__(self, dimension: "RoutingDimension"): _pywrapcp.GlobalVehicleBreaksConstraint_swiginit(self, _pywrapcp.new_GlobalVehicleBreaksConstraint(dimension)) def DebugString(self) -> "std::string": return _pywrapcp.GlobalVehicleBreaksConstraint_DebugString(self) def Post(self) -> "void": return _pywrapcp.GlobalVehicleBreaksConstraint_Post(self) def InitialPropagateWrapper(self) -> "void": return _pywrapcp.GlobalVehicleBreaksConstraint_InitialPropagateWrapper(self) __swig_destroy__ = _pywrapcp.delete_GlobalVehicleBreaksConstraintAncestors
Methods
def DebugString(self) ‑> std::string-
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def DebugString(self) -> "std::string": return _pywrapcp.GlobalVehicleBreaksConstraint_DebugString(self)
Inherited members
class IntExpr (*args, **kwargs)-
The class IntExpr is the base of all integer expressions in constraint programming. It contains the basic protocol for an expression: - setting and modifying its bound - querying if it is bound - listening to events modifying its bounds - casting it into a variable (instance of IntVar)
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class IntExpr(PropagationBaseObject): r""" The class IntExpr is the base of all integer expressions in constraint programming. It contains the basic protocol for an expression: - setting and modifying its bound - querying if it is bound - listening to events modifying its bounds - casting it into a variable (instance of IntVar) """ thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined - class is abstract") def Min(self) -> "int64_t": return _pywrapcp.IntExpr_Min(self) def SetMin(self, m: "int64_t") -> "void": return _pywrapcp.IntExpr_SetMin(self, m) def Max(self) -> "int64_t": return _pywrapcp.IntExpr_Max(self) def SetMax(self, m: "int64_t") -> "void": return _pywrapcp.IntExpr_SetMax(self, m) def SetRange(self, l: "int64_t", u: "int64_t") -> "void": r""" This method sets both the min and the max of the expression.""" return _pywrapcp.IntExpr_SetRange(self, l, u) def SetValue(self, v: "int64_t") -> "void": r""" This method sets the value of the expression.""" return _pywrapcp.IntExpr_SetValue(self, v) def Bound(self) -> "bool": r""" Returns true if the min and the max of the expression are equal.""" return _pywrapcp.IntExpr_Bound(self) def IsVar(self) -> "bool": r""" Returns true if the expression is indeed a variable.""" return _pywrapcp.IntExpr_IsVar(self) def Var(self) -> "operations_research::IntVar *": r""" Creates a variable from the expression.""" return _pywrapcp.IntExpr_Var(self) def VarWithName(self, name: "std::string const &") -> "operations_research::IntVar *": r""" Creates a variable from the expression and set the name of the resulting var. If the expression is already a variable, then it will set the name of the expression, possibly overwriting it. This is just a shortcut to Var() followed by set_name(). """ return _pywrapcp.IntExpr_VarWithName(self, name) def WhenRange(self, *args) -> "void": r""" *Overload 1:* Attach a demon that will watch the min or the max of the expression. | *Overload 2:* Attach a demon that will watch the min or the max of the expression. """ return _pywrapcp.IntExpr_WhenRange(self, *args) def __repr__(self) -> "std::string": return _pywrapcp.IntExpr___repr__(self) def __str__(self) -> "std::string": return _pywrapcp.IntExpr___str__(self) def __add__(self, *args) -> "operations_research::IntExpr *": return _pywrapcp.IntExpr___add__(self, *args) def __radd__(self, v: "int64_t") -> "operations_research::IntExpr *": return _pywrapcp.IntExpr___radd__(self, v) def __sub__(self, *args) -> "operations_research::IntExpr *": return _pywrapcp.IntExpr___sub__(self, *args) def __rsub__(self, v: "int64_t") -> "operations_research::IntExpr *": return _pywrapcp.IntExpr___rsub__(self, v) def __mul__(self, *args) -> "operations_research::IntExpr *": return _pywrapcp.IntExpr___mul__(self, *args) def __rmul__(self, v: "int64_t") -> "operations_research::IntExpr *": return _pywrapcp.IntExpr___rmul__(self, v) def __floordiv__(self, *args) -> "operations_research::IntExpr *": return _pywrapcp.IntExpr___floordiv__(self, *args) def __mod__(self, *args) -> "operations_research::IntExpr *": return _pywrapcp.IntExpr___mod__(self, *args) def __neg__(self) -> "operations_research::IntExpr *": return _pywrapcp.IntExpr___neg__(self) def __abs__(self) -> "operations_research::IntExpr *": return _pywrapcp.IntExpr___abs__(self) def Square(self) -> "operations_research::IntExpr *": return _pywrapcp.IntExpr_Square(self) def __eq__(self, *args) -> "operations_research::Constraint *": return _pywrapcp.IntExpr___eq__(self, *args) def __ne__(self, *args) -> "operations_research::Constraint *": return _pywrapcp.IntExpr___ne__(self, *args) def __ge__(self, *args) -> "operations_research::Constraint *": return _pywrapcp.IntExpr___ge__(self, *args) def __gt__(self, *args) -> "operations_research::Constraint *": return _pywrapcp.IntExpr___gt__(self, *args) def __le__(self, *args) -> "operations_research::Constraint *": return _pywrapcp.IntExpr___le__(self, *args) def __lt__(self, *args) -> "operations_research::Constraint *": return _pywrapcp.IntExpr___lt__(self, *args) def MapTo(self, vars: "std::vector< operations_research::IntVar * > const &") -> "operations_research::Constraint *": return _pywrapcp.IntExpr_MapTo(self, vars) def IndexOf(self, *args) -> "operations_research::IntExpr *": return _pywrapcp.IntExpr_IndexOf(self, *args) def IsMember(self, values: "std::vector< int64_t > const &") -> "operations_research::IntVar *": return _pywrapcp.IntExpr_IsMember(self, values) def Member(self, values: "std::vector< int64_t > const &") -> "operations_research::Constraint *": return _pywrapcp.IntExpr_Member(self, values) def NotMember(self, starts: "std::vector< int64_t > const &", ends: "std::vector< int64_t > const &") -> "operations_research::Constraint *": return _pywrapcp.IntExpr_NotMember(self, starts, ends)Ancestors
Subclasses
Methods
def Bound(self) ‑> bool-
Returns true if the min and the max of the expression are equal.
Expand source code
def Bound(self) -> "bool": r""" Returns true if the min and the max of the expression are equal.""" return _pywrapcp.IntExpr_Bound(self) def IndexOf(self, *args) ‑> operations_research::IntExpr *-
Expand source code
def IndexOf(self, *args) -> "operations_research::IntExpr *": return _pywrapcp.IntExpr_IndexOf(self, *args) def IsMember(self, values: std::vector< int64_t > const &) ‑> operations_research::IntVar *-
Expand source code
def IsMember(self, values: "std::vector< int64_t > const &") -> "operations_research::IntVar *": return _pywrapcp.IntExpr_IsMember(self, values) def IsVar(self) ‑> bool-
Returns true if the expression is indeed a variable.
Expand source code
def IsVar(self) -> "bool": r""" Returns true if the expression is indeed a variable.""" return _pywrapcp.IntExpr_IsVar(self) def MapTo(self, vars: std::vector< operations_research::IntVar * > const &) ‑> operations_research::Constraint *-
Expand source code
def MapTo(self, vars: "std::vector< operations_research::IntVar * > const &") -> "operations_research::Constraint *": return _pywrapcp.IntExpr_MapTo(self, vars) def Max(self) ‑> int64_t-
Expand source code
def Max(self) -> "int64_t": return _pywrapcp.IntExpr_Max(self) def Member(self, values: std::vector< int64_t > const &) ‑> operations_research::Constraint *-
Expand source code
def Member(self, values: "std::vector< int64_t > const &") -> "operations_research::Constraint *": return _pywrapcp.IntExpr_Member(self, values) def Min(self) ‑> int64_t-
Expand source code
def Min(self) -> "int64_t": return _pywrapcp.IntExpr_Min(self) def NotMember(self, starts: std::vector< int64_t > const &, ends: std::vector< int64_t > const &) ‑> operations_research::Constraint *-
Expand source code
def NotMember(self, starts: "std::vector< int64_t > const &", ends: "std::vector< int64_t > const &") -> "operations_research::Constraint *": return _pywrapcp.IntExpr_NotMember(self, starts, ends) def SetMax(self, m: int64_t) ‑> void-
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def SetMax(self, m: "int64_t") -> "void": return _pywrapcp.IntExpr_SetMax(self, m) def SetMin(self, m: int64_t) ‑> void-
Expand source code
def SetMin(self, m: "int64_t") -> "void": return _pywrapcp.IntExpr_SetMin(self, m) def SetRange(self, l: int64_t, u: int64_t) ‑> void-
This method sets both the min and the max of the expression.
Expand source code
def SetRange(self, l: "int64_t", u: "int64_t") -> "void": r""" This method sets both the min and the max of the expression.""" return _pywrapcp.IntExpr_SetRange(self, l, u) def SetValue(self, v: int64_t) ‑> void-
This method sets the value of the expression.
Expand source code
def SetValue(self, v: "int64_t") -> "void": r""" This method sets the value of the expression.""" return _pywrapcp.IntExpr_SetValue(self, v) def Square(self) ‑> operations_research::IntExpr *-
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def Square(self) -> "operations_research::IntExpr *": return _pywrapcp.IntExpr_Square(self) def Var(self) ‑> operations_research::IntVar *-
Creates a variable from the expression.
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def Var(self) -> "operations_research::IntVar *": r""" Creates a variable from the expression.""" return _pywrapcp.IntExpr_Var(self) def VarWithName(self, name: std::string const &) ‑> operations_research::IntVar *-
Creates a variable from the expression and set the name of the resulting var. If the expression is already a variable, then it will set the name of the expression, possibly overwriting it. This is just a shortcut to Var() followed by set_name().
Expand source code
def VarWithName(self, name: "std::string const &") -> "operations_research::IntVar *": r""" Creates a variable from the expression and set the name of the resulting var. If the expression is already a variable, then it will set the name of the expression, possibly overwriting it. This is just a shortcut to Var() followed by set_name(). """ return _pywrapcp.IntExpr_VarWithName(self, name) def WhenRange(self, *args) ‑> void-
Overload 1: Attach a demon that will watch the min or the max of the expression.
|
Overload 2: Attach a demon that will watch the min or the max of the expression.
Expand source code
def WhenRange(self, *args) -> "void": r""" *Overload 1:* Attach a demon that will watch the min or the max of the expression. | *Overload 2:* Attach a demon that will watch the min or the max of the expression. """ return _pywrapcp.IntExpr_WhenRange(self, *args)
Inherited members
class IntVar (*args, **kwargs)-
The class IntVar is a subset of IntExpr. In addition to the IntExpr protocol, it offers persistence, removing values from the domains, and a finer model for events.
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class IntVar(IntExpr): r""" The class IntVar is a subset of IntExpr. In addition to the IntExpr protocol, it offers persistence, removing values from the domains, and a finer model for events. """ thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined - class is abstract") def IsVar(self) -> "bool": return _pywrapcp.IntVar_IsVar(self) def Var(self) -> "operations_research::IntVar *": return _pywrapcp.IntVar_Var(self) def Value(self) -> "int64_t": r""" This method returns the value of the variable. This method checks before that the variable is bound. """ return _pywrapcp.IntVar_Value(self) def RemoveValue(self, v: "int64_t") -> "void": r""" This method removes the value 'v' from the domain of the variable.""" return _pywrapcp.IntVar_RemoveValue(self, v) def RemoveInterval(self, l: "int64_t", u: "int64_t") -> "void": r""" This method removes the interval 'l' .. 'u' from the domain of the variable. It assumes that 'l' <= 'u'. """ return _pywrapcp.IntVar_RemoveInterval(self, l, u) def RemoveValues(self, values: "std::vector< int64_t > const &") -> "void": r""" This method remove the values from the domain of the variable.""" return _pywrapcp.IntVar_RemoveValues(self, values) def SetValues(self, values: "std::vector< int64_t > const &") -> "void": r""" This method intersects the current domain with the values in the array.""" return _pywrapcp.IntVar_SetValues(self, values) def WhenBound(self, *args) -> "void": r""" *Overload 1:* This method attaches a demon that will be awakened when the variable is bound. | *Overload 2:* This method attaches a closure that will be awakened when the variable is bound. """ return _pywrapcp.IntVar_WhenBound(self, *args) def WhenDomain(self, *args) -> "void": r""" *Overload 1:* This method attaches a demon that will watch any domain modification of the domain of the variable. | *Overload 2:* This method attaches a closure that will watch any domain modification of the domain of the variable. """ return _pywrapcp.IntVar_WhenDomain(self, *args) def Size(self) -> "uint64_t": r""" This method returns the number of values in the domain of the variable.""" return _pywrapcp.IntVar_Size(self) def Contains(self, v: "int64_t") -> "bool": r""" This method returns whether the value 'v' is in the domain of the variable. """ return _pywrapcp.IntVar_Contains(self, v) def HoleIteratorAux(self, reversible: "bool") -> "operations_research::IntVarIterator *": r""" Creates a hole iterator. When 'reversible' is false, the returned object is created on the normal C++ heap and the solver does NOT take ownership of the object. """ return _pywrapcp.IntVar_HoleIteratorAux(self, reversible) def DomainIteratorAux(self, reversible: "bool") -> "operations_research::IntVarIterator *": r""" Creates a domain iterator. When 'reversible' is false, the returned object is created on the normal C++ heap and the solver does NOT take ownership of the object. """ return _pywrapcp.IntVar_DomainIteratorAux(self, reversible) def OldMin(self) -> "int64_t": r""" Returns the previous min.""" return _pywrapcp.IntVar_OldMin(self) def OldMax(self) -> "int64_t": r""" Returns the previous max.""" return _pywrapcp.IntVar_OldMax(self) def __repr__(self) -> "std::string": return _pywrapcp.IntVar___repr__(self) def __str__(self) -> "std::string": return _pywrapcp.IntVar___str__(self) def DomainIterator(self): return iter(self.DomainIteratorAux(False)) def HoleIterator(self): return iter(self.HoleIteratorAux(False))Ancestors
Subclasses
Methods
def Contains(self, v: int64_t) ‑> bool-
This method returns whether the value 'v' is in the domain of the variable.
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def Contains(self, v: "int64_t") -> "bool": r""" This method returns whether the value 'v' is in the domain of the variable. """ return _pywrapcp.IntVar_Contains(self, v) def DomainIterator(self)-
Expand source code
def DomainIterator(self): return iter(self.DomainIteratorAux(False)) def DomainIteratorAux(self, reversible: bool) ‑> operations_research::IntVarIterator *-
Creates a domain iterator. When 'reversible' is false, the returned object is created on the normal C++ heap and the solver does NOT take ownership of the object.
Expand source code
def DomainIteratorAux(self, reversible: "bool") -> "operations_research::IntVarIterator *": r""" Creates a domain iterator. When 'reversible' is false, the returned object is created on the normal C++ heap and the solver does NOT take ownership of the object. """ return _pywrapcp.IntVar_DomainIteratorAux(self, reversible) def HoleIterator(self)-
Expand source code
def HoleIterator(self): return iter(self.HoleIteratorAux(False)) def HoleIteratorAux(self, reversible: bool) ‑> operations_research::IntVarIterator *-
Creates a hole iterator. When 'reversible' is false, the returned object is created on the normal C++ heap and the solver does NOT take ownership of the object.
Expand source code
def HoleIteratorAux(self, reversible: "bool") -> "operations_research::IntVarIterator *": r""" Creates a hole iterator. When 'reversible' is false, the returned object is created on the normal C++ heap and the solver does NOT take ownership of the object. """ return _pywrapcp.IntVar_HoleIteratorAux(self, reversible) def OldMax(self) ‑> int64_t-
Returns the previous max.
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def OldMax(self) -> "int64_t": r""" Returns the previous max.""" return _pywrapcp.IntVar_OldMax(self) def OldMin(self) ‑> int64_t-
Returns the previous min.
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def OldMin(self) -> "int64_t": r""" Returns the previous min.""" return _pywrapcp.IntVar_OldMin(self) def RemoveInterval(self, l: int64_t, u: int64_t) ‑> void-
This method removes the interval 'l' .. 'u' from the domain of the variable. It assumes that 'l' <= 'u'.
Expand source code
def RemoveInterval(self, l: "int64_t", u: "int64_t") -> "void": r""" This method removes the interval 'l' .. 'u' from the domain of the variable. It assumes that 'l' <= 'u'. """ return _pywrapcp.IntVar_RemoveInterval(self, l, u) def RemoveValue(self, v: int64_t) ‑> void-
This method removes the value 'v' from the domain of the variable.
Expand source code
def RemoveValue(self, v: "int64_t") -> "void": r""" This method removes the value 'v' from the domain of the variable.""" return _pywrapcp.IntVar_RemoveValue(self, v) def RemoveValues(self, values: std::vector< int64_t > const &) ‑> void-
This method remove the values from the domain of the variable.
Expand source code
def RemoveValues(self, values: "std::vector< int64_t > const &") -> "void": r""" This method remove the values from the domain of the variable.""" return _pywrapcp.IntVar_RemoveValues(self, values) def SetValues(self, values: std::vector< int64_t > const &) ‑> void-
This method intersects the current domain with the values in the array.
Expand source code
def SetValues(self, values: "std::vector< int64_t > const &") -> "void": r""" This method intersects the current domain with the values in the array.""" return _pywrapcp.IntVar_SetValues(self, values) def Size(self) ‑> uint64_t-
This method returns the number of values in the domain of the variable.
Expand source code
def Size(self) -> "uint64_t": r""" This method returns the number of values in the domain of the variable.""" return _pywrapcp.IntVar_Size(self) def Value(self) ‑> int64_t-
This method returns the value of the variable. This method checks before that the variable is bound.
Expand source code
def Value(self) -> "int64_t": r""" This method returns the value of the variable. This method checks before that the variable is bound. """ return _pywrapcp.IntVar_Value(self) def WhenBound(self, *args) ‑> void-
Overload 1: This method attaches a demon that will be awakened when the variable is bound.
|
Overload 2: This method attaches a closure that will be awakened when the variable is bound.
Expand source code
def WhenBound(self, *args) -> "void": r""" *Overload 1:* This method attaches a demon that will be awakened when the variable is bound. | *Overload 2:* This method attaches a closure that will be awakened when the variable is bound. """ return _pywrapcp.IntVar_WhenBound(self, *args) def WhenDomain(self, *args) ‑> void-
Overload 1: This method attaches a demon that will watch any domain modification of the domain of the variable.
|
Overload 2: This method attaches a closure that will watch any domain modification of the domain of the variable.
Expand source code
def WhenDomain(self, *args) -> "void": r""" *Overload 1:* This method attaches a demon that will watch any domain modification of the domain of the variable. | *Overload 2:* This method attaches a closure that will watch any domain modification of the domain of the variable. """ return _pywrapcp.IntVar_WhenDomain(self, *args)
Inherited members
class IntVarContainer (*args, **kwargs)-
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class IntVarContainer(object): thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined") __repr__ = _swig_repr def Contains(self, var: "IntVar") -> "bool": return _pywrapcp.IntVarContainer_Contains(self, var) def Element(self, index: "int") -> "operations_research::IntVarElement *": return _pywrapcp.IntVarContainer_Element(self, index) def Size(self) -> "int": return _pywrapcp.IntVarContainer_Size(self) def Store(self) -> "void": return _pywrapcp.IntVarContainer_Store(self) def Restore(self) -> "void": return _pywrapcp.IntVarContainer_Restore(self) def __eq__(self, container: "IntVarContainer") -> "bool": r""" Returns true if this and 'container' both represent the same V* -> E map. Runs in linear time; requires that the == operator on the type E is well defined. """ return _pywrapcp.IntVarContainer___eq__(self, container) def __ne__(self, container: "IntVarContainer") -> "bool": return _pywrapcp.IntVarContainer___ne__(self, container) __swig_destroy__ = _pywrapcp.delete_IntVarContainerInstance variables
var thisown-
The membership flag
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thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
Methods
def Contains(self, var: IntVar) ‑> bool-
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def Contains(self, var: "IntVar") -> "bool": return _pywrapcp.IntVarContainer_Contains(self, var) def Element(self, index: int) ‑> operations_research::IntVarElement *-
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def Element(self, index: "int") -> "operations_research::IntVarElement *": return _pywrapcp.IntVarContainer_Element(self, index) def Restore(self) ‑> void-
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def Restore(self) -> "void": return _pywrapcp.IntVarContainer_Restore(self) def Size(self) ‑> int-
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def Size(self) -> "int": return _pywrapcp.IntVarContainer_Size(self) def Store(self) ‑> void-
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def Store(self) -> "void": return _pywrapcp.IntVarContainer_Store(self)
class IntVarElement (*args, **kwargs)-
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class IntVarElement(AssignmentElement): thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined") __repr__ = _swig_repr def Var(self) -> "operations_research::IntVar *": return _pywrapcp.IntVarElement_Var(self) def Min(self) -> "int64_t": return _pywrapcp.IntVarElement_Min(self) def SetMin(self, m: "int64_t") -> "void": return _pywrapcp.IntVarElement_SetMin(self, m) def Max(self) -> "int64_t": return _pywrapcp.IntVarElement_Max(self) def SetMax(self, m: "int64_t") -> "void": return _pywrapcp.IntVarElement_SetMax(self, m) def Value(self) -> "int64_t": return _pywrapcp.IntVarElement_Value(self) def Bound(self) -> "bool": return _pywrapcp.IntVarElement_Bound(self) def SetRange(self, l: "int64_t", u: "int64_t") -> "void": return _pywrapcp.IntVarElement_SetRange(self, l, u) def SetValue(self, v: "int64_t") -> "void": return _pywrapcp.IntVarElement_SetValue(self, v) def __eq__(self, element: "IntVarElement") -> "bool": return _pywrapcp.IntVarElement___eq__(self, element) def __ne__(self, element: "IntVarElement") -> "bool": return _pywrapcp.IntVarElement___ne__(self, element) __swig_destroy__ = _pywrapcp.delete_IntVarElementAncestors
Methods
def Bound(self) ‑> bool-
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def Bound(self) -> "bool": return _pywrapcp.IntVarElement_Bound(self) def Max(self) ‑> int64_t-
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def Max(self) -> "int64_t": return _pywrapcp.IntVarElement_Max(self) def Min(self) ‑> int64_t-
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def Min(self) -> "int64_t": return _pywrapcp.IntVarElement_Min(self) def SetMax(self, m: int64_t) ‑> void-
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def SetMax(self, m: "int64_t") -> "void": return _pywrapcp.IntVarElement_SetMax(self, m) def SetMin(self, m: int64_t) ‑> void-
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def SetMin(self, m: "int64_t") -> "void": return _pywrapcp.IntVarElement_SetMin(self, m) def SetRange(self, l: int64_t, u: int64_t) ‑> void-
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def SetRange(self, l: "int64_t", u: "int64_t") -> "void": return _pywrapcp.IntVarElement_SetRange(self, l, u) def SetValue(self, v: int64_t) ‑> void-
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def SetValue(self, v: "int64_t") -> "void": return _pywrapcp.IntVarElement_SetValue(self, v) def Value(self) ‑> int64_t-
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def Value(self) -> "int64_t": return _pywrapcp.IntVarElement_Value(self) def Var(self) ‑> operations_research::IntVar *-
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def Var(self) -> "operations_research::IntVar *": return _pywrapcp.IntVarElement_Var(self)
Inherited members
class IntVarIterator (*args, **kwargs)-
The class Iterator has two direct subclasses. HoleIterators iterates over all holes, that is value removed between the current min and max of the variable since the last time the variable was processed in the queue. DomainIterators iterates over all elements of the variable domain. Both iterators are not robust to domain changes. Hole iterators can also report values outside the current min and max of the variable. HoleIterators should only be called from a demon attached to the variable that has created this iterator. IntVar* current_var; std::unique_ptr
it(current_var->MakeHoleIterator(false)); for (const int64_t hole : InitAndGetValues(it)) { use the hole } Expand source code
class IntVarIterator(BaseObject): r""" The class Iterator has two direct subclasses. HoleIterators iterates over all holes, that is value removed between the current min and max of the variable since the last time the variable was processed in the queue. DomainIterators iterates over all elements of the variable domain. Both iterators are not robust to domain changes. Hole iterators can also report values outside the current min and max of the variable. HoleIterators should only be called from a demon attached to the variable that has created this iterator. IntVar* current_var; std::unique_ptr<IntVarIterator> it(current_var->MakeHoleIterator(false)); for (const int64_t hole : InitAndGetValues(it)) { use the hole } """ thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined - class is abstract") __repr__ = _swig_repr def Init(self) -> "void": r""" This method must be called before each loop.""" return _pywrapcp.IntVarIterator_Init(self) def Ok(self) -> "bool": r""" This method indicates if we can call Value() or not.""" return _pywrapcp.IntVarIterator_Ok(self) def Value(self) -> "int64_t": r""" This method returns the current value of the iterator.""" return _pywrapcp.IntVarIterator_Value(self) def Next(self) -> "void": r""" This method moves the iterator to the next value.""" return _pywrapcp.IntVarIterator_Next(self) def DebugString(self) -> "std::string": r""" Pretty Print.""" return _pywrapcp.IntVarIterator_DebugString(self) def __iter__(self): self.Init() return self def next(self): if self.Ok(): result = self.Value() self.Next() return result else: raise StopIteration() def __next__(self): return self.next()Ancestors
Methods
def DebugString(self) ‑> std::string-
Pretty Print.
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def DebugString(self) -> "std::string": r""" Pretty Print.""" return _pywrapcp.IntVarIterator_DebugString(self) def Init(self) ‑> void-
This method must be called before each loop.
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def Init(self) -> "void": r""" This method must be called before each loop.""" return _pywrapcp.IntVarIterator_Init(self) def Next(self) ‑> void-
This method moves the iterator to the next value.
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def Next(self) -> "void": r""" This method moves the iterator to the next value.""" return _pywrapcp.IntVarIterator_Next(self) def Ok(self) ‑> bool-
This method indicates if we can call Value() or not.
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def Ok(self) -> "bool": r""" This method indicates if we can call Value() or not.""" return _pywrapcp.IntVarIterator_Ok(self) def Value(self) ‑> int64_t-
This method returns the current value of the iterator.
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def Value(self) -> "int64_t": r""" This method returns the current value of the iterator.""" return _pywrapcp.IntVarIterator_Value(self) def next(self)-
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def next(self): if self.Ok(): result = self.Value() self.Next() return result else: raise StopIteration()
Inherited members
class IntVarLocalSearchFilter (vars: std::vector< operations_research::IntVar * > const &)-
Classes to which this template function can be applied to as of 04/2014. Usage: LocalSearchOperator op = MakeLocalSearchOperator
(…); class TwoOpt; class Relocate; class Exchange; class Cross; class MakeActiveOperator; class MakeInactiveOperator; class MakeChainInactiveOperator; class SwapActiveOperator; class ExtendedSwapActiveOperator; class MakeActiveAndRelocate; class RelocateAndMakeActiveOperator; class RelocateAndMakeInactiveOperator; Local Search Filters are used for fast neighbor pruning. Filtering a move is done in several phases: - in the Relax phase, filters determine which parts of their internals will be changed by the candidate, and modify intermediary State - in the Accept phase, filters check that the candidate is feasible, - if the Accept phase succeeds, the solver may decide to trigger a Synchronize phase that makes filters change their internal representation to the last candidate, - otherwise (Accept fails or the solver does not want to synchronize), a Revert phase makes filters erase any intermediary State generated by the Relax and Accept phases. A given filter has phases called with the following pattern: (Relax.Accept.Synchronize | Relax.Accept.Revert | Relax.Revert) . Filters's Revert() is always called in the reverse order their Accept() was called, to allow late filters to use state done/undone by early filters' Accept()/Revert().Expand source code
class IntVarLocalSearchFilter(LocalSearchFilter): thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") __repr__ = _swig_repr def __init__(self, vars: "std::vector< operations_research::IntVar * > const &"): if self.__class__ == IntVarLocalSearchFilter: _self = None else: _self = self _pywrapcp.IntVarLocalSearchFilter_swiginit(self, _pywrapcp.new_IntVarLocalSearchFilter(_self, vars)) __swig_destroy__ = _pywrapcp.delete_IntVarLocalSearchFilter def Synchronize(self, assignment: "Assignment", delta: "Assignment") -> "void": r""" This method should not be overridden. Override OnSynchronize() instead which is called before exiting this method. """ return _pywrapcp.IntVarLocalSearchFilter_Synchronize(self, assignment, delta) def Size(self) -> "int": return _pywrapcp.IntVarLocalSearchFilter_Size(self) def Value(self, index: "int") -> "int64_t": return _pywrapcp.IntVarLocalSearchFilter_Value(self, index) def IndexFromVar(self, var: "IntVar") -> "int64_t": return _pywrapcp.IntVarLocalSearchFilter_IndexFromVar(self, var) def __disown__(self): self.this.disown() _pywrapcp.disown_IntVarLocalSearchFilter(self) return weakref.proxy(self)Ancestors
Methods
def IndexFromVar(self, var: IntVar) ‑> int64_t-
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def IndexFromVar(self, var: "IntVar") -> "int64_t": return _pywrapcp.IntVarLocalSearchFilter_IndexFromVar(self, var) def Size(self) ‑> int-
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def Size(self) -> "int": return _pywrapcp.IntVarLocalSearchFilter_Size(self) def Synchronize(self, assignment: Assignment, delta: Assignment) ‑> void-
This method should not be overridden. Override OnSynchronize() instead which is called before exiting this method.
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def Synchronize(self, assignment: "Assignment", delta: "Assignment") -> "void": r""" This method should not be overridden. Override OnSynchronize() instead which is called before exiting this method. """ return _pywrapcp.IntVarLocalSearchFilter_Synchronize(self, assignment, delta) def Value(self, index: int) ‑> int64_t-
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def Value(self, index: "int") -> "int64_t": return _pywrapcp.IntVarLocalSearchFilter_Value(self, index)
Inherited members
class IntVarLocalSearchOperator (*args)-
Base operator class for operators manipulating variables.
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class IntVarLocalSearchOperator(IntVarLocalSearchOperatorTemplate): thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") __repr__ = _swig_repr def __init__(self, *args): if self.__class__ == IntVarLocalSearchOperator: _self = None else: _self = self _pywrapcp.IntVarLocalSearchOperator_swiginit(self, _pywrapcp.new_IntVarLocalSearchOperator(_self, *args)) __swig_destroy__ = _pywrapcp.delete_IntVarLocalSearchOperator def NextNeighbor(self, delta: "Assignment", deltadelta: "Assignment") -> "bool": r""" Redefines MakeNextNeighbor to export a simpler interface. The calls to ApplyChanges() and RevertChanges() are factored in this method, hiding both delta and deltadelta from subclasses which only need to override MakeOneNeighbor(). Therefore this method should not be overridden. Override MakeOneNeighbor() instead. """ return _pywrapcp.IntVarLocalSearchOperator_NextNeighbor(self, delta, deltadelta) def OneNeighbor(self) -> "bool": r""" Creates a new neighbor. It returns false when the neighborhood is completely explored. MakeNextNeighbor() in a subclass of IntVarLocalSearchOperator. """ return _pywrapcp.IntVarLocalSearchOperator_OneNeighbor(self) def __disown__(self): self.this.disown() _pywrapcp.disown_IntVarLocalSearchOperator(self) return weakref.proxy(self)Ancestors
Subclasses
Methods
def NextNeighbor(self, delta: Assignment, deltadelta: Assignment) ‑> bool-
Redefines MakeNextNeighbor to export a simpler interface. The calls to ApplyChanges() and RevertChanges() are factored in this method, hiding both delta and deltadelta from subclasses which only need to override MakeOneNeighbor(). Therefore this method should not be overridden. Override MakeOneNeighbor() instead.
Expand source code
def NextNeighbor(self, delta: "Assignment", deltadelta: "Assignment") -> "bool": r""" Redefines MakeNextNeighbor to export a simpler interface. The calls to ApplyChanges() and RevertChanges() are factored in this method, hiding both delta and deltadelta from subclasses which only need to override MakeOneNeighbor(). Therefore this method should not be overridden. Override MakeOneNeighbor() instead. """ return _pywrapcp.IntVarLocalSearchOperator_NextNeighbor(self, delta, deltadelta) def OneNeighbor(self) ‑> bool-
Creates a new neighbor. It returns false when the neighborhood is completely explored. MakeNextNeighbor() in a subclass of IntVarLocalSearchOperator.
Expand source code
def OneNeighbor(self) -> "bool": r""" Creates a new neighbor. It returns false when the neighborhood is completely explored. MakeNextNeighbor() in a subclass of IntVarLocalSearchOperator. """ return _pywrapcp.IntVarLocalSearchOperator_OneNeighbor(self)
Inherited members
class IntVarLocalSearchOperatorTemplate (*args, **kwargs)-
Base operator class for operators manipulating variables.
Expand source code
class IntVarLocalSearchOperatorTemplate(LocalSearchOperator): r""" Base operator class for operators manipulating variables.""" thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined - class is abstract") __repr__ = _swig_repr def Start(self, assignment: "Assignment") -> "void": r""" This method should not be overridden. Override OnStart() instead which is called before exiting this method. """ return _pywrapcp.IntVarLocalSearchOperatorTemplate_Start(self, assignment) def IsIncremental(self) -> "bool": return _pywrapcp.IntVarLocalSearchOperatorTemplate_IsIncremental(self) def Size(self) -> "int": return _pywrapcp.IntVarLocalSearchOperatorTemplate_Size(self) def Value(self, index: "int64_t") -> "long const &": r""" Returns the value in the current assignment of the variable of given index. """ return _pywrapcp.IntVarLocalSearchOperatorTemplate_Value(self, index) def OldValue(self, index: "int64_t") -> "long const &": return _pywrapcp.IntVarLocalSearchOperatorTemplate_OldValue(self, index) def SetValue(self, index: "int64_t", value: "long const &") -> "void": return _pywrapcp.IntVarLocalSearchOperatorTemplate_SetValue(self, index, value) def OnStart(self) -> "void": r""" Called by Start() after synchronizing the operator with the current assignment. Should be overridden instead of Start() to avoid calling VarLocalSearchOperator::Start explicitly. """ return _pywrapcp.IntVarLocalSearchOperatorTemplate_OnStart(self)Ancestors
Subclasses
Methods
def IsIncremental(self) ‑> bool-
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def IsIncremental(self) -> "bool": return _pywrapcp.IntVarLocalSearchOperatorTemplate_IsIncremental(self) def OldValue(self, index: int64_t) ‑> long const &-
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def OldValue(self, index: "int64_t") -> "long const &": return _pywrapcp.IntVarLocalSearchOperatorTemplate_OldValue(self, index) def OnStart(self) ‑> void-
Called by Start() after synchronizing the operator with the current assignment. Should be overridden instead of Start() to avoid calling VarLocalSearchOperator::Start explicitly.
Expand source code
def OnStart(self) -> "void": r""" Called by Start() after synchronizing the operator with the current assignment. Should be overridden instead of Start() to avoid calling VarLocalSearchOperator::Start explicitly. """ return _pywrapcp.IntVarLocalSearchOperatorTemplate_OnStart(self) def SetValue(self, index: int64_t, value: long const &) ‑> void-
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def SetValue(self, index: "int64_t", value: "long const &") -> "void": return _pywrapcp.IntVarLocalSearchOperatorTemplate_SetValue(self, index, value) def Size(self) ‑> int-
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def Size(self) -> "int": return _pywrapcp.IntVarLocalSearchOperatorTemplate_Size(self) def Start(self, assignment: Assignment) ‑> void-
This method should not be overridden. Override OnStart() instead which is called before exiting this method.
Expand source code
def Start(self, assignment: "Assignment") -> "void": r""" This method should not be overridden. Override OnStart() instead which is called before exiting this method. """ return _pywrapcp.IntVarLocalSearchOperatorTemplate_Start(self, assignment) def Value(self, index: int64_t) ‑> long const &-
Returns the value in the current assignment of the variable of given index.
Expand source code
def Value(self, index: "int64_t") -> "long const &": r""" Returns the value in the current assignment of the variable of given index. """ return _pywrapcp.IntVarLocalSearchOperatorTemplate_Value(self, index)
Inherited members
class IntervalVar (*args, **kwargs)-
Interval variables are often used in scheduling. The main characteristics of an IntervalVar are the start position, duration, and end date. All these characteristics can be queried and set, and demons can be posted on their modifications.
An important aspect is optionality: an IntervalVar can be performed or not. If unperformed, then it simply does not exist, and its characteristics cannot be accessed any more. An interval var is automatically marked as unperformed when it is not consistent anymore (start greater than end, duration < 0…)
Expand source code
class IntervalVar(PropagationBaseObject): r""" Interval variables are often used in scheduling. The main characteristics of an IntervalVar are the start position, duration, and end date. All these characteristics can be queried and set, and demons can be posted on their modifications. An important aspect is optionality: an IntervalVar can be performed or not. If unperformed, then it simply does not exist, and its characteristics cannot be accessed any more. An interval var is automatically marked as unperformed when it is not consistent anymore (start greater than end, duration < 0...) """ thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined - class is abstract") def StartMin(self) -> "int64_t": r""" These methods query, set, and watch the start position of the interval var. """ return _pywrapcp.IntervalVar_StartMin(self) def StartMax(self) -> "int64_t": return _pywrapcp.IntervalVar_StartMax(self) def SetStartMin(self, m: "int64_t") -> "void": return _pywrapcp.IntervalVar_SetStartMin(self, m) def SetStartMax(self, m: "int64_t") -> "void": return _pywrapcp.IntervalVar_SetStartMax(self, m) def SetStartRange(self, mi: "int64_t", ma: "int64_t") -> "void": return _pywrapcp.IntervalVar_SetStartRange(self, mi, ma) def OldStartMin(self) -> "int64_t": return _pywrapcp.IntervalVar_OldStartMin(self) def OldStartMax(self) -> "int64_t": return _pywrapcp.IntervalVar_OldStartMax(self) def WhenStartRange(self, *args) -> "void": return _pywrapcp.IntervalVar_WhenStartRange(self, *args) def WhenStartBound(self, *args) -> "void": return _pywrapcp.IntervalVar_WhenStartBound(self, *args) def DurationMin(self) -> "int64_t": r""" These methods query, set, and watch the duration of the interval var.""" return _pywrapcp.IntervalVar_DurationMin(self) def DurationMax(self) -> "int64_t": return _pywrapcp.IntervalVar_DurationMax(self) def SetDurationMin(self, m: "int64_t") -> "void": return _pywrapcp.IntervalVar_SetDurationMin(self, m) def SetDurationMax(self, m: "int64_t") -> "void": return _pywrapcp.IntervalVar_SetDurationMax(self, m) def SetDurationRange(self, mi: "int64_t", ma: "int64_t") -> "void": return _pywrapcp.IntervalVar_SetDurationRange(self, mi, ma) def OldDurationMin(self) -> "int64_t": return _pywrapcp.IntervalVar_OldDurationMin(self) def OldDurationMax(self) -> "int64_t": return _pywrapcp.IntervalVar_OldDurationMax(self) def WhenDurationRange(self, *args) -> "void": return _pywrapcp.IntervalVar_WhenDurationRange(self, *args) def WhenDurationBound(self, *args) -> "void": return _pywrapcp.IntervalVar_WhenDurationBound(self, *args) def EndMin(self) -> "int64_t": r""" These methods query, set, and watch the end position of the interval var.""" return _pywrapcp.IntervalVar_EndMin(self) def EndMax(self) -> "int64_t": return _pywrapcp.IntervalVar_EndMax(self) def SetEndMin(self, m: "int64_t") -> "void": return _pywrapcp.IntervalVar_SetEndMin(self, m) def SetEndMax(self, m: "int64_t") -> "void": return _pywrapcp.IntervalVar_SetEndMax(self, m) def SetEndRange(self, mi: "int64_t", ma: "int64_t") -> "void": return _pywrapcp.IntervalVar_SetEndRange(self, mi, ma) def OldEndMin(self) -> "int64_t": return _pywrapcp.IntervalVar_OldEndMin(self) def OldEndMax(self) -> "int64_t": return _pywrapcp.IntervalVar_OldEndMax(self) def WhenEndRange(self, *args) -> "void": return _pywrapcp.IntervalVar_WhenEndRange(self, *args) def WhenEndBound(self, *args) -> "void": return _pywrapcp.IntervalVar_WhenEndBound(self, *args) def MustBePerformed(self) -> "bool": r""" These methods query, set, and watch the performed status of the interval var. """ return _pywrapcp.IntervalVar_MustBePerformed(self) def MayBePerformed(self) -> "bool": return _pywrapcp.IntervalVar_MayBePerformed(self) def CannotBePerformed(self) -> "bool": return _pywrapcp.IntervalVar_CannotBePerformed(self) def IsPerformedBound(self) -> "bool": return _pywrapcp.IntervalVar_IsPerformedBound(self) def SetPerformed(self, val: "bool") -> "void": return _pywrapcp.IntervalVar_SetPerformed(self, val) def WasPerformedBound(self) -> "bool": return _pywrapcp.IntervalVar_WasPerformedBound(self) def WhenPerformedBound(self, *args) -> "void": return _pywrapcp.IntervalVar_WhenPerformedBound(self, *args) def WhenAnything(self, *args) -> "void": r""" *Overload 1:* Attaches a demon awakened when anything about this interval changes. | *Overload 2:* Attaches a closure awakened when anything about this interval changes. """ return _pywrapcp.IntervalVar_WhenAnything(self, *args) def StartExpr(self) -> "operations_research::IntExpr *": r""" These methods create expressions encapsulating the start, end and duration of the interval var. Please note that these must not be used if the interval var is unperformed. """ return _pywrapcp.IntervalVar_StartExpr(self) def DurationExpr(self) -> "operations_research::IntExpr *": return _pywrapcp.IntervalVar_DurationExpr(self) def EndExpr(self) -> "operations_research::IntExpr *": return _pywrapcp.IntervalVar_EndExpr(self) def PerformedExpr(self) -> "operations_research::IntExpr *": return _pywrapcp.IntervalVar_PerformedExpr(self) def SafeStartExpr(self, unperformed_value: "int64_t") -> "operations_research::IntExpr *": r""" These methods create expressions encapsulating the start, end and duration of the interval var. If the interval var is unperformed, they will return the unperformed_value. """ return _pywrapcp.IntervalVar_SafeStartExpr(self, unperformed_value) def SafeDurationExpr(self, unperformed_value: "int64_t") -> "operations_research::IntExpr *": return _pywrapcp.IntervalVar_SafeDurationExpr(self, unperformed_value) def SafeEndExpr(self, unperformed_value: "int64_t") -> "operations_research::IntExpr *": return _pywrapcp.IntervalVar_SafeEndExpr(self, unperformed_value) def EndsAfterEnd(self, other: "IntervalVar") -> "operations_research::Constraint *": return _pywrapcp.IntervalVar_EndsAfterEnd(self, other) def EndsAfterEndWithDelay(self, other: "IntervalVar", delay: "int64_t") -> "operations_research::Constraint *": return _pywrapcp.IntervalVar_EndsAfterEndWithDelay(self, other, delay) def EndsAfterStart(self, other: "IntervalVar") -> "operations_research::Constraint *": return _pywrapcp.IntervalVar_EndsAfterStart(self, other) def EndsAfterStartWithDelay(self, other: "IntervalVar", delay: "int64_t") -> "operations_research::Constraint *": return _pywrapcp.IntervalVar_EndsAfterStartWithDelay(self, other, delay) def EndsAtEnd(self, other: "IntervalVar") -> "operations_research::Constraint *": return _pywrapcp.IntervalVar_EndsAtEnd(self, other) def EndsAtEndWithDelay(self, other: "IntervalVar", delay: "int64_t") -> "operations_research::Constraint *": return _pywrapcp.IntervalVar_EndsAtEndWithDelay(self, other, delay) def EndsAtStart(self, other: "IntervalVar") -> "operations_research::Constraint *": return _pywrapcp.IntervalVar_EndsAtStart(self, other) def EndsAtStartWithDelay(self, other: "IntervalVar", delay: "int64_t") -> "operations_research::Constraint *": return _pywrapcp.IntervalVar_EndsAtStartWithDelay(self, other, delay) def StartsAfterEnd(self, other: "IntervalVar") -> "operations_research::Constraint *": return _pywrapcp.IntervalVar_StartsAfterEnd(self, other) def StartsAfterEndWithDelay(self, other: "IntervalVar", delay: "int64_t") -> "operations_research::Constraint *": return _pywrapcp.IntervalVar_StartsAfterEndWithDelay(self, other, delay) def StartsAfterStart(self, other: "IntervalVar") -> "operations_research::Constraint *": return _pywrapcp.IntervalVar_StartsAfterStart(self, other) def StartsAfterStartWithDelay(self, other: "IntervalVar", delay: "int64_t") -> "operations_research::Constraint *": return _pywrapcp.IntervalVar_StartsAfterStartWithDelay(self, other, delay) def StartsAtEnd(self, other: "IntervalVar") -> "operations_research::Constraint *": return _pywrapcp.IntervalVar_StartsAtEnd(self, other) def StartsAtEndWithDelay(self, other: "IntervalVar", delay: "int64_t") -> "operations_research::Constraint *": return _pywrapcp.IntervalVar_StartsAtEndWithDelay(self, other, delay) def StartsAtStart(self, other: "IntervalVar") -> "operations_research::Constraint *": return _pywrapcp.IntervalVar_StartsAtStart(self, other) def StartsAtStartWithDelay(self, other: "IntervalVar", delay: "int64_t") -> "operations_research::Constraint *": return _pywrapcp.IntervalVar_StartsAtStartWithDelay(self, other, delay) def StaysInSync(self, other: "IntervalVar") -> "operations_research::Constraint *": return _pywrapcp.IntervalVar_StaysInSync(self, other) def StaysInSyncWithDelay(self, other: "IntervalVar", delay: "int64_t") -> "operations_research::Constraint *": return _pywrapcp.IntervalVar_StaysInSyncWithDelay(self, other, delay) def EndsAfter(self, date: "int64_t") -> "operations_research::Constraint *": return _pywrapcp.IntervalVar_EndsAfter(self, date) def EndsAt(self, date: "int64_t") -> "operations_research::Constraint *": return _pywrapcp.IntervalVar_EndsAt(self, date) def EndsBefore(self, date: "int64_t") -> "operations_research::Constraint *": return _pywrapcp.IntervalVar_EndsBefore(self, date) def StartsAfter(self, date: "int64_t") -> "operations_research::Constraint *": return _pywrapcp.IntervalVar_StartsAfter(self, date) def StartsAt(self, date: "int64_t") -> "operations_research::Constraint *": return _pywrapcp.IntervalVar_StartsAt(self, date) def StartsBefore(self, date: "int64_t") -> "operations_research::Constraint *": return _pywrapcp.IntervalVar_StartsBefore(self, date) def CrossesDate(self, date: "int64_t") -> "operations_research::Constraint *": return _pywrapcp.IntervalVar_CrossesDate(self, date) def AvoidsDate(self, date: "int64_t") -> "operations_research::Constraint *": return _pywrapcp.IntervalVar_AvoidsDate(self, date) def __repr__(self) -> "std::string": return _pywrapcp.IntervalVar___repr__(self) def __str__(self) -> "std::string": return _pywrapcp.IntervalVar___str__(self)Ancestors
Methods
def AvoidsDate(self, date: int64_t) ‑> operations_research::Constraint *-
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def AvoidsDate(self, date: "int64_t") -> "operations_research::Constraint *": return _pywrapcp.IntervalVar_AvoidsDate(self, date) def CannotBePerformed(self) ‑> bool-
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def CannotBePerformed(self) -> "bool": return _pywrapcp.IntervalVar_CannotBePerformed(self) def CrossesDate(self, date: int64_t) ‑> operations_research::Constraint *-
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def CrossesDate(self, date: "int64_t") -> "operations_research::Constraint *": return _pywrapcp.IntervalVar_CrossesDate(self, date) def DurationExpr(self) ‑> operations_research::IntExpr *-
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def DurationExpr(self) -> "operations_research::IntExpr *": return _pywrapcp.IntervalVar_DurationExpr(self) def DurationMax(self) ‑> int64_t-
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def DurationMax(self) -> "int64_t": return _pywrapcp.IntervalVar_DurationMax(self) def DurationMin(self) ‑> int64_t-
These methods query, set, and watch the duration of the interval var.
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def DurationMin(self) -> "int64_t": r""" These methods query, set, and watch the duration of the interval var.""" return _pywrapcp.IntervalVar_DurationMin(self) def EndExpr(self) ‑> operations_research::IntExpr *-
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def EndExpr(self) -> "operations_research::IntExpr *": return _pywrapcp.IntervalVar_EndExpr(self) def EndMax(self) ‑> int64_t-
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def EndMax(self) -> "int64_t": return _pywrapcp.IntervalVar_EndMax(self) def EndMin(self) ‑> int64_t-
These methods query, set, and watch the end position of the interval var.
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def EndMin(self) -> "int64_t": r""" These methods query, set, and watch the end position of the interval var.""" return _pywrapcp.IntervalVar_EndMin(self) def EndsAfter(self, date: int64_t) ‑> operations_research::Constraint *-
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def EndsAfter(self, date: "int64_t") -> "operations_research::Constraint *": return _pywrapcp.IntervalVar_EndsAfter(self, date) def EndsAfterEnd(self, other: IntervalVar) ‑> operations_research::Constraint *-
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def EndsAfterEnd(self, other: "IntervalVar") -> "operations_research::Constraint *": return _pywrapcp.IntervalVar_EndsAfterEnd(self, other) def EndsAfterEndWithDelay(self, other: IntervalVar, delay: int64_t) ‑> operations_research::Constraint *-
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def EndsAfterEndWithDelay(self, other: "IntervalVar", delay: "int64_t") -> "operations_research::Constraint *": return _pywrapcp.IntervalVar_EndsAfterEndWithDelay(self, other, delay) def EndsAfterStart(self, other: IntervalVar) ‑> operations_research::Constraint *-
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def EndsAfterStart(self, other: "IntervalVar") -> "operations_research::Constraint *": return _pywrapcp.IntervalVar_EndsAfterStart(self, other) def EndsAfterStartWithDelay(self, other: IntervalVar, delay: int64_t) ‑> operations_research::Constraint *-
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def EndsAfterStartWithDelay(self, other: "IntervalVar", delay: "int64_t") -> "operations_research::Constraint *": return _pywrapcp.IntervalVar_EndsAfterStartWithDelay(self, other, delay) def EndsAt(self, date: int64_t) ‑> operations_research::Constraint *-
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def EndsAt(self, date: "int64_t") -> "operations_research::Constraint *": return _pywrapcp.IntervalVar_EndsAt(self, date) def EndsAtEnd(self, other: IntervalVar) ‑> operations_research::Constraint *-
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def EndsAtEnd(self, other: "IntervalVar") -> "operations_research::Constraint *": return _pywrapcp.IntervalVar_EndsAtEnd(self, other) def EndsAtEndWithDelay(self, other: IntervalVar, delay: int64_t) ‑> operations_research::Constraint *-
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def EndsAtEndWithDelay(self, other: "IntervalVar", delay: "int64_t") -> "operations_research::Constraint *": return _pywrapcp.IntervalVar_EndsAtEndWithDelay(self, other, delay) def EndsAtStart(self, other: IntervalVar) ‑> operations_research::Constraint *-
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def EndsAtStart(self, other: "IntervalVar") -> "operations_research::Constraint *": return _pywrapcp.IntervalVar_EndsAtStart(self, other) def EndsAtStartWithDelay(self, other: IntervalVar, delay: int64_t) ‑> operations_research::Constraint *-
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def EndsAtStartWithDelay(self, other: "IntervalVar", delay: "int64_t") -> "operations_research::Constraint *": return _pywrapcp.IntervalVar_EndsAtStartWithDelay(self, other, delay) def EndsBefore(self, date: int64_t) ‑> operations_research::Constraint *-
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def EndsBefore(self, date: "int64_t") -> "operations_research::Constraint *": return _pywrapcp.IntervalVar_EndsBefore(self, date) def IsPerformedBound(self) ‑> bool-
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def IsPerformedBound(self) -> "bool": return _pywrapcp.IntervalVar_IsPerformedBound(self) def MayBePerformed(self) ‑> bool-
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def MayBePerformed(self) -> "bool": return _pywrapcp.IntervalVar_MayBePerformed(self) def MustBePerformed(self) ‑> bool-
These methods query, set, and watch the performed status of the interval var.
Expand source code
def MustBePerformed(self) -> "bool": r""" These methods query, set, and watch the performed status of the interval var. """ return _pywrapcp.IntervalVar_MustBePerformed(self) def OldDurationMax(self) ‑> int64_t-
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def OldDurationMax(self) -> "int64_t": return _pywrapcp.IntervalVar_OldDurationMax(self) def OldDurationMin(self) ‑> int64_t-
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def OldDurationMin(self) -> "int64_t": return _pywrapcp.IntervalVar_OldDurationMin(self) def OldEndMax(self) ‑> int64_t-
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def OldEndMax(self) -> "int64_t": return _pywrapcp.IntervalVar_OldEndMax(self) def OldEndMin(self) ‑> int64_t-
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def OldEndMin(self) -> "int64_t": return _pywrapcp.IntervalVar_OldEndMin(self) def OldStartMax(self) ‑> int64_t-
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def OldStartMax(self) -> "int64_t": return _pywrapcp.IntervalVar_OldStartMax(self) def OldStartMin(self) ‑> int64_t-
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def OldStartMin(self) -> "int64_t": return _pywrapcp.IntervalVar_OldStartMin(self) def PerformedExpr(self) ‑> operations_research::IntExpr *-
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def PerformedExpr(self) -> "operations_research::IntExpr *": return _pywrapcp.IntervalVar_PerformedExpr(self) def SafeDurationExpr(self, unperformed_value: int64_t) ‑> operations_research::IntExpr *-
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def SafeDurationExpr(self, unperformed_value: "int64_t") -> "operations_research::IntExpr *": return _pywrapcp.IntervalVar_SafeDurationExpr(self, unperformed_value) def SafeEndExpr(self, unperformed_value: int64_t) ‑> operations_research::IntExpr *-
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def SafeEndExpr(self, unperformed_value: "int64_t") -> "operations_research::IntExpr *": return _pywrapcp.IntervalVar_SafeEndExpr(self, unperformed_value) def SafeStartExpr(self, unperformed_value: int64_t) ‑> operations_research::IntExpr *-
These methods create expressions encapsulating the start, end and duration of the interval var. If the interval var is unperformed, they will return the unperformed_value.
Expand source code
def SafeStartExpr(self, unperformed_value: "int64_t") -> "operations_research::IntExpr *": r""" These methods create expressions encapsulating the start, end and duration of the interval var. If the interval var is unperformed, they will return the unperformed_value. """ return _pywrapcp.IntervalVar_SafeStartExpr(self, unperformed_value) def SetDurationMax(self, m: int64_t) ‑> void-
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def SetDurationMax(self, m: "int64_t") -> "void": return _pywrapcp.IntervalVar_SetDurationMax(self, m) def SetDurationMin(self, m: int64_t) ‑> void-
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def SetDurationMin(self, m: "int64_t") -> "void": return _pywrapcp.IntervalVar_SetDurationMin(self, m) def SetDurationRange(self, mi: int64_t, ma: int64_t) ‑> void-
Expand source code
def SetDurationRange(self, mi: "int64_t", ma: "int64_t") -> "void": return _pywrapcp.IntervalVar_SetDurationRange(self, mi, ma) def SetEndMax(self, m: int64_t) ‑> void-
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def SetEndMax(self, m: "int64_t") -> "void": return _pywrapcp.IntervalVar_SetEndMax(self, m) def SetEndMin(self, m: int64_t) ‑> void-
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def SetEndMin(self, m: "int64_t") -> "void": return _pywrapcp.IntervalVar_SetEndMin(self, m) def SetEndRange(self, mi: int64_t, ma: int64_t) ‑> void-
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def SetEndRange(self, mi: "int64_t", ma: "int64_t") -> "void": return _pywrapcp.IntervalVar_SetEndRange(self, mi, ma) def SetPerformed(self, val: bool) ‑> void-
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def SetPerformed(self, val: "bool") -> "void": return _pywrapcp.IntervalVar_SetPerformed(self, val) def SetStartMax(self, m: int64_t) ‑> void-
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def SetStartMax(self, m: "int64_t") -> "void": return _pywrapcp.IntervalVar_SetStartMax(self, m) def SetStartMin(self, m: int64_t) ‑> void-
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def SetStartMin(self, m: "int64_t") -> "void": return _pywrapcp.IntervalVar_SetStartMin(self, m) def SetStartRange(self, mi: int64_t, ma: int64_t) ‑> void-
Expand source code
def SetStartRange(self, mi: "int64_t", ma: "int64_t") -> "void": return _pywrapcp.IntervalVar_SetStartRange(self, mi, ma) def StartExpr(self) ‑> operations_research::IntExpr *-
These methods create expressions encapsulating the start, end and duration of the interval var. Please note that these must not be used if the interval var is unperformed.
Expand source code
def StartExpr(self) -> "operations_research::IntExpr *": r""" These methods create expressions encapsulating the start, end and duration of the interval var. Please note that these must not be used if the interval var is unperformed. """ return _pywrapcp.IntervalVar_StartExpr(self) def StartMax(self) ‑> int64_t-
Expand source code
def StartMax(self) -> "int64_t": return _pywrapcp.IntervalVar_StartMax(self) def StartMin(self) ‑> int64_t-
These methods query, set, and watch the start position of the interval var.
Expand source code
def StartMin(self) -> "int64_t": r""" These methods query, set, and watch the start position of the interval var. """ return _pywrapcp.IntervalVar_StartMin(self) def StartsAfter(self, date: int64_t) ‑> operations_research::Constraint *-
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def StartsAfter(self, date: "int64_t") -> "operations_research::Constraint *": return _pywrapcp.IntervalVar_StartsAfter(self, date) def StartsAfterEnd(self, other: IntervalVar) ‑> operations_research::Constraint *-
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def StartsAfterEnd(self, other: "IntervalVar") -> "operations_research::Constraint *": return _pywrapcp.IntervalVar_StartsAfterEnd(self, other) def StartsAfterEndWithDelay(self, other: IntervalVar, delay: int64_t) ‑> operations_research::Constraint *-
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def StartsAfterEndWithDelay(self, other: "IntervalVar", delay: "int64_t") -> "operations_research::Constraint *": return _pywrapcp.IntervalVar_StartsAfterEndWithDelay(self, other, delay) def StartsAfterStart(self, other: IntervalVar) ‑> operations_research::Constraint *-
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def StartsAfterStart(self, other: "IntervalVar") -> "operations_research::Constraint *": return _pywrapcp.IntervalVar_StartsAfterStart(self, other) def StartsAfterStartWithDelay(self, other: IntervalVar, delay: int64_t) ‑> operations_research::Constraint *-
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def StartsAfterStartWithDelay(self, other: "IntervalVar", delay: "int64_t") -> "operations_research::Constraint *": return _pywrapcp.IntervalVar_StartsAfterStartWithDelay(self, other, delay) def StartsAt(self, date: int64_t) ‑> operations_research::Constraint *-
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def StartsAt(self, date: "int64_t") -> "operations_research::Constraint *": return _pywrapcp.IntervalVar_StartsAt(self, date) def StartsAtEnd(self, other: IntervalVar) ‑> operations_research::Constraint *-
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def StartsAtEnd(self, other: "IntervalVar") -> "operations_research::Constraint *": return _pywrapcp.IntervalVar_StartsAtEnd(self, other) def StartsAtEndWithDelay(self, other: IntervalVar, delay: int64_t) ‑> operations_research::Constraint *-
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def StartsAtEndWithDelay(self, other: "IntervalVar", delay: "int64_t") -> "operations_research::Constraint *": return _pywrapcp.IntervalVar_StartsAtEndWithDelay(self, other, delay) def StartsAtStart(self, other: IntervalVar) ‑> operations_research::Constraint *-
Expand source code
def StartsAtStart(self, other: "IntervalVar") -> "operations_research::Constraint *": return _pywrapcp.IntervalVar_StartsAtStart(self, other) def StartsAtStartWithDelay(self, other: IntervalVar, delay: int64_t) ‑> operations_research::Constraint *-
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def StartsAtStartWithDelay(self, other: "IntervalVar", delay: "int64_t") -> "operations_research::Constraint *": return _pywrapcp.IntervalVar_StartsAtStartWithDelay(self, other, delay) def StartsBefore(self, date: int64_t) ‑> operations_research::Constraint *-
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def StartsBefore(self, date: "int64_t") -> "operations_research::Constraint *": return _pywrapcp.IntervalVar_StartsBefore(self, date) def StaysInSync(self, other: IntervalVar) ‑> operations_research::Constraint *-
Expand source code
def StaysInSync(self, other: "IntervalVar") -> "operations_research::Constraint *": return _pywrapcp.IntervalVar_StaysInSync(self, other) def StaysInSyncWithDelay(self, other: IntervalVar, delay: int64_t) ‑> operations_research::Constraint *-
Expand source code
def StaysInSyncWithDelay(self, other: "IntervalVar", delay: "int64_t") -> "operations_research::Constraint *": return _pywrapcp.IntervalVar_StaysInSyncWithDelay(self, other, delay) def WasPerformedBound(self) ‑> bool-
Expand source code
def WasPerformedBound(self) -> "bool": return _pywrapcp.IntervalVar_WasPerformedBound(self) def WhenAnything(self, *args) ‑> void-
Overload 1: Attaches a demon awakened when anything about this interval changes.
|
Overload 2: Attaches a closure awakened when anything about this interval changes.
Expand source code
def WhenAnything(self, *args) -> "void": r""" *Overload 1:* Attaches a demon awakened when anything about this interval changes. | *Overload 2:* Attaches a closure awakened when anything about this interval changes. """ return _pywrapcp.IntervalVar_WhenAnything(self, *args) def WhenDurationBound(self, *args) ‑> void-
Expand source code
def WhenDurationBound(self, *args) -> "void": return _pywrapcp.IntervalVar_WhenDurationBound(self, *args) def WhenDurationRange(self, *args) ‑> void-
Expand source code
def WhenDurationRange(self, *args) -> "void": return _pywrapcp.IntervalVar_WhenDurationRange(self, *args) def WhenEndBound(self, *args) ‑> void-
Expand source code
def WhenEndBound(self, *args) -> "void": return _pywrapcp.IntervalVar_WhenEndBound(self, *args) def WhenEndRange(self, *args) ‑> void-
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def WhenEndRange(self, *args) -> "void": return _pywrapcp.IntervalVar_WhenEndRange(self, *args) def WhenPerformedBound(self, *args) ‑> void-
Expand source code
def WhenPerformedBound(self, *args) -> "void": return _pywrapcp.IntervalVar_WhenPerformedBound(self, *args) def WhenStartBound(self, *args) ‑> void-
Expand source code
def WhenStartBound(self, *args) -> "void": return _pywrapcp.IntervalVar_WhenStartBound(self, *args) def WhenStartRange(self, *args) ‑> void-
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def WhenStartRange(self, *args) -> "void": return _pywrapcp.IntervalVar_WhenStartRange(self, *args)
Inherited members
class IntervalVarContainer (*args, **kwargs)-
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class IntervalVarContainer(object): thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined") __repr__ = _swig_repr def Contains(self, var: "IntervalVar") -> "bool": return _pywrapcp.IntervalVarContainer_Contains(self, var) def Element(self, index: "int") -> "operations_research::IntervalVarElement *": return _pywrapcp.IntervalVarContainer_Element(self, index) def Size(self) -> "int": return _pywrapcp.IntervalVarContainer_Size(self) def Store(self) -> "void": return _pywrapcp.IntervalVarContainer_Store(self) def Restore(self) -> "void": return _pywrapcp.IntervalVarContainer_Restore(self) def __eq__(self, container: "IntervalVarContainer") -> "bool": r""" Returns true if this and 'container' both represent the same V* -> E map. Runs in linear time; requires that the == operator on the type E is well defined. """ return _pywrapcp.IntervalVarContainer___eq__(self, container) def __ne__(self, container: "IntervalVarContainer") -> "bool": return _pywrapcp.IntervalVarContainer___ne__(self, container) __swig_destroy__ = _pywrapcp.delete_IntervalVarContainerInstance variables
var thisown-
The membership flag
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thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
Methods
def Contains(self, var: IntervalVar) ‑> bool-
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def Contains(self, var: "IntervalVar") -> "bool": return _pywrapcp.IntervalVarContainer_Contains(self, var) def Element(self, index: int) ‑> operations_research::IntervalVarElement *-
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def Element(self, index: "int") -> "operations_research::IntervalVarElement *": return _pywrapcp.IntervalVarContainer_Element(self, index) def Restore(self) ‑> void-
Expand source code
def Restore(self) -> "void": return _pywrapcp.IntervalVarContainer_Restore(self) def Size(self) ‑> int-
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def Size(self) -> "int": return _pywrapcp.IntervalVarContainer_Size(self) def Store(self) ‑> void-
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def Store(self) -> "void": return _pywrapcp.IntervalVarContainer_Store(self)
class IntervalVarElement (*args, **kwargs)-
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class IntervalVarElement(AssignmentElement): thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined") __repr__ = _swig_repr def Var(self) -> "operations_research::IntervalVar *": return _pywrapcp.IntervalVarElement_Var(self) def StartMin(self) -> "int64_t": return _pywrapcp.IntervalVarElement_StartMin(self) def StartMax(self) -> "int64_t": return _pywrapcp.IntervalVarElement_StartMax(self) def StartValue(self) -> "int64_t": return _pywrapcp.IntervalVarElement_StartValue(self) def DurationMin(self) -> "int64_t": return _pywrapcp.IntervalVarElement_DurationMin(self) def DurationMax(self) -> "int64_t": return _pywrapcp.IntervalVarElement_DurationMax(self) def DurationValue(self) -> "int64_t": return _pywrapcp.IntervalVarElement_DurationValue(self) def EndMin(self) -> "int64_t": return _pywrapcp.IntervalVarElement_EndMin(self) def EndMax(self) -> "int64_t": return _pywrapcp.IntervalVarElement_EndMax(self) def EndValue(self) -> "int64_t": return _pywrapcp.IntervalVarElement_EndValue(self) def PerformedMin(self) -> "int64_t": return _pywrapcp.IntervalVarElement_PerformedMin(self) def PerformedMax(self) -> "int64_t": return _pywrapcp.IntervalVarElement_PerformedMax(self) def PerformedValue(self) -> "int64_t": return _pywrapcp.IntervalVarElement_PerformedValue(self) def SetStartMin(self, m: "int64_t") -> "void": return _pywrapcp.IntervalVarElement_SetStartMin(self, m) def SetStartMax(self, m: "int64_t") -> "void": return _pywrapcp.IntervalVarElement_SetStartMax(self, m) def SetStartRange(self, mi: "int64_t", ma: "int64_t") -> "void": return _pywrapcp.IntervalVarElement_SetStartRange(self, mi, ma) def SetStartValue(self, v: "int64_t") -> "void": return _pywrapcp.IntervalVarElement_SetStartValue(self, v) def SetDurationMin(self, m: "int64_t") -> "void": return _pywrapcp.IntervalVarElement_SetDurationMin(self, m) def SetDurationMax(self, m: "int64_t") -> "void": return _pywrapcp.IntervalVarElement_SetDurationMax(self, m) def SetDurationRange(self, mi: "int64_t", ma: "int64_t") -> "void": return _pywrapcp.IntervalVarElement_SetDurationRange(self, mi, ma) def SetDurationValue(self, v: "int64_t") -> "void": return _pywrapcp.IntervalVarElement_SetDurationValue(self, v) def SetEndMin(self, m: "int64_t") -> "void": return _pywrapcp.IntervalVarElement_SetEndMin(self, m) def SetEndMax(self, m: "int64_t") -> "void": return _pywrapcp.IntervalVarElement_SetEndMax(self, m) def SetEndRange(self, mi: "int64_t", ma: "int64_t") -> "void": return _pywrapcp.IntervalVarElement_SetEndRange(self, mi, ma) def SetEndValue(self, v: "int64_t") -> "void": return _pywrapcp.IntervalVarElement_SetEndValue(self, v) def SetPerformedMin(self, m: "int64_t") -> "void": return _pywrapcp.IntervalVarElement_SetPerformedMin(self, m) def SetPerformedMax(self, m: "int64_t") -> "void": return _pywrapcp.IntervalVarElement_SetPerformedMax(self, m) def SetPerformedRange(self, mi: "int64_t", ma: "int64_t") -> "void": return _pywrapcp.IntervalVarElement_SetPerformedRange(self, mi, ma) def SetPerformedValue(self, v: "int64_t") -> "void": return _pywrapcp.IntervalVarElement_SetPerformedValue(self, v) def __eq__(self, element: "IntervalVarElement") -> "bool": return _pywrapcp.IntervalVarElement___eq__(self, element) def __ne__(self, element: "IntervalVarElement") -> "bool": return _pywrapcp.IntervalVarElement___ne__(self, element) __swig_destroy__ = _pywrapcp.delete_IntervalVarElementAncestors
Methods
def DurationMax(self) ‑> int64_t-
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def DurationMax(self) -> "int64_t": return _pywrapcp.IntervalVarElement_DurationMax(self) def DurationMin(self) ‑> int64_t-
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def DurationMin(self) -> "int64_t": return _pywrapcp.IntervalVarElement_DurationMin(self) def DurationValue(self) ‑> int64_t-
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def DurationValue(self) -> "int64_t": return _pywrapcp.IntervalVarElement_DurationValue(self) def EndMax(self) ‑> int64_t-
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def EndMax(self) -> "int64_t": return _pywrapcp.IntervalVarElement_EndMax(self) def EndMin(self) ‑> int64_t-
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def EndMin(self) -> "int64_t": return _pywrapcp.IntervalVarElement_EndMin(self) def EndValue(self) ‑> int64_t-
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def EndValue(self) -> "int64_t": return _pywrapcp.IntervalVarElement_EndValue(self) def PerformedMax(self) ‑> int64_t-
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def PerformedMax(self) -> "int64_t": return _pywrapcp.IntervalVarElement_PerformedMax(self) def PerformedMin(self) ‑> int64_t-
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def PerformedMin(self) -> "int64_t": return _pywrapcp.IntervalVarElement_PerformedMin(self) def PerformedValue(self) ‑> int64_t-
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def PerformedValue(self) -> "int64_t": return _pywrapcp.IntervalVarElement_PerformedValue(self) def SetDurationMax(self, m: int64_t) ‑> void-
Expand source code
def SetDurationMax(self, m: "int64_t") -> "void": return _pywrapcp.IntervalVarElement_SetDurationMax(self, m) def SetDurationMin(self, m: int64_t) ‑> void-
Expand source code
def SetDurationMin(self, m: "int64_t") -> "void": return _pywrapcp.IntervalVarElement_SetDurationMin(self, m) def SetDurationRange(self, mi: int64_t, ma: int64_t) ‑> void-
Expand source code
def SetDurationRange(self, mi: "int64_t", ma: "int64_t") -> "void": return _pywrapcp.IntervalVarElement_SetDurationRange(self, mi, ma) def SetDurationValue(self, v: int64_t) ‑> void-
Expand source code
def SetDurationValue(self, v: "int64_t") -> "void": return _pywrapcp.IntervalVarElement_SetDurationValue(self, v) def SetEndMax(self, m: int64_t) ‑> void-
Expand source code
def SetEndMax(self, m: "int64_t") -> "void": return _pywrapcp.IntervalVarElement_SetEndMax(self, m) def SetEndMin(self, m: int64_t) ‑> void-
Expand source code
def SetEndMin(self, m: "int64_t") -> "void": return _pywrapcp.IntervalVarElement_SetEndMin(self, m) def SetEndRange(self, mi: int64_t, ma: int64_t) ‑> void-
Expand source code
def SetEndRange(self, mi: "int64_t", ma: "int64_t") -> "void": return _pywrapcp.IntervalVarElement_SetEndRange(self, mi, ma) def SetEndValue(self, v: int64_t) ‑> void-
Expand source code
def SetEndValue(self, v: "int64_t") -> "void": return _pywrapcp.IntervalVarElement_SetEndValue(self, v) def SetPerformedMax(self, m: int64_t) ‑> void-
Expand source code
def SetPerformedMax(self, m: "int64_t") -> "void": return _pywrapcp.IntervalVarElement_SetPerformedMax(self, m) def SetPerformedMin(self, m: int64_t) ‑> void-
Expand source code
def SetPerformedMin(self, m: "int64_t") -> "void": return _pywrapcp.IntervalVarElement_SetPerformedMin(self, m) def SetPerformedRange(self, mi: int64_t, ma: int64_t) ‑> void-
Expand source code
def SetPerformedRange(self, mi: "int64_t", ma: "int64_t") -> "void": return _pywrapcp.IntervalVarElement_SetPerformedRange(self, mi, ma) def SetPerformedValue(self, v: int64_t) ‑> void-
Expand source code
def SetPerformedValue(self, v: "int64_t") -> "void": return _pywrapcp.IntervalVarElement_SetPerformedValue(self, v) def SetStartMax(self, m: int64_t) ‑> void-
Expand source code
def SetStartMax(self, m: "int64_t") -> "void": return _pywrapcp.IntervalVarElement_SetStartMax(self, m) def SetStartMin(self, m: int64_t) ‑> void-
Expand source code
def SetStartMin(self, m: "int64_t") -> "void": return _pywrapcp.IntervalVarElement_SetStartMin(self, m) def SetStartRange(self, mi: int64_t, ma: int64_t) ‑> void-
Expand source code
def SetStartRange(self, mi: "int64_t", ma: "int64_t") -> "void": return _pywrapcp.IntervalVarElement_SetStartRange(self, mi, ma) def SetStartValue(self, v: int64_t) ‑> void-
Expand source code
def SetStartValue(self, v: "int64_t") -> "void": return _pywrapcp.IntervalVarElement_SetStartValue(self, v) def StartMax(self) ‑> int64_t-
Expand source code
def StartMax(self) -> "int64_t": return _pywrapcp.IntervalVarElement_StartMax(self) def StartMin(self) ‑> int64_t-
Expand source code
def StartMin(self) -> "int64_t": return _pywrapcp.IntervalVarElement_StartMin(self) def StartValue(self) ‑> int64_t-
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def StartValue(self) -> "int64_t": return _pywrapcp.IntervalVarElement_StartValue(self) def Var(self) ‑> operations_research::IntervalVar *-
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def Var(self) -> "operations_research::IntervalVar *": return _pywrapcp.IntervalVarElement_Var(self)
Inherited members
class LocalSearchFilter (*args, **kwargs)-
Classes to which this template function can be applied to as of 04/2014. Usage: LocalSearchOperator op = MakeLocalSearchOperator
(…); class TwoOpt; class Relocate; class Exchange; class Cross; class MakeActiveOperator; class MakeInactiveOperator; class MakeChainInactiveOperator; class SwapActiveOperator; class ExtendedSwapActiveOperator; class MakeActiveAndRelocate; class RelocateAndMakeActiveOperator; class RelocateAndMakeInactiveOperator; Local Search Filters are used for fast neighbor pruning. Filtering a move is done in several phases: - in the Relax phase, filters determine which parts of their internals will be changed by the candidate, and modify intermediary State - in the Accept phase, filters check that the candidate is feasible, - if the Accept phase succeeds, the solver may decide to trigger a Synchronize phase that makes filters change their internal representation to the last candidate, - otherwise (Accept fails or the solver does not want to synchronize), a Revert phase makes filters erase any intermediary State generated by the Relax and Accept phases. A given filter has phases called with the following pattern: (Relax.Accept.Synchronize | Relax.Accept.Revert | Relax.Revert) . Filters's Revert() is always called in the reverse order their Accept() was called, to allow late filters to use state done/undone by early filters' Accept()/Revert().Expand source code
class LocalSearchFilter(BaseObject): r""" Classes to which this template function can be applied to as of 04/2014. Usage: LocalSearchOperator* op = MakeLocalSearchOperator<Relocate>(...); class TwoOpt; class Relocate; class Exchange; class Cross; class MakeActiveOperator; class MakeInactiveOperator; class MakeChainInactiveOperator; class SwapActiveOperator; class ExtendedSwapActiveOperator; class MakeActiveAndRelocate; class RelocateAndMakeActiveOperator; class RelocateAndMakeInactiveOperator; Local Search Filters are used for fast neighbor pruning. Filtering a move is done in several phases: - in the Relax phase, filters determine which parts of their internals will be changed by the candidate, and modify intermediary State - in the Accept phase, filters check that the candidate is feasible, - if the Accept phase succeeds, the solver may decide to trigger a Synchronize phase that makes filters change their internal representation to the last candidate, - otherwise (Accept fails or the solver does not want to synchronize), a Revert phase makes filters erase any intermediary State generated by the Relax and Accept phases. A given filter has phases called with the following pattern: (Relax.Accept.Synchronize | Relax.Accept.Revert | Relax.Revert)*. Filters's Revert() is always called in the reverse order their Accept() was called, to allow late filters to use state done/undone by early filters' Accept()/Revert(). """ thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined - class is abstract") __repr__ = _swig_repr def Accept(self, delta: "Assignment", deltadelta: "Assignment", objective_min: "int64_t", objective_max: "int64_t") -> "bool": r""" Accepts a "delta" given the assignment with which the filter has been synchronized; the delta holds the variables which have been modified and their new value. If the filter represents a part of the global objective, its contribution must be between objective_min and objective_max. Sample: supposing one wants to maintain a[0,1] + b[0,1] <= 1, for the assignment (a,1), (b,0), the delta (b,1) will be rejected but the delta (a,0) will be accepted. TODO(user): Remove arguments when there are no more need for those. """ return _pywrapcp.LocalSearchFilter_Accept(self, delta, deltadelta, objective_min, objective_max) def IsIncremental(self) -> "bool": return _pywrapcp.LocalSearchFilter_IsIncremental(self) def Synchronize(self, assignment: "Assignment", delta: "Assignment") -> "void": r""" Synchronizes the filter with the current solution, delta being the difference with the solution passed to the previous call to Synchronize() or IncrementalSynchronize(). 'delta' can be used to incrementally synchronizing the filter with the new solution by only considering the changes in delta. """ return _pywrapcp.LocalSearchFilter_Synchronize(self, assignment, delta) __swig_destroy__ = _pywrapcp.delete_LocalSearchFilterAncestors
Subclasses
Methods
def Accept(self, delta: Assignment, deltadelta: Assignment, objective_min: int64_t, objective_max: int64_t) ‑> bool-
Accepts a "delta" given the assignment with which the filter has been synchronized; the delta holds the variables which have been modified and their new value. If the filter represents a part of the global objective, its contribution must be between objective_min and objective_max. Sample: supposing one wants to maintain a[0,1] + b[0,1] <= 1, for the assignment (a,1), (b,0), the delta (b,1) will be rejected but the delta (a,0) will be accepted. TODO(user): Remove arguments when there are no more need for those.
Expand source code
def Accept(self, delta: "Assignment", deltadelta: "Assignment", objective_min: "int64_t", objective_max: "int64_t") -> "bool": r""" Accepts a "delta" given the assignment with which the filter has been synchronized; the delta holds the variables which have been modified and their new value. If the filter represents a part of the global objective, its contribution must be between objective_min and objective_max. Sample: supposing one wants to maintain a[0,1] + b[0,1] <= 1, for the assignment (a,1), (b,0), the delta (b,1) will be rejected but the delta (a,0) will be accepted. TODO(user): Remove arguments when there are no more need for those. """ return _pywrapcp.LocalSearchFilter_Accept(self, delta, deltadelta, objective_min, objective_max) def IsIncremental(self) ‑> bool-
Expand source code
def IsIncremental(self) -> "bool": return _pywrapcp.LocalSearchFilter_IsIncremental(self) def Synchronize(self, assignment: Assignment, delta: Assignment) ‑> void-
Synchronizes the filter with the current solution, delta being the difference with the solution passed to the previous call to Synchronize() or IncrementalSynchronize(). 'delta' can be used to incrementally synchronizing the filter with the new solution by only considering the changes in delta.
Expand source code
def Synchronize(self, assignment: "Assignment", delta: "Assignment") -> "void": r""" Synchronizes the filter with the current solution, delta being the difference with the solution passed to the previous call to Synchronize() or IncrementalSynchronize(). 'delta' can be used to incrementally synchronizing the filter with the new solution by only considering the changes in delta. """ return _pywrapcp.LocalSearchFilter_Synchronize(self, assignment, delta)
Inherited members
class LocalSearchFilterManager (*args)-
Filter manager: when a move is made, filters are executed to decide whether the solution is feasible and compute parts of the new cost. This class schedules filter execution and composes costs as a sum.
Expand source code
class LocalSearchFilterManager(BaseObject): r""" Filter manager: when a move is made, filters are executed to decide whether the solution is feasible and compute parts of the new cost. This class schedules filter execution and composes costs as a sum. """ thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") __repr__ = _swig_repr def DebugString(self) -> "std::string": return _pywrapcp.LocalSearchFilterManager_DebugString(self) def __init__(self, *args): _pywrapcp.LocalSearchFilterManager_swiginit(self, _pywrapcp.new_LocalSearchFilterManager(*args)) def Accept(self, monitor: "operations_research::LocalSearchMonitor *const", delta: "Assignment", deltadelta: "Assignment", objective_min: "int64_t", objective_max: "int64_t") -> "bool": r""" Returns true iff all filters return true, and the sum of their accepted objectives is between objective_min and objective_max. The monitor has its Begin/EndFiltering events triggered. """ return _pywrapcp.LocalSearchFilterManager_Accept(self, monitor, delta, deltadelta, objective_min, objective_max) def Synchronize(self, assignment: "Assignment", delta: "Assignment") -> "void": r""" Synchronizes all filters to assignment.""" return _pywrapcp.LocalSearchFilterManager_Synchronize(self, assignment, delta) __swig_destroy__ = _pywrapcp.delete_LocalSearchFilterManagerAncestors
Methods
def Accept(self, monitor: operations_research::LocalSearchMonitor *const, delta: Assignment, deltadelta: Assignment, objective_min: int64_t, objective_max: int64_t) ‑> bool-
Returns true iff all filters return true, and the sum of their accepted objectives is between objective_min and objective_max. The monitor has its Begin/EndFiltering events triggered.
Expand source code
def Accept(self, monitor: "operations_research::LocalSearchMonitor *const", delta: "Assignment", deltadelta: "Assignment", objective_min: "int64_t", objective_max: "int64_t") -> "bool": r""" Returns true iff all filters return true, and the sum of their accepted objectives is between objective_min and objective_max. The monitor has its Begin/EndFiltering events triggered. """ return _pywrapcp.LocalSearchFilterManager_Accept(self, monitor, delta, deltadelta, objective_min, objective_max) def DebugString(self) ‑> std::string-
Expand source code
def DebugString(self) -> "std::string": return _pywrapcp.LocalSearchFilterManager_DebugString(self) def Synchronize(self, assignment: Assignment, delta: Assignment) ‑> void-
Synchronizes all filters to assignment.
Expand source code
def Synchronize(self, assignment: "Assignment", delta: "Assignment") -> "void": r""" Synchronizes all filters to assignment.""" return _pywrapcp.LocalSearchFilterManager_Synchronize(self, assignment, delta)
Inherited members
class LocalSearchOperator (*args, **kwargs)-
This class represent a reversible FIFO structure. The main difference w.r.t a standard FIFO structure is that a Solver is given as parameter to the modifiers such that the solver can store the backtrack information Iterator's traversing order should not be changed, as some algorithm depend on it to be consistent. It's main use is to store a list of demons in the various classes of variables. The base class for all local search operators.
A local search operator is an object that defines the neighborhood of a solution. In other words, a neighborhood is the set of solutions which can be reached from a given solution using an operator.
The behavior of the LocalSearchOperator class is similar to iterators. The operator is synchronized with an assignment (gives the current values of the variables); this is done in the Start() method.
Then one can iterate over the neighbors using the MakeNextNeighbor method. This method returns an assignment which represents the incremental changes to the current solution. It also returns a second assignment representing the changes to the last solution defined by the neighborhood operator; this assignment is empty if the neighborhood operator cannot track this information.
Expand source code
class LocalSearchOperator(BaseObject): r""" This class represent a reversible FIFO structure. The main difference w.r.t a standard FIFO structure is that a Solver is given as parameter to the modifiers such that the solver can store the backtrack information Iterator's traversing order should not be changed, as some algorithm depend on it to be consistent. It's main use is to store a list of demons in the various classes of variables. The base class for all local search operators. A local search operator is an object that defines the neighborhood of a solution. In other words, a neighborhood is the set of solutions which can be reached from a given solution using an operator. The behavior of the LocalSearchOperator class is similar to iterators. The operator is synchronized with an assignment (gives the current values of the variables); this is done in the Start() method. Then one can iterate over the neighbors using the MakeNextNeighbor method. This method returns an assignment which represents the incremental changes to the current solution. It also returns a second assignment representing the changes to the last solution defined by the neighborhood operator; this assignment is empty if the neighborhood operator cannot track this information. """ thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined - class is abstract") __repr__ = _swig_repr def NextNeighbor(self, delta: "Assignment", deltadelta: "Assignment") -> "bool": return _pywrapcp.LocalSearchOperator_NextNeighbor(self, delta, deltadelta) def Start(self, assignment: "Assignment") -> "void": return _pywrapcp.LocalSearchOperator_Start(self, assignment) def __disown__(self): self.this.disown() _pywrapcp.disown_LocalSearchOperator(self) return weakref.proxy(self)Ancestors
Subclasses
Methods
def NextNeighbor(self, delta: Assignment, deltadelta: Assignment) ‑> bool-
Expand source code
def NextNeighbor(self, delta: "Assignment", deltadelta: "Assignment") -> "bool": return _pywrapcp.LocalSearchOperator_NextNeighbor(self, delta, deltadelta) def Start(self, assignment: Assignment) ‑> void-
Expand source code
def Start(self, assignment: "Assignment") -> "void": return _pywrapcp.LocalSearchOperator_Start(self, assignment)
Inherited members
class NumericalRevInteger (val: long const &)-
Subclass of Rev
which adds numerical operations. Expand source code
class NumericalRevInteger(RevInteger): r""" Subclass of Rev<T> which adds numerical operations.""" thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") __repr__ = _swig_repr def __init__(self, val: "long const &"): _pywrapcp.NumericalRevInteger_swiginit(self, _pywrapcp.new_NumericalRevInteger(val)) def Add(self, s: "Solver", to_add: "long const &") -> "void": return _pywrapcp.NumericalRevInteger_Add(self, s, to_add) def Incr(self, s: "Solver") -> "void": return _pywrapcp.NumericalRevInteger_Incr(self, s) def Decr(self, s: "Solver") -> "void": return _pywrapcp.NumericalRevInteger_Decr(self, s) __swig_destroy__ = _pywrapcp.delete_NumericalRevIntegerAncestors
Methods
def Add(self, s: Solver, to_add: long const &) ‑> void-
Expand source code
def Add(self, s: "Solver", to_add: "long const &") -> "void": return _pywrapcp.NumericalRevInteger_Add(self, s, to_add) def Decr(self, s: Solver) ‑> void-
Expand source code
def Decr(self, s: "Solver") -> "void": return _pywrapcp.NumericalRevInteger_Decr(self, s) def Incr(self, s: Solver) ‑> void-
Expand source code
def Incr(self, s: "Solver") -> "void": return _pywrapcp.NumericalRevInteger_Incr(self, s)
Inherited members
class OptimizeVar (*args, **kwargs)-
This class encapsulates an objective. It requires the direction (minimize or maximize), the variable to optimize, and the improvement step.
Expand source code
class OptimizeVar(SearchMonitor): r""" This class encapsulates an objective. It requires the direction (minimize or maximize), the variable to optimize, and the improvement step. """ thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined") __repr__ = _swig_repr def Best(self) -> "int64_t": r""" Returns the best value found during search.""" return _pywrapcp.OptimizeVar_Best(self) def Var(self) -> "operations_research::IntVar *": r""" Returns the variable that is optimized.""" return _pywrapcp.OptimizeVar_Var(self) def AcceptDelta(self, delta: "Assignment", deltadelta: "Assignment") -> "bool": r""" Internal methods.""" return _pywrapcp.OptimizeVar_AcceptDelta(self, delta, deltadelta) def EnterSearch(self) -> "void": return _pywrapcp.OptimizeVar_EnterSearch(self) def BeginNextDecision(self, db: "DecisionBuilder") -> "void": return _pywrapcp.OptimizeVar_BeginNextDecision(self, db) def RefuteDecision(self, d: "Decision") -> "void": return _pywrapcp.OptimizeVar_RefuteDecision(self, d) def AtSolution(self) -> "bool": return _pywrapcp.OptimizeVar_AtSolution(self) def AcceptSolution(self) -> "bool": return _pywrapcp.OptimizeVar_AcceptSolution(self) def DebugString(self) -> "std::string": return _pywrapcp.OptimizeVar_DebugString(self)Ancestors
Methods
def AcceptDelta(self, delta: Assignment, deltadelta: Assignment) ‑> bool-
Internal methods.
Expand source code
def AcceptDelta(self, delta: "Assignment", deltadelta: "Assignment") -> "bool": r""" Internal methods.""" return _pywrapcp.OptimizeVar_AcceptDelta(self, delta, deltadelta) def Best(self) ‑> int64_t-
Returns the best value found during search.
Expand source code
def Best(self) -> "int64_t": r""" Returns the best value found during search.""" return _pywrapcp.OptimizeVar_Best(self) def DebugString(self) ‑> std::string-
Expand source code
def DebugString(self) -> "std::string": return _pywrapcp.OptimizeVar_DebugString(self) def Var(self) ‑> operations_research::IntVar *-
Returns the variable that is optimized.
Expand source code
def Var(self) -> "operations_research::IntVar *": r""" Returns the variable that is optimized.""" return _pywrapcp.OptimizeVar_Var(self)
Inherited members
class Pack (*args, **kwargs)-
A constraint is the main modeling object. It provides two methods: - Post() is responsible for creating the demons and attaching them to immediate demons(). - InitialPropagate() is called once just after Post and performs the initial propagation. The subsequent propagations will be performed by the demons Posted during the post() method.
Expand source code
class Pack(Constraint): thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined") __repr__ = _swig_repr def AddWeightedSumLessOrEqualConstantDimension(self, *args) -> "void": r""" *Overload 1:* Dimensions are additional constraints than can restrict what is possible with the pack constraint. It can be used to set capacity limits, to count objects per bin, to compute unassigned penalties... This dimension imposes that for all bins b, the weighted sum (weights[i]) of all objects i assigned to 'b' is less or equal 'bounds[b]'. | *Overload 2:* This dimension imposes that for all bins b, the weighted sum (weights->Run(i)) of all objects i assigned to 'b' is less or equal to 'bounds[b]'. Ownership of the callback is transferred to the pack constraint. | *Overload 3:* This dimension imposes that for all bins b, the weighted sum (weights->Run(i, b) of all objects i assigned to 'b' is less or equal to 'bounds[b]'. Ownership of the callback is transferred to the pack constraint. """ return _pywrapcp.Pack_AddWeightedSumLessOrEqualConstantDimension(self, *args) def AddWeightedSumEqualVarDimension(self, *args) -> "void": r""" *Overload 1:* This dimension imposes that for all bins b, the weighted sum (weights[i]) of all objects i assigned to 'b' is equal to loads[b]. | *Overload 2:* This dimension imposes that for all bins b, the weighted sum (weights->Run(i, b)) of all objects i assigned to 'b' is equal to loads[b]. """ return _pywrapcp.Pack_AddWeightedSumEqualVarDimension(self, *args) def AddSumVariableWeightsLessOrEqualConstantDimension(self, usage: "std::vector< operations_research::IntVar * > const &", capacity: "std::vector< int64_t > const &") -> "void": r""" This dimension imposes: forall b in bins, sum (i in items: usage[i] * is_assigned(i, b)) <= capacity[b] where is_assigned(i, b) is true if and only if item i is assigned to the bin b. This can be used to model shapes of items by linking variables of the same item on parallel dimensions with an allowed assignment constraint. """ return _pywrapcp.Pack_AddSumVariableWeightsLessOrEqualConstantDimension(self, usage, capacity) def AddWeightedSumOfAssignedDimension(self, weights: "std::vector< int64_t > const &", cost_var: "IntVar") -> "void": r""" This dimension enforces that cost_var == sum of weights[i] for all objects 'i' assigned to a bin. """ return _pywrapcp.Pack_AddWeightedSumOfAssignedDimension(self, weights, cost_var) def AddCountUsedBinDimension(self, count_var: "IntVar") -> "void": r""" This dimension links 'count_var' to the actual number of bins used in the pack. """ return _pywrapcp.Pack_AddCountUsedBinDimension(self, count_var) def AddCountAssignedItemsDimension(self, count_var: "IntVar") -> "void": r""" This dimension links 'count_var' to the actual number of items assigned to a bin in the pack. """ return _pywrapcp.Pack_AddCountAssignedItemsDimension(self, count_var) def Post(self) -> "void": return _pywrapcp.Pack_Post(self) def InitialPropagateWrapper(self) -> "void": return _pywrapcp.Pack_InitialPropagateWrapper(self) def DebugString(self) -> "std::string": return _pywrapcp.Pack_DebugString(self)Ancestors
Methods
def AddCountAssignedItemsDimension(self, count_var: IntVar) ‑> void-
This dimension links 'count_var' to the actual number of items assigned to a bin in the pack.
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def AddCountAssignedItemsDimension(self, count_var: "IntVar") -> "void": r""" This dimension links 'count_var' to the actual number of items assigned to a bin in the pack. """ return _pywrapcp.Pack_AddCountAssignedItemsDimension(self, count_var) def AddCountUsedBinDimension(self, count_var: IntVar) ‑> void-
This dimension links 'count_var' to the actual number of bins used in the pack.
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def AddCountUsedBinDimension(self, count_var: "IntVar") -> "void": r""" This dimension links 'count_var' to the actual number of bins used in the pack. """ return _pywrapcp.Pack_AddCountUsedBinDimension(self, count_var) def AddSumVariableWeightsLessOrEqualConstantDimension(self, usage: std::vector< operations_research::IntVar * > const &, capacity: std::vector< int64_t > const &) ‑> void-
This dimension imposes: forall b in bins, sum (i in items: usage[i] * is_assigned(i, b)) <= capacity[b] where is_assigned(i, b) is true if and only if item i is assigned to the bin b.
This can be used to model shapes of items by linking variables of the same item on parallel dimensions with an allowed assignment constraint.
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def AddSumVariableWeightsLessOrEqualConstantDimension(self, usage: "std::vector< operations_research::IntVar * > const &", capacity: "std::vector< int64_t > const &") -> "void": r""" This dimension imposes: forall b in bins, sum (i in items: usage[i] * is_assigned(i, b)) <= capacity[b] where is_assigned(i, b) is true if and only if item i is assigned to the bin b. This can be used to model shapes of items by linking variables of the same item on parallel dimensions with an allowed assignment constraint. """ return _pywrapcp.Pack_AddSumVariableWeightsLessOrEqualConstantDimension(self, usage, capacity) def AddWeightedSumEqualVarDimension(self, *args) ‑> void-
Overload 1: This dimension imposes that for all bins b, the weighted sum (weights[i]) of all objects i assigned to 'b' is equal to loads[b].
|
Overload 2: This dimension imposes that for all bins b, the weighted sum (weights->Run(i, b)) of all objects i assigned to 'b' is equal to loads[b].
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def AddWeightedSumEqualVarDimension(self, *args) -> "void": r""" *Overload 1:* This dimension imposes that for all bins b, the weighted sum (weights[i]) of all objects i assigned to 'b' is equal to loads[b]. | *Overload 2:* This dimension imposes that for all bins b, the weighted sum (weights->Run(i, b)) of all objects i assigned to 'b' is equal to loads[b]. """ return _pywrapcp.Pack_AddWeightedSumEqualVarDimension(self, *args) def AddWeightedSumLessOrEqualConstantDimension(self, *args) ‑> void-
Overload 1: Dimensions are additional constraints than can restrict what is possible with the pack constraint. It can be used to set capacity limits, to count objects per bin, to compute unassigned penalties… This dimension imposes that for all bins b, the weighted sum (weights[i]) of all objects i assigned to 'b' is less or equal 'bounds[b]'.
|
Overload 2: This dimension imposes that for all bins b, the weighted sum (weights->Run(i)) of all objects i assigned to 'b' is less or equal to 'bounds[b]'. Ownership of the callback is transferred to the pack constraint.
|
Overload 3: This dimension imposes that for all bins b, the weighted sum (weights->Run(i, b) of all objects i assigned to 'b' is less or equal to 'bounds[b]'. Ownership of the callback is transferred to the pack constraint.
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def AddWeightedSumLessOrEqualConstantDimension(self, *args) -> "void": r""" *Overload 1:* Dimensions are additional constraints than can restrict what is possible with the pack constraint. It can be used to set capacity limits, to count objects per bin, to compute unassigned penalties... This dimension imposes that for all bins b, the weighted sum (weights[i]) of all objects i assigned to 'b' is less or equal 'bounds[b]'. | *Overload 2:* This dimension imposes that for all bins b, the weighted sum (weights->Run(i)) of all objects i assigned to 'b' is less or equal to 'bounds[b]'. Ownership of the callback is transferred to the pack constraint. | *Overload 3:* This dimension imposes that for all bins b, the weighted sum (weights->Run(i, b) of all objects i assigned to 'b' is less or equal to 'bounds[b]'. Ownership of the callback is transferred to the pack constraint. """ return _pywrapcp.Pack_AddWeightedSumLessOrEqualConstantDimension(self, *args) def AddWeightedSumOfAssignedDimension(self, weights: std::vector< int64_t > const &, cost_var: IntVar) ‑> void-
This dimension enforces that cost_var == sum of weights[i] for all objects 'i' assigned to a bin.
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def AddWeightedSumOfAssignedDimension(self, weights: "std::vector< int64_t > const &", cost_var: "IntVar") -> "void": r""" This dimension enforces that cost_var == sum of weights[i] for all objects 'i' assigned to a bin. """ return _pywrapcp.Pack_AddWeightedSumOfAssignedDimension(self, weights, cost_var) def DebugString(self) ‑> std::string-
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def DebugString(self) -> "std::string": return _pywrapcp.Pack_DebugString(self)
Inherited members
class PathOperator (*args, **kwargs)-
Base class of the local search operators dedicated to path modifications (a path is a set of nodes linked together by arcs). This family of neighborhoods supposes they are handling next variables representing the arcs (var[i] represents the node immediately after i on a path). Several services are provided: - arc manipulators (SetNext(), ReverseChain(), MoveChain()) - path inspectors (Next(), Prev(), IsPathEnd()) - path iterators: operators need a given number of nodes to define a neighbor; this class provides the iteration on a given number of (base) nodes which can be used to define a neighbor (through the BaseNode method) Subclasses only need to override MakeNeighbor to create neighbors using the services above (no direct manipulation of assignments).
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class PathOperator(IntVarLocalSearchOperator): r""" Base class of the local search operators dedicated to path modifications (a path is a set of nodes linked together by arcs). This family of neighborhoods supposes they are handling next variables representing the arcs (var[i] represents the node immediately after i on a path). Several services are provided: - arc manipulators (SetNext(), ReverseChain(), MoveChain()) - path inspectors (Next(), Prev(), IsPathEnd()) - path iterators: operators need a given number of nodes to define a neighbor; this class provides the iteration on a given number of (base) nodes which can be used to define a neighbor (through the BaseNode method) Subclasses only need to override MakeNeighbor to create neighbors using the services above (no direct manipulation of assignments). """ thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined - class is abstract") __repr__ = _swig_repr def Neighbor(self) -> "bool": return _pywrapcp.PathOperator_Neighbor(self)Ancestors
Methods
def Neighbor(self) ‑> bool-
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def Neighbor(self) -> "bool": return _pywrapcp.PathOperator_Neighbor(self)
Inherited members
class PropagationBaseObject (s:Â Solver)-
NOLINT The PropagationBaseObject is a subclass of BaseObject that is also friend to the Solver class. It allows accessing methods useful when writing new constraints or new expressions.
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class PropagationBaseObject(BaseObject): r""" NOLINT The PropagationBaseObject is a subclass of BaseObject that is also friend to the Solver class. It allows accessing methods useful when writing new constraints or new expressions. """ thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") __repr__ = _swig_repr def __init__(self, s: "Solver"): if self.__class__ == PropagationBaseObject: _self = None else: _self = self _pywrapcp.PropagationBaseObject_swiginit(self, _pywrapcp.new_PropagationBaseObject(_self, s)) __swig_destroy__ = _pywrapcp.delete_PropagationBaseObject def DebugString(self) -> "std::string": return _pywrapcp.PropagationBaseObject_DebugString(self) def solver(self) -> "operations_research::Solver *": return _pywrapcp.PropagationBaseObject_solver(self) def Name(self) -> "std::string": r""" Object naming.""" return _pywrapcp.PropagationBaseObject_Name(self) def __disown__(self): self.this.disown() _pywrapcp.disown_PropagationBaseObject(self) return weakref.proxy(self)Ancestors
Subclasses
Methods
def DebugString(self) ‑> std::string-
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def DebugString(self) -> "std::string": return _pywrapcp.PropagationBaseObject_DebugString(self) def Name(self) ‑> std::string-
Object naming.
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def Name(self) -> "std::string": r""" Object naming.""" return _pywrapcp.PropagationBaseObject_Name(self) def solver(self) ‑> operations_research::Solver *-
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def solver(self) -> "operations_research::Solver *": return _pywrapcp.PropagationBaseObject_solver(self)
Inherited members
class PyConstraint (solver)-
A constraint is the main modeling object. It provides two methods: - Post() is responsible for creating the demons and attaching them to immediate demons(). - InitialPropagate() is called once just after Post and performs the initial propagation. The subsequent propagations will be performed by the demons Posted during the post() method.
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class PyConstraint(Constraint): def __init__(self, solver): Constraint.__init__(self, solver) self.__demons = [] def Demon(self, method, *args): demon = PyConstraintDemon(self, method, False, *args) self.__demons.append(demon) return demon def DelayedDemon(self, method, *args): demon = PyConstraintDemon(self, method, True, *args) self.__demons.append(demon) return demon def InitialPropagateDemon(self): return self.solver().ConstraintInitialPropagateCallback(self) def DelayedInitialPropagateDemon(self): return self.solver().DelayedConstraintInitialPropagateCallback(self) def InitialPropagateWrapper(self): try: self.InitialPropagate() except Exception as e: if 'CP Solver fail' in str(e): self.solver().ShouldFail() else: raise def DebugString(self): return "PyConstraint"Ancestors
Methods
def DebugString(self)-
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def DebugString(self): return "PyConstraint" def DelayedDemon(self, method, *args)-
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def DelayedDemon(self, method, *args): demon = PyConstraintDemon(self, method, True, *args) self.__demons.append(demon) return demon def DelayedInitialPropagateDemon(self)-
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def DelayedInitialPropagateDemon(self): return self.solver().DelayedConstraintInitialPropagateCallback(self) def Demon(self, method, *args)-
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def Demon(self, method, *args): demon = PyConstraintDemon(self, method, False, *args) self.__demons.append(demon) return demon def InitialPropagateDemon(self)-
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def InitialPropagateDemon(self): return self.solver().ConstraintInitialPropagateCallback(self)
Inherited members
class PyConstraintDemon (ct, method, delayed, *args)-
A Demon is the base element of a propagation queue. It is the main object responsible for implementing the actual propagation of the constraint and pruning the inconsistent values in the domains of the variables. The main concept is that demons are listeners that are attached to the variables and listen to their modifications. There are two methods: - Run() is the actual method called when the demon is processed. - priority() returns its priority. Standard priorities are slow, normal or fast. "immediate" is reserved for variables and is treated separately.
This indicates the priority of a demon. Immediate demons are treated separately and corresponds to variables.
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class PyConstraintDemon(PyDemon): def __init__(self, ct, method, delayed, *args): PyDemon.__init__(self) self.__constraint = ct self.__method = method self.__delayed = delayed self.__args = args def Run(self, solver): self.__method(self.__constraint, *self.__args) def Priority(self): return Solver.DELAYED_PRIORITY if self.__delayed else Solver.NORMAL_PRIORITY def DebugString(self): return 'PyConstraintDemon'Ancestors
Methods
def DebugString(self)-
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def DebugString(self): return 'PyConstraintDemon' def Run(self, solver)-
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def Run(self, solver): self.__method(self.__constraint, *self.__args)
Inherited members
class PyDecision-
A Decision represents a choice point in the search tree. The two main methods are Apply() to go left, or Refute() to go right.
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class PyDecision(Decision): def __init__(self): Decision.__init__(self) def ApplyWrapper(self, solver): try: self.Apply(solver) except Exception as e: if 'CP Solver fail' in str(e): solver.ShouldFail() else: raise def RefuteWrapper(self, solver): try: self.Refute(solver) except Exception as e: if 'CP Solver fail' in str(e): solver.ShouldFail() else: raise def DebugString(self): return "PyDecision"Ancestors
Methods
def DebugString(self)-
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def DebugString(self): return "PyDecision"
Inherited members
class PyDecisionBuilder-
A DecisionBuilder is responsible for creating the search tree. The important method is Next(), which returns the next decision to execute.
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class PyDecisionBuilder(DecisionBuilder): def __init__(self): DecisionBuilder.__init__(self) def NextWrapper(self, solver): try: return self.Next(solver) except Exception as e: if 'CP Solver fail' in str(e): return solver.FailDecision() else: raise def DebugString(self): return "PyDecisionBuilder"Ancestors
Methods
def DebugString(self)-
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def DebugString(self): return "PyDecisionBuilder"
Inherited members
class PyDemon-
A Demon is the base element of a propagation queue. It is the main object responsible for implementing the actual propagation of the constraint and pruning the inconsistent values in the domains of the variables. The main concept is that demons are listeners that are attached to the variables and listen to their modifications. There are two methods: - Run() is the actual method called when the demon is processed. - priority() returns its priority. Standard priorities are slow, normal or fast. "immediate" is reserved for variables and is treated separately.
This indicates the priority of a demon. Immediate demons are treated separately and corresponds to variables.
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class PyDemon(Demon): def RunWrapper(self, solver): try: self.Run(solver) except Exception as e: if 'CP Solver fail' in str(e): solver.ShouldFail() else: raise def DebugString(self): return "PyDemon"Ancestors
Subclasses
Methods
def DebugString(self)-
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def DebugString(self): return "PyDemon"
Inherited members
class RevBool (val: bool const &)-
This class adds reversibility to a POD type. It contains the stamp optimization. i.e. the SaveValue call is done only once per node of the search tree. Please note that actual stamps always starts at 1, thus an initial value of 0 will always trigger the first SaveValue.
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class RevBool(object): r""" This class adds reversibility to a POD type. It contains the stamp optimization. i.e. the SaveValue call is done only once per node of the search tree. Please note that actual stamps always starts at 1, thus an initial value of 0 will always trigger the first SaveValue. """ thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") __repr__ = _swig_repr def __init__(self, val: "bool const &"): _pywrapcp.RevBool_swiginit(self, _pywrapcp.new_RevBool(val)) def Value(self) -> "bool const &": return _pywrapcp.RevBool_Value(self) def SetValue(self, s: "Solver", val: "bool const &") -> "void": return _pywrapcp.RevBool_SetValue(self, s, val) __swig_destroy__ = _pywrapcp.delete_RevBoolInstance variables
var thisown-
The membership flag
Expand source code
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
Methods
def SetValue(self, s: Solver, val: bool const &) ‑> void-
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def SetValue(self, s: "Solver", val: "bool const &") -> "void": return _pywrapcp.RevBool_SetValue(self, s, val) def Value(self) ‑> bool const &-
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def Value(self) -> "bool const &": return _pywrapcp.RevBool_Value(self)
class RevInteger (val: long const &)-
This class adds reversibility to a POD type. It contains the stamp optimization. i.e. the SaveValue call is done only once per node of the search tree. Please note that actual stamps always starts at 1, thus an initial value of 0 will always trigger the first SaveValue.
Expand source code
class RevInteger(object): r""" This class adds reversibility to a POD type. It contains the stamp optimization. i.e. the SaveValue call is done only once per node of the search tree. Please note that actual stamps always starts at 1, thus an initial value of 0 will always trigger the first SaveValue. """ thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") __repr__ = _swig_repr def __init__(self, val: "long const &"): _pywrapcp.RevInteger_swiginit(self, _pywrapcp.new_RevInteger(val)) def Value(self) -> "long const &": return _pywrapcp.RevInteger_Value(self) def SetValue(self, s: "Solver", val: "long const &") -> "void": return _pywrapcp.RevInteger_SetValue(self, s, val) __swig_destroy__ = _pywrapcp.delete_RevIntegerSubclasses
Instance variables
var thisown-
The membership flag
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thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
Methods
def SetValue(self, s: Solver, val: long const &) ‑> void-
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def SetValue(self, s: "Solver", val: "long const &") -> "void": return _pywrapcp.RevInteger_SetValue(self, s, val) def Value(self) ‑> long const &-
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def Value(self) -> "long const &": return _pywrapcp.RevInteger_Value(self)
class RoutingDimension (*args, **kwargs)-
Dimensions represent quantities accumulated at nodes along the routes. They represent quantities such as weights or volumes carried along the route, or distance or times.
Quantities at a node are represented by "cumul" variables and the increase or decrease of quantities between nodes are represented by "transit" variables. These variables are linked as follows:
if j == next(i), cumuls(j) = cumuls(i) + transits(i) + slacks(i) + state_dependent_transits(i)
where slack is a positive slack variable (can represent waiting times for a time dimension), and state_dependent_transits is a non-purely functional version of transits_. Favour transits over state_dependent_transits when possible, because purely functional callbacks allow more optimisations and make the model faster and easier to solve. for a given vehicle, it is passed as an external vector, it would be better to have this information here.
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class RoutingDimension(object): r""" Dimensions represent quantities accumulated at nodes along the routes. They represent quantities such as weights or volumes carried along the route, or distance or times. Quantities at a node are represented by "cumul" variables and the increase or decrease of quantities between nodes are represented by "transit" variables. These variables are linked as follows: if j == next(i), cumuls(j) = cumuls(i) + transits(i) + slacks(i) + state_dependent_transits(i) where slack is a positive slack variable (can represent waiting times for a time dimension), and state_dependent_transits is a non-purely functional version of transits_. Favour transits over state_dependent_transits when possible, because purely functional callbacks allow more optimisations and make the model faster and easier to solve. for a given vehicle, it is passed as an external vector, it would be better to have this information here. """ thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined") __repr__ = _swig_repr __swig_destroy__ = _pywrapcp.delete_RoutingDimension def model(self) -> "operations_research::RoutingModel *": r""" Returns the model on which the dimension was created.""" return _pywrapcp.RoutingDimension_model(self) def GetTransitValue(self, from_index: "int64_t", to_index: "int64_t", vehicle: "int64_t") -> "int64_t": r""" Returns the transition value for a given pair of nodes (as var index); this value is the one taken by the corresponding transit variable when the 'next' variable for 'from_index' is bound to 'to_index'. """ return _pywrapcp.RoutingDimension_GetTransitValue(self, from_index, to_index, vehicle) def GetTransitValueFromClass(self, from_index: "int64_t", to_index: "int64_t", vehicle_class: "int64_t") -> "int64_t": r""" Same as above but taking a vehicle class of the dimension instead of a vehicle (the class of a vehicle can be obtained with vehicle_to_class()). """ return _pywrapcp.RoutingDimension_GetTransitValueFromClass(self, from_index, to_index, vehicle_class) def CumulVar(self, index: "int64_t") -> "operations_research::IntVar *": r""" Get the cumul, transit and slack variables for the given node (given as int64_t var index). """ return _pywrapcp.RoutingDimension_CumulVar(self, index) def TransitVar(self, index: "int64_t") -> "operations_research::IntVar *": return _pywrapcp.RoutingDimension_TransitVar(self, index) def FixedTransitVar(self, index: "int64_t") -> "operations_research::IntVar *": return _pywrapcp.RoutingDimension_FixedTransitVar(self, index) def SlackVar(self, index: "int64_t") -> "operations_research::IntVar *": return _pywrapcp.RoutingDimension_SlackVar(self, index) def SetSpanUpperBoundForVehicle(self, upper_bound: "int64_t", vehicle: "int") -> "void": r""" Sets an upper bound on the dimension span on a given vehicle. This is the preferred way to limit the "length" of the route of a vehicle according to a dimension. """ return _pywrapcp.RoutingDimension_SetSpanUpperBoundForVehicle(self, upper_bound, vehicle) def SetSpanCostCoefficientForVehicle(self, coefficient: "int64_t", vehicle: "int") -> "void": r""" Sets a cost proportional to the dimension span on a given vehicle, or on all vehicles at once. "coefficient" must be nonnegative. This is handy to model costs proportional to idle time when the dimension represents time. The cost for a vehicle is span_cost = coefficient * (dimension end value - dimension start value). """ return _pywrapcp.RoutingDimension_SetSpanCostCoefficientForVehicle(self, coefficient, vehicle) def SetSpanCostCoefficientForAllVehicles(self, coefficient: "int64_t") -> "void": return _pywrapcp.RoutingDimension_SetSpanCostCoefficientForAllVehicles(self, coefficient) def SetGlobalSpanCostCoefficient(self, coefficient: "int64_t") -> "void": r""" Sets a cost proportional to the *global* dimension span, that is the difference between the largest value of route end cumul variables and the smallest value of route start cumul variables. In other words: global_span_cost = coefficient * (Max(dimension end value) - Min(dimension start value)). """ return _pywrapcp.RoutingDimension_SetGlobalSpanCostCoefficient(self, coefficient) def SetCumulVarSoftUpperBound(self, index: "int64_t", upper_bound: "int64_t", coefficient: "int64_t") -> "void": r""" Sets a soft upper bound to the cumul variable of a given variable index. If the value of the cumul variable is greater than the bound, a cost proportional to the difference between this value and the bound is added to the cost function of the model: cumulVar <= upper_bound -> cost = 0 cumulVar > upper_bound -> cost = coefficient * (cumulVar - upper_bound) This is also handy to model tardiness costs when the dimension represents time. """ return _pywrapcp.RoutingDimension_SetCumulVarSoftUpperBound(self, index, upper_bound, coefficient) def HasCumulVarSoftUpperBound(self, index: "int64_t") -> "bool": r""" Returns true if a soft upper bound has been set for a given variable index. """ return _pywrapcp.RoutingDimension_HasCumulVarSoftUpperBound(self, index) def GetCumulVarSoftUpperBound(self, index: "int64_t") -> "int64_t": r""" Returns the soft upper bound of a cumul variable for a given variable index. The "hard" upper bound of the variable is returned if no soft upper bound has been set. """ return _pywrapcp.RoutingDimension_GetCumulVarSoftUpperBound(self, index) def GetCumulVarSoftUpperBoundCoefficient(self, index: "int64_t") -> "int64_t": r""" Returns the cost coefficient of the soft upper bound of a cumul variable for a given variable index. If no soft upper bound has been set, 0 is returned. """ return _pywrapcp.RoutingDimension_GetCumulVarSoftUpperBoundCoefficient(self, index) def SetCumulVarSoftLowerBound(self, index: "int64_t", lower_bound: "int64_t", coefficient: "int64_t") -> "void": r""" Sets a soft lower bound to the cumul variable of a given variable index. If the value of the cumul variable is less than the bound, a cost proportional to the difference between this value and the bound is added to the cost function of the model: cumulVar > lower_bound -> cost = 0 cumulVar <= lower_bound -> cost = coefficient * (lower_bound - cumulVar). This is also handy to model earliness costs when the dimension represents time. """ return _pywrapcp.RoutingDimension_SetCumulVarSoftLowerBound(self, index, lower_bound, coefficient) def HasCumulVarSoftLowerBound(self, index: "int64_t") -> "bool": r""" Returns true if a soft lower bound has been set for a given variable index. """ return _pywrapcp.RoutingDimension_HasCumulVarSoftLowerBound(self, index) def GetCumulVarSoftLowerBound(self, index: "int64_t") -> "int64_t": r""" Returns the soft lower bound of a cumul variable for a given variable index. The "hard" lower bound of the variable is returned if no soft lower bound has been set. """ return _pywrapcp.RoutingDimension_GetCumulVarSoftLowerBound(self, index) def GetCumulVarSoftLowerBoundCoefficient(self, index: "int64_t") -> "int64_t": r""" Returns the cost coefficient of the soft lower bound of a cumul variable for a given variable index. If no soft lower bound has been set, 0 is returned. """ return _pywrapcp.RoutingDimension_GetCumulVarSoftLowerBoundCoefficient(self, index) def SetBreakIntervalsOfVehicle(self, breaks: "std::vector< operations_research::IntervalVar * >", vehicle: "int", node_visit_transits: "std::vector< int64_t >") -> "void": r""" Sets the breaks for a given vehicle. Breaks are represented by IntervalVars. They may interrupt transits between nodes and increase the value of corresponding slack variables. A break may take place before the start of a vehicle, after the end of a vehicle, or during a travel i -> j. In that case, the interval [break.Start(), break.End()) must be a subset of [CumulVar(i) + pre_travel(i, j), CumulVar(j) - post_travel(i, j)). In other words, a break may not overlap any node n's visit, given by [CumulVar(n) - post_travel(_, n), CumulVar(n) + pre_travel(n, _)). This formula considers post_travel(_, start) and pre_travel(end, _) to be 0; pre_travel will never be called on any (_, start) and post_travel will never we called on any (end, _). If pre_travel_evaluator or post_travel_evaluator is -1, it will be taken as a function that always returns 0. Deprecated, sets pre_travel(i, j) = node_visit_transit[i]. """ return _pywrapcp.RoutingDimension_SetBreakIntervalsOfVehicle(self, breaks, vehicle, node_visit_transits) def SetBreakDistanceDurationOfVehicle(self, distance: "int64_t", duration: "int64_t", vehicle: "int") -> "void": r""" With breaks supposed to be consecutive, this forces the distance between breaks of size at least minimum_break_duration to be at most distance. This supposes that the time until route start and after route end are infinite breaks. """ return _pywrapcp.RoutingDimension_SetBreakDistanceDurationOfVehicle(self, distance, duration, vehicle) def InitializeBreaks(self) -> "void": r""" Sets up vehicle_break_intervals_, vehicle_break_distance_duration_, pre_travel_evaluators and post_travel_evaluators. """ return _pywrapcp.RoutingDimension_InitializeBreaks(self) def HasBreakConstraints(self) -> "bool": r""" Returns true if any break interval or break distance was defined.""" return _pywrapcp.RoutingDimension_HasBreakConstraints(self) def GetPreTravelEvaluatorOfVehicle(self, vehicle: "int") -> "int": return _pywrapcp.RoutingDimension_GetPreTravelEvaluatorOfVehicle(self, vehicle) def GetPostTravelEvaluatorOfVehicle(self, vehicle: "int") -> "int": return _pywrapcp.RoutingDimension_GetPostTravelEvaluatorOfVehicle(self, vehicle) def base_dimension(self) -> "operations_research::RoutingDimension const *": r""" Returns the parent in the dependency tree if any or nullptr otherwise.""" return _pywrapcp.RoutingDimension_base_dimension(self) def ShortestTransitionSlack(self, node: "int64_t") -> "int64_t": r""" It makes sense to use the function only for self-dependent dimension. For such dimensions the value of the slack of a node determines the transition cost of the next transit. Provided that 1. cumul[node] is fixed, 2. next[node] and next[next[node]] (if exists) are fixed, the value of slack[node] for which cumul[next[node]] + transit[next[node]] is minimized can be found in O(1) using this function. """ return _pywrapcp.RoutingDimension_ShortestTransitionSlack(self, node) def name(self) -> "std::string const &": r""" Returns the name of the dimension.""" return _pywrapcp.RoutingDimension_name(self) def SetPickupToDeliveryLimitFunctionForPair(self, limit_function: "operations_research::RoutingDimension::PickupToDeliveryLimitFunction", pair_index: "int") -> "void": return _pywrapcp.RoutingDimension_SetPickupToDeliveryLimitFunctionForPair(self, limit_function, pair_index) def HasPickupToDeliveryLimits(self) -> "bool": return _pywrapcp.RoutingDimension_HasPickupToDeliveryLimits(self) def AddNodePrecedence(self, first_node: "int64_t", second_node: "int64_t", offset: "int64_t") -> "void": return _pywrapcp.RoutingDimension_AddNodePrecedence(self, first_node, second_node, offset) def GetSpanUpperBoundForVehicle(self, vehicle: "int") -> "int64_t": return _pywrapcp.RoutingDimension_GetSpanUpperBoundForVehicle(self, vehicle) def GetSpanCostCoefficientForVehicle(self, vehicle: "int") -> "int64_t": return _pywrapcp.RoutingDimension_GetSpanCostCoefficientForVehicle(self, vehicle) def global_span_cost_coefficient(self) -> "int64_t": return _pywrapcp.RoutingDimension_global_span_cost_coefficient(self) def GetGlobalOptimizerOffset(self) -> "int64_t": return _pywrapcp.RoutingDimension_GetGlobalOptimizerOffset(self) def GetLocalOptimizerOffsetForVehicle(self, vehicle: "int") -> "int64_t": return _pywrapcp.RoutingDimension_GetLocalOptimizerOffsetForVehicle(self, vehicle)Instance variables
var thisown-
The membership flag
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thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
Methods
def AddNodePrecedence(self, first_node: int64_t, second_node: int64_t, offset: int64_t) ‑> void-
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def AddNodePrecedence(self, first_node: "int64_t", second_node: "int64_t", offset: "int64_t") -> "void": return _pywrapcp.RoutingDimension_AddNodePrecedence(self, first_node, second_node, offset) def CumulVar(self, index: int64_t) ‑> operations_research::IntVar *-
Get the cumul, transit and slack variables for the given node (given as int64_t var index).
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def CumulVar(self, index: "int64_t") -> "operations_research::IntVar *": r""" Get the cumul, transit and slack variables for the given node (given as int64_t var index). """ return _pywrapcp.RoutingDimension_CumulVar(self, index) def FixedTransitVar(self, index: int64_t) ‑> operations_research::IntVar *-
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def FixedTransitVar(self, index: "int64_t") -> "operations_research::IntVar *": return _pywrapcp.RoutingDimension_FixedTransitVar(self, index) def GetCumulVarSoftLowerBound(self, index: int64_t) ‑> int64_t-
Returns the soft lower bound of a cumul variable for a given variable index. The "hard" lower bound of the variable is returned if no soft lower bound has been set.
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def GetCumulVarSoftLowerBound(self, index: "int64_t") -> "int64_t": r""" Returns the soft lower bound of a cumul variable for a given variable index. The "hard" lower bound of the variable is returned if no soft lower bound has been set. """ return _pywrapcp.RoutingDimension_GetCumulVarSoftLowerBound(self, index) def GetCumulVarSoftLowerBoundCoefficient(self, index: int64_t) ‑> int64_t-
Returns the cost coefficient of the soft lower bound of a cumul variable for a given variable index. If no soft lower bound has been set, 0 is returned.
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def GetCumulVarSoftLowerBoundCoefficient(self, index: "int64_t") -> "int64_t": r""" Returns the cost coefficient of the soft lower bound of a cumul variable for a given variable index. If no soft lower bound has been set, 0 is returned. """ return _pywrapcp.RoutingDimension_GetCumulVarSoftLowerBoundCoefficient(self, index) def GetCumulVarSoftUpperBound(self, index: int64_t) ‑> int64_t-
Returns the soft upper bound of a cumul variable for a given variable index. The "hard" upper bound of the variable is returned if no soft upper bound has been set.
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def GetCumulVarSoftUpperBound(self, index: "int64_t") -> "int64_t": r""" Returns the soft upper bound of a cumul variable for a given variable index. The "hard" upper bound of the variable is returned if no soft upper bound has been set. """ return _pywrapcp.RoutingDimension_GetCumulVarSoftUpperBound(self, index) def GetCumulVarSoftUpperBoundCoefficient(self, index: int64_t) ‑> int64_t-
Returns the cost coefficient of the soft upper bound of a cumul variable for a given variable index. If no soft upper bound has been set, 0 is returned.
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def GetCumulVarSoftUpperBoundCoefficient(self, index: "int64_t") -> "int64_t": r""" Returns the cost coefficient of the soft upper bound of a cumul variable for a given variable index. If no soft upper bound has been set, 0 is returned. """ return _pywrapcp.RoutingDimension_GetCumulVarSoftUpperBoundCoefficient(self, index) def GetGlobalOptimizerOffset(self) ‑> int64_t-
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def GetGlobalOptimizerOffset(self) -> "int64_t": return _pywrapcp.RoutingDimension_GetGlobalOptimizerOffset(self) def GetLocalOptimizerOffsetForVehicle(self, vehicle: int) ‑> int64_t-
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def GetLocalOptimizerOffsetForVehicle(self, vehicle: "int") -> "int64_t": return _pywrapcp.RoutingDimension_GetLocalOptimizerOffsetForVehicle(self, vehicle) def GetPostTravelEvaluatorOfVehicle(self, vehicle: int) ‑> int-
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def GetPostTravelEvaluatorOfVehicle(self, vehicle: "int") -> "int": return _pywrapcp.RoutingDimension_GetPostTravelEvaluatorOfVehicle(self, vehicle) def GetPreTravelEvaluatorOfVehicle(self, vehicle: int) ‑> int-
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def GetPreTravelEvaluatorOfVehicle(self, vehicle: "int") -> "int": return _pywrapcp.RoutingDimension_GetPreTravelEvaluatorOfVehicle(self, vehicle) def GetSpanCostCoefficientForVehicle(self, vehicle: int) ‑> int64_t-
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def GetSpanCostCoefficientForVehicle(self, vehicle: "int") -> "int64_t": return _pywrapcp.RoutingDimension_GetSpanCostCoefficientForVehicle(self, vehicle) def GetSpanUpperBoundForVehicle(self, vehicle: int) ‑> int64_t-
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def GetSpanUpperBoundForVehicle(self, vehicle: "int") -> "int64_t": return _pywrapcp.RoutingDimension_GetSpanUpperBoundForVehicle(self, vehicle) def GetTransitValue(self, from_index: int64_t, to_index: int64_t, vehicle: int64_t) ‑> int64_t-
Returns the transition value for a given pair of nodes (as var index); this value is the one taken by the corresponding transit variable when the 'next' variable for 'from_index' is bound to 'to_index'.
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def GetTransitValue(self, from_index: "int64_t", to_index: "int64_t", vehicle: "int64_t") -> "int64_t": r""" Returns the transition value for a given pair of nodes (as var index); this value is the one taken by the corresponding transit variable when the 'next' variable for 'from_index' is bound to 'to_index'. """ return _pywrapcp.RoutingDimension_GetTransitValue(self, from_index, to_index, vehicle) def GetTransitValueFromClass(self, from_index: int64_t, to_index: int64_t, vehicle_class: int64_t) ‑> int64_t-
Same as above but taking a vehicle class of the dimension instead of a vehicle (the class of a vehicle can be obtained with vehicle_to_class()).
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def GetTransitValueFromClass(self, from_index: "int64_t", to_index: "int64_t", vehicle_class: "int64_t") -> "int64_t": r""" Same as above but taking a vehicle class of the dimension instead of a vehicle (the class of a vehicle can be obtained with vehicle_to_class()). """ return _pywrapcp.RoutingDimension_GetTransitValueFromClass(self, from_index, to_index, vehicle_class) def HasBreakConstraints(self) ‑> bool-
Returns true if any break interval or break distance was defined.
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def HasBreakConstraints(self) -> "bool": r""" Returns true if any break interval or break distance was defined.""" return _pywrapcp.RoutingDimension_HasBreakConstraints(self) def HasCumulVarSoftLowerBound(self, index: int64_t) ‑> bool-
Returns true if a soft lower bound has been set for a given variable index.
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def HasCumulVarSoftLowerBound(self, index: "int64_t") -> "bool": r""" Returns true if a soft lower bound has been set for a given variable index. """ return _pywrapcp.RoutingDimension_HasCumulVarSoftLowerBound(self, index) def HasCumulVarSoftUpperBound(self, index: int64_t) ‑> bool-
Returns true if a soft upper bound has been set for a given variable index.
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def HasCumulVarSoftUpperBound(self, index: "int64_t") -> "bool": r""" Returns true if a soft upper bound has been set for a given variable index. """ return _pywrapcp.RoutingDimension_HasCumulVarSoftUpperBound(self, index) def HasPickupToDeliveryLimits(self) ‑> bool-
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def HasPickupToDeliveryLimits(self) -> "bool": return _pywrapcp.RoutingDimension_HasPickupToDeliveryLimits(self) def InitializeBreaks(self) ‑> void-
Sets up vehicle_break_intervals_, vehicle_break_distance_duration_, pre_travel_evaluators and post_travel_evaluators.
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def InitializeBreaks(self) -> "void": r""" Sets up vehicle_break_intervals_, vehicle_break_distance_duration_, pre_travel_evaluators and post_travel_evaluators. """ return _pywrapcp.RoutingDimension_InitializeBreaks(self) def SetBreakDistanceDurationOfVehicle(self, distance: int64_t, duration: int64_t, vehicle: int) ‑> void-
With breaks supposed to be consecutive, this forces the distance between breaks of size at least minimum_break_duration to be at most distance. This supposes that the time until route start and after route end are infinite breaks.
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def SetBreakDistanceDurationOfVehicle(self, distance: "int64_t", duration: "int64_t", vehicle: "int") -> "void": r""" With breaks supposed to be consecutive, this forces the distance between breaks of size at least minimum_break_duration to be at most distance. This supposes that the time until route start and after route end are infinite breaks. """ return _pywrapcp.RoutingDimension_SetBreakDistanceDurationOfVehicle(self, distance, duration, vehicle) def SetBreakIntervalsOfVehicle(self, breaks: std::vector< operations_research::IntervalVar * >, vehicle: int, node_visit_transits: std::vector< int64_t >) ‑> void-
Sets the breaks for a given vehicle. Breaks are represented by IntervalVars. They may interrupt transits between nodes and increase the value of corresponding slack variables. A break may take place before the start of a vehicle, after the end of a vehicle, or during a travel i -> j.
In that case, the interval [break.Start(), break.End()) must be a subset of [CumulVar(i) + pre_travel(i, j), CumulVar(j) - post_travel(i, j)). In other words, a break may not overlap any node n's visit, given by [CumulVar(n) - post_travel(, n), CumulVar(n) + pre_travel(n, )). This formula considers post_travel(, start) and pre_travel(end, ) to be 0; pre_travel will never be called on any (, start) and post_travel will never we called on any (end, ). If pre_travel_evaluator or post_travel_evaluator is -1, it will be taken as a function that always returns 0. Deprecated, sets pre_travel(i, j) = node_visit_transit[i].
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def SetBreakIntervalsOfVehicle(self, breaks: "std::vector< operations_research::IntervalVar * >", vehicle: "int", node_visit_transits: "std::vector< int64_t >") -> "void": r""" Sets the breaks for a given vehicle. Breaks are represented by IntervalVars. They may interrupt transits between nodes and increase the value of corresponding slack variables. A break may take place before the start of a vehicle, after the end of a vehicle, or during a travel i -> j. In that case, the interval [break.Start(), break.End()) must be a subset of [CumulVar(i) + pre_travel(i, j), CumulVar(j) - post_travel(i, j)). In other words, a break may not overlap any node n's visit, given by [CumulVar(n) - post_travel(_, n), CumulVar(n) + pre_travel(n, _)). This formula considers post_travel(_, start) and pre_travel(end, _) to be 0; pre_travel will never be called on any (_, start) and post_travel will never we called on any (end, _). If pre_travel_evaluator or post_travel_evaluator is -1, it will be taken as a function that always returns 0. Deprecated, sets pre_travel(i, j) = node_visit_transit[i]. """ return _pywrapcp.RoutingDimension_SetBreakIntervalsOfVehicle(self, breaks, vehicle, node_visit_transits) def SetCumulVarSoftLowerBound(self, index: int64_t, lower_bound: int64_t, coefficient: int64_t) ‑> void-
Sets a soft lower bound to the cumul variable of a given variable index. If the value of the cumul variable is less than the bound, a cost proportional to the difference between this value and the bound is added to the cost function of the model: cumulVar > lower_bound -> cost = 0 cumulVar <= lower_bound -> cost = coefficient * (lower_bound - cumulVar). This is also handy to model earliness costs when the dimension represents time.
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def SetCumulVarSoftLowerBound(self, index: "int64_t", lower_bound: "int64_t", coefficient: "int64_t") -> "void": r""" Sets a soft lower bound to the cumul variable of a given variable index. If the value of the cumul variable is less than the bound, a cost proportional to the difference between this value and the bound is added to the cost function of the model: cumulVar > lower_bound -> cost = 0 cumulVar <= lower_bound -> cost = coefficient * (lower_bound - cumulVar). This is also handy to model earliness costs when the dimension represents time. """ return _pywrapcp.RoutingDimension_SetCumulVarSoftLowerBound(self, index, lower_bound, coefficient) def SetCumulVarSoftUpperBound(self, index: int64_t, upper_bound: int64_t, coefficient: int64_t) ‑> void-
Sets a soft upper bound to the cumul variable of a given variable index. If the value of the cumul variable is greater than the bound, a cost proportional to the difference between this value and the bound is added to the cost function of the model: cumulVar <= upper_bound -> cost = 0 cumulVar > upper_bound -> cost = coefficient * (cumulVar - upper_bound) This is also handy to model tardiness costs when the dimension represents time.
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def SetCumulVarSoftUpperBound(self, index: "int64_t", upper_bound: "int64_t", coefficient: "int64_t") -> "void": r""" Sets a soft upper bound to the cumul variable of a given variable index. If the value of the cumul variable is greater than the bound, a cost proportional to the difference between this value and the bound is added to the cost function of the model: cumulVar <= upper_bound -> cost = 0 cumulVar > upper_bound -> cost = coefficient * (cumulVar - upper_bound) This is also handy to model tardiness costs when the dimension represents time. """ return _pywrapcp.RoutingDimension_SetCumulVarSoftUpperBound(self, index, upper_bound, coefficient) def SetGlobalSpanCostCoefficient(self, coefficient: int64_t) ‑> void-
Sets a cost proportional to the global dimension span, that is the difference between the largest value of route end cumul variables and the smallest value of route start cumul variables. In other words: global_span_cost = coefficient * (Max(dimension end value) - Min(dimension start value)).
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def SetGlobalSpanCostCoefficient(self, coefficient: "int64_t") -> "void": r""" Sets a cost proportional to the *global* dimension span, that is the difference between the largest value of route end cumul variables and the smallest value of route start cumul variables. In other words: global_span_cost = coefficient * (Max(dimension end value) - Min(dimension start value)). """ return _pywrapcp.RoutingDimension_SetGlobalSpanCostCoefficient(self, coefficient) def SetPickupToDeliveryLimitFunctionForPair(self, limit_function: operations_research::RoutingDimension::PickupToDeliveryLimitFunction, pair_index: int) ‑> void-
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def SetPickupToDeliveryLimitFunctionForPair(self, limit_function: "operations_research::RoutingDimension::PickupToDeliveryLimitFunction", pair_index: "int") -> "void": return _pywrapcp.RoutingDimension_SetPickupToDeliveryLimitFunctionForPair(self, limit_function, pair_index) def SetSpanCostCoefficientForAllVehicles(self, coefficient: int64_t) ‑> void-
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def SetSpanCostCoefficientForAllVehicles(self, coefficient: "int64_t") -> "void": return _pywrapcp.RoutingDimension_SetSpanCostCoefficientForAllVehicles(self, coefficient) def SetSpanCostCoefficientForVehicle(self, coefficient: int64_t, vehicle: int) ‑> void-
Sets a cost proportional to the dimension span on a given vehicle, or on all vehicles at once. "coefficient" must be nonnegative. This is handy to model costs proportional to idle time when the dimension represents time. The cost for a vehicle is span_cost = coefficient * (dimension end value - dimension start value).
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def SetSpanCostCoefficientForVehicle(self, coefficient: "int64_t", vehicle: "int") -> "void": r""" Sets a cost proportional to the dimension span on a given vehicle, or on all vehicles at once. "coefficient" must be nonnegative. This is handy to model costs proportional to idle time when the dimension represents time. The cost for a vehicle is span_cost = coefficient * (dimension end value - dimension start value). """ return _pywrapcp.RoutingDimension_SetSpanCostCoefficientForVehicle(self, coefficient, vehicle) def SetSpanUpperBoundForVehicle(self, upper_bound: int64_t, vehicle: int) ‑> void-
Sets an upper bound on the dimension span on a given vehicle. This is the preferred way to limit the "length" of the route of a vehicle according to a dimension.
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def SetSpanUpperBoundForVehicle(self, upper_bound: "int64_t", vehicle: "int") -> "void": r""" Sets an upper bound on the dimension span on a given vehicle. This is the preferred way to limit the "length" of the route of a vehicle according to a dimension. """ return _pywrapcp.RoutingDimension_SetSpanUpperBoundForVehicle(self, upper_bound, vehicle) def ShortestTransitionSlack(self, node: int64_t) ‑> int64_t-
It makes sense to use the function only for self-dependent dimension. For such dimensions the value of the slack of a node determines the transition cost of the next transit. Provided that 1. cumul[node] is fixed, 2. next[node] and next[next[node]] (if exists) are fixed, the value of slack[node] for which cumul[next[node]] + transit[next[node]] is minimized can be found in O(1) using this function.
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def ShortestTransitionSlack(self, node: "int64_t") -> "int64_t": r""" It makes sense to use the function only for self-dependent dimension. For such dimensions the value of the slack of a node determines the transition cost of the next transit. Provided that 1. cumul[node] is fixed, 2. next[node] and next[next[node]] (if exists) are fixed, the value of slack[node] for which cumul[next[node]] + transit[next[node]] is minimized can be found in O(1) using this function. """ return _pywrapcp.RoutingDimension_ShortestTransitionSlack(self, node) def SlackVar(self, index: int64_t) ‑> operations_research::IntVar *-
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def SlackVar(self, index: "int64_t") -> "operations_research::IntVar *": return _pywrapcp.RoutingDimension_SlackVar(self, index) def TransitVar(self, index: int64_t) ‑> operations_research::IntVar *-
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def TransitVar(self, index: "int64_t") -> "operations_research::IntVar *": return _pywrapcp.RoutingDimension_TransitVar(self, index) def base_dimension(self) ‑> operations_research::RoutingDimension const *-
Returns the parent in the dependency tree if any or nullptr otherwise.
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def base_dimension(self) -> "operations_research::RoutingDimension const *": r""" Returns the parent in the dependency tree if any or nullptr otherwise.""" return _pywrapcp.RoutingDimension_base_dimension(self) def global_span_cost_coefficient(self) ‑> int64_t-
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def global_span_cost_coefficient(self) -> "int64_t": return _pywrapcp.RoutingDimension_global_span_cost_coefficient(self) def model(self) ‑> operations_research::RoutingModel *-
Returns the model on which the dimension was created.
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def model(self) -> "operations_research::RoutingModel *": r""" Returns the model on which the dimension was created.""" return _pywrapcp.RoutingDimension_model(self) def name(self) ‑> std::string const &-
Returns the name of the dimension.
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def name(self) -> "std::string const &": r""" Returns the name of the dimension.""" return _pywrapcp.RoutingDimension_name(self)
class RoutingIndexManager (*args)-
Manager for any NodeIndex <-> variable index conversion. The routing solver uses variable indices internally and through its API. These variable indices are tricky to manage directly because one Node can correspond to a multitude of variables, depending on the number of times they appear in the model, and if they're used as start and/or end points. This class aims to simplify variable index usage, allowing users to use NodeIndex instead.
Usage
.. code-block:: c++
auto starts_ends = ...; /// These are NodeIndex. RoutingIndexManager manager(10, 4, starts_ends); // 10 nodes, 4 vehicles. RoutingModel model(manager);Then, use 'manager.NodeToIndex(node)' whenever model requires a variable index.
Note: the mapping between node indices and variables indices is subject to change so no assumption should be made on it. The only guarantee is that indices range between 0 and n-1, where n = number of vehicles * 2 (for start and end nodes) + number of non-start or end nodes.
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class RoutingIndexManager(object): r""" Manager for any NodeIndex <-> variable index conversion. The routing solver uses variable indices internally and through its API. These variable indices are tricky to manage directly because one Node can correspond to a multitude of variables, depending on the number of times they appear in the model, and if they're used as start and/or end points. This class aims to simplify variable index usage, allowing users to use NodeIndex instead. Usage: .. code-block:: c++ auto starts_ends = ...; /// These are NodeIndex. RoutingIndexManager manager(10, 4, starts_ends); // 10 nodes, 4 vehicles. RoutingModel model(manager); Then, use 'manager.NodeToIndex(node)' whenever model requires a variable index. Note: the mapping between node indices and variables indices is subject to change so no assumption should be made on it. The only guarantee is that indices range between 0 and n-1, where n = number of vehicles * 2 (for start and end nodes) + number of non-start or end nodes. """ thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") __repr__ = _swig_repr def __init__(self, *args): _pywrapcp.RoutingIndexManager_swiginit(self, _pywrapcp.new_RoutingIndexManager(*args)) __swig_destroy__ = _pywrapcp.delete_RoutingIndexManager def GetNumberOfNodes(self) -> "int": return _pywrapcp.RoutingIndexManager_GetNumberOfNodes(self) def GetNumberOfVehicles(self) -> "int": return _pywrapcp.RoutingIndexManager_GetNumberOfVehicles(self) def GetNumberOfIndices(self) -> "int": return _pywrapcp.RoutingIndexManager_GetNumberOfIndices(self) def GetStartIndex(self, vehicle: "int") -> "int64_t": return _pywrapcp.RoutingIndexManager_GetStartIndex(self, vehicle) def GetEndIndex(self, vehicle: "int") -> "int64_t": return _pywrapcp.RoutingIndexManager_GetEndIndex(self, vehicle) def NodeToIndex(self, node: "operations_research::RoutingIndexManager::NodeIndex") -> "int64_t": return _pywrapcp.RoutingIndexManager_NodeToIndex(self, node) def IndexToNode(self, index: "int64_t") -> "operations_research::RoutingIndexManager::NodeIndex": return _pywrapcp.RoutingIndexManager_IndexToNode(self, index)Instance variables
var thisown-
The membership flag
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thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
Methods
def GetEndIndex(self, vehicle: int) ‑> int64_t-
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def GetEndIndex(self, vehicle: "int") -> "int64_t": return _pywrapcp.RoutingIndexManager_GetEndIndex(self, vehicle) def GetNumberOfIndices(self) ‑> int-
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def GetNumberOfIndices(self) -> "int": return _pywrapcp.RoutingIndexManager_GetNumberOfIndices(self) def GetNumberOfNodes(self) ‑> int-
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def GetNumberOfNodes(self) -> "int": return _pywrapcp.RoutingIndexManager_GetNumberOfNodes(self) def GetNumberOfVehicles(self) ‑> int-
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def GetNumberOfVehicles(self) -> "int": return _pywrapcp.RoutingIndexManager_GetNumberOfVehicles(self) def GetStartIndex(self, vehicle: int) ‑> int64_t-
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def GetStartIndex(self, vehicle: "int") -> "int64_t": return _pywrapcp.RoutingIndexManager_GetStartIndex(self, vehicle) def IndexToNode(self, index: int64_t) ‑> operations_research::RoutingIndexManager::NodeIndex-
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def IndexToNode(self, index: "int64_t") -> "operations_research::RoutingIndexManager::NodeIndex": return _pywrapcp.RoutingIndexManager_IndexToNode(self, index) def NodeToIndex(self, node: operations_research::RoutingIndexManager::NodeIndex) ‑> int64_t-
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def NodeToIndex(self, node: "operations_research::RoutingIndexManager::NodeIndex") -> "int64_t": return _pywrapcp.RoutingIndexManager_NodeToIndex(self, node)
class RoutingModel (*args)-
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class RoutingModel(object): thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") __repr__ = _swig_repr ROUTING_NOT_SOLVED = _pywrapcp.RoutingModel_ROUTING_NOT_SOLVED r""" Problem not solved yet (before calling RoutingModel::Solve()).""" ROUTING_SUCCESS = _pywrapcp.RoutingModel_ROUTING_SUCCESS r""" Problem solved successfully after calling RoutingModel::Solve().""" ROUTING_FAIL = _pywrapcp.RoutingModel_ROUTING_FAIL r""" No solution found to the problem after calling RoutingModel::Solve().""" ROUTING_FAIL_TIMEOUT = _pywrapcp.RoutingModel_ROUTING_FAIL_TIMEOUT r""" Time limit reached before finding a solution with RoutingModel::Solve().""" ROUTING_INVALID = _pywrapcp.RoutingModel_ROUTING_INVALID r""" Model, model parameters or flags are not valid.""" PICKUP_AND_DELIVERY_NO_ORDER = _pywrapcp.RoutingModel_PICKUP_AND_DELIVERY_NO_ORDER r""" Any precedence is accepted.""" PICKUP_AND_DELIVERY_LIFO = _pywrapcp.RoutingModel_PICKUP_AND_DELIVERY_LIFO r""" Deliveries must be performed in reverse order of pickups.""" PICKUP_AND_DELIVERY_FIFO = _pywrapcp.RoutingModel_PICKUP_AND_DELIVERY_FIFO r""" Deliveries must be performed in the same order as pickups.""" def __init__(self, *args): _pywrapcp.RoutingModel_swiginit(self, _pywrapcp.new_RoutingModel(*args)) __swig_destroy__ = _pywrapcp.delete_RoutingModel def RegisterUnaryTransitVector(self, values: "std::vector< int64_t >") -> "int": r""" Registers 'callback' and returns its index.""" return _pywrapcp.RoutingModel_RegisterUnaryTransitVector(self, values) def RegisterUnaryTransitCallback(self, callback: "operations_research::RoutingModel::TransitCallback1") -> "int": return _pywrapcp.RoutingModel_RegisterUnaryTransitCallback(self, callback) def RegisterPositiveUnaryTransitCallback(self, callback: "operations_research::RoutingModel::TransitCallback1") -> "int": return _pywrapcp.RoutingModel_RegisterPositiveUnaryTransitCallback(self, callback) def RegisterTransitMatrix(self, values: "std::vector< std::vector< int64_t > >") -> "int": return _pywrapcp.RoutingModel_RegisterTransitMatrix(self, values) def RegisterTransitCallback(self, callback: "operations_research::RoutingModel::TransitCallback2") -> "int": return _pywrapcp.RoutingModel_RegisterTransitCallback(self, callback) def RegisterPositiveTransitCallback(self, callback: "operations_research::RoutingModel::TransitCallback2") -> "int": return _pywrapcp.RoutingModel_RegisterPositiveTransitCallback(self, callback) def TransitCallback(self, callback_index: "int") -> "operations_research::RoutingModel::TransitCallback2 const &": return _pywrapcp.RoutingModel_TransitCallback(self, callback_index) def UnaryTransitCallbackOrNull(self, callback_index: "int") -> "operations_research::RoutingModel::TransitCallback1 const &": return _pywrapcp.RoutingModel_UnaryTransitCallbackOrNull(self, callback_index) def AddDimension(self, evaluator_index: "int", slack_max: "int64_t", capacity: "int64_t", fix_start_cumul_to_zero: "bool", name: "std::string const &") -> "bool": r""" Model creation Methods to add dimensions to routes; dimensions represent quantities accumulated at nodes along the routes. They represent quantities such as weights or volumes carried along the route, or distance or times. Quantities at a node are represented by "cumul" variables and the increase or decrease of quantities between nodes are represented by "transit" variables. These variables are linked as follows: if j == next(i), cumul(j) = cumul(i) + transit(i) + slack(i) where slack is a positive slack variable (can represent waiting times for a time dimension). Setting the value of fix_start_cumul_to_zero to true will force the "cumul" variable of the start node of all vehicles to be equal to 0. Creates a dimension where the transit variable is constrained to be equal to evaluator(i, next(i)); 'slack_max' is the upper bound of the slack variable and 'capacity' is the upper bound of the cumul variables. 'name' is the name used to reference the dimension; this name is used to get cumul and transit variables from the routing model. Returns false if a dimension with the same name has already been created (and doesn't create the new dimension). Takes ownership of the callback 'evaluator'. """ return _pywrapcp.RoutingModel_AddDimension(self, evaluator_index, slack_max, capacity, fix_start_cumul_to_zero, name) def AddDimensionWithVehicleTransits(self, evaluator_indices: "std::vector< int > const &", slack_max: "int64_t", capacity: "int64_t", fix_start_cumul_to_zero: "bool", name: "std::string const &") -> "bool": return _pywrapcp.RoutingModel_AddDimensionWithVehicleTransits(self, evaluator_indices, slack_max, capacity, fix_start_cumul_to_zero, name) def AddDimensionWithVehicleCapacity(self, evaluator_index: "int", slack_max: "int64_t", vehicle_capacities: "std::vector< int64_t >", fix_start_cumul_to_zero: "bool", name: "std::string const &") -> "bool": return _pywrapcp.RoutingModel_AddDimensionWithVehicleCapacity(self, evaluator_index, slack_max, vehicle_capacities, fix_start_cumul_to_zero, name) def AddDimensionWithVehicleTransitAndCapacity(self, evaluator_indices: "std::vector< int > const &", slack_max: "int64_t", vehicle_capacities: "std::vector< int64_t >", fix_start_cumul_to_zero: "bool", name: "std::string const &") -> "bool": return _pywrapcp.RoutingModel_AddDimensionWithVehicleTransitAndCapacity(self, evaluator_indices, slack_max, vehicle_capacities, fix_start_cumul_to_zero, name) def AddConstantDimensionWithSlack(self, value: "int64_t", capacity: "int64_t", slack_max: "int64_t", fix_start_cumul_to_zero: "bool", name: "std::string const &") -> "std::pair< int,bool >": r""" Creates a dimension where the transit variable is constrained to be equal to 'value'; 'capacity' is the upper bound of the cumul variables. 'name' is the name used to reference the dimension; this name is used to get cumul and transit variables from the routing model. Returns a pair consisting of an index to the registered unary transit callback and a bool denoting whether the dimension has been created. It is false if a dimension with the same name has already been created (and doesn't create the new dimension but still register a new callback). """ return _pywrapcp.RoutingModel_AddConstantDimensionWithSlack(self, value, capacity, slack_max, fix_start_cumul_to_zero, name) def AddConstantDimension(self, value: "int64_t", capacity: "int64_t", fix_start_cumul_to_zero: "bool", name: "std::string const &") -> "std::pair< int,bool >": return _pywrapcp.RoutingModel_AddConstantDimension(self, value, capacity, fix_start_cumul_to_zero, name) def AddVectorDimension(self, values: "std::vector< int64_t >", capacity: "int64_t", fix_start_cumul_to_zero: "bool", name: "std::string const &") -> "std::pair< int,bool >": r""" Creates a dimension where the transit variable is constrained to be equal to 'values[i]' for node i; 'capacity' is the upper bound of the cumul variables. 'name' is the name used to reference the dimension; this name is used to get cumul and transit variables from the routing model. Returns a pair consisting of an index to the registered unary transit callback and a bool denoting whether the dimension has been created. It is false if a dimension with the same name has already been created (and doesn't create the new dimension but still register a new callback). """ return _pywrapcp.RoutingModel_AddVectorDimension(self, values, capacity, fix_start_cumul_to_zero, name) def AddMatrixDimension(self, values: "std::vector< std::vector< int64_t > >", capacity: "int64_t", fix_start_cumul_to_zero: "bool", name: "std::string const &") -> "std::pair< int,bool >": r""" Creates a dimension where the transit variable is constrained to be equal to 'values[i][next(i)]' for node i; 'capacity' is the upper bound of the cumul variables. 'name' is the name used to reference the dimension; this name is used to get cumul and transit variables from the routing model. Returns a pair consisting of an index to the registered transit callback and a bool denoting whether the dimension has been created. It is false if a dimension with the same name has already been created (and doesn't create the new dimension but still register a new callback). """ return _pywrapcp.RoutingModel_AddMatrixDimension(self, values, capacity, fix_start_cumul_to_zero, name) def MakePathSpansAndTotalSlacks(self, dimension: "RoutingDimension", spans: "std::vector< operations_research::IntVar * >", total_slacks: "std::vector< operations_research::IntVar * >") -> "operations_research::Constraint *": r""" For every vehicle of the routing model: - if total_slacks[vehicle] is not nullptr, constrains it to be the sum of slacks on that vehicle, that is, dimension->CumulVar(end) - dimension->CumulVar(start) - sum_{node in path of vehicle} dimension->FixedTransitVar(node). - if spans[vehicle] is not nullptr, constrains it to be dimension->CumulVar(end) - dimension->CumulVar(start) This does stronger propagation than a decomposition, and takes breaks into account. """ return _pywrapcp.RoutingModel_MakePathSpansAndTotalSlacks(self, dimension, spans, total_slacks) def GetAllDimensionNames(self) -> "std::vector< std::string >": r""" Outputs the names of all dimensions added to the routing engine.""" return _pywrapcp.RoutingModel_GetAllDimensionNames(self) def GetDimensions(self) -> "std::vector< operations_research::RoutingDimension * > const &": r""" Returns all dimensions of the model.""" return _pywrapcp.RoutingModel_GetDimensions(self) def GetDimensionsWithSoftOrSpanCosts(self) -> "std::vector< operations_research::RoutingDimension * >": r""" Returns dimensions with soft or vehicle span costs.""" return _pywrapcp.RoutingModel_GetDimensionsWithSoftOrSpanCosts(self) def GetGlobalDimensionCumulOptimizers(self) -> "std::vector< std::unique_ptr< operations_research::GlobalDimensionCumulOptimizer > > const &": r""" Returns [global|local]_dimension_optimizers_, which are empty if the model has not been closed. """ return _pywrapcp.RoutingModel_GetGlobalDimensionCumulOptimizers(self) def GetLocalDimensionCumulOptimizers(self) -> "std::vector< std::unique_ptr< operations_research::LocalDimensionCumulOptimizer > > const &": return _pywrapcp.RoutingModel_GetLocalDimensionCumulOptimizers(self) def GetLocalDimensionCumulMPOptimizers(self) -> "std::vector< std::unique_ptr< operations_research::LocalDimensionCumulOptimizer > > const &": return _pywrapcp.RoutingModel_GetLocalDimensionCumulMPOptimizers(self) def GetMutableGlobalCumulOptimizer(self, dimension: "RoutingDimension") -> "operations_research::GlobalDimensionCumulOptimizer *": r""" Returns the global/local dimension cumul optimizer for a given dimension, or nullptr if there is none. """ return _pywrapcp.RoutingModel_GetMutableGlobalCumulOptimizer(self, dimension) def GetMutableLocalCumulOptimizer(self, dimension: "RoutingDimension") -> "operations_research::LocalDimensionCumulOptimizer *": return _pywrapcp.RoutingModel_GetMutableLocalCumulOptimizer(self, dimension) def GetMutableLocalCumulMPOptimizer(self, dimension: "RoutingDimension") -> "operations_research::LocalDimensionCumulOptimizer *": return _pywrapcp.RoutingModel_GetMutableLocalCumulMPOptimizer(self, dimension) def HasDimension(self, dimension_name: "std::string const &") -> "bool": r""" Returns true if a dimension exists for a given dimension name.""" return _pywrapcp.RoutingModel_HasDimension(self, dimension_name) def GetDimensionOrDie(self, dimension_name: "std::string const &") -> "operations_research::RoutingDimension const &": r""" Returns a dimension from its name. Dies if the dimension does not exist.""" return _pywrapcp.RoutingModel_GetDimensionOrDie(self, dimension_name) def GetMutableDimension(self, dimension_name: "std::string const &") -> "operations_research::RoutingDimension *": r""" Returns a dimension from its name. Returns nullptr if the dimension does not exist. """ return _pywrapcp.RoutingModel_GetMutableDimension(self, dimension_name) def SetPrimaryConstrainedDimension(self, dimension_name: "std::string const &") -> "void": r""" Set the given dimension as "primary constrained". As of August 2013, this is only used by ArcIsMoreConstrainedThanArc(). "dimension" must be the name of an existing dimension, or be empty, in which case there will not be a primary dimension after this call. """ return _pywrapcp.RoutingModel_SetPrimaryConstrainedDimension(self, dimension_name) def GetPrimaryConstrainedDimension(self) -> "std::string const &": r""" Get the primary constrained dimension, or an empty string if it is unset.""" return _pywrapcp.RoutingModel_GetPrimaryConstrainedDimension(self) def AddDisjunction(self, *args) -> "operations_research::RoutingModel::DisjunctionIndex": r""" Adds a disjunction constraint on the indices: exactly 'max_cardinality' of the indices are active. Start and end indices of any vehicle cannot be part of a disjunction. If a penalty is given, at most 'max_cardinality' of the indices can be active, and if less are active, 'penalty' is payed per inactive index. This is equivalent to adding the constraint: p + Sum(i)active[i] == max_cardinality where p is an integer variable, and the following cost to the cost function: p * penalty. 'penalty' must be positive to make the disjunction optional; a negative penalty will force 'max_cardinality' indices of the disjunction to be performed, and therefore p == 0. Note: passing a vector with a single index will model an optional index with a penalty cost if it is not visited. """ return _pywrapcp.RoutingModel_AddDisjunction(self, *args) def GetDisjunctionIndices(self, index: "int64_t") -> "std::vector< operations_research::RoutingModel::DisjunctionIndex > const &": r""" Returns the indices of the disjunctions to which an index belongs.""" return _pywrapcp.RoutingModel_GetDisjunctionIndices(self, index) def GetDisjunctionPenalty(self, index: "operations_research::RoutingModel::DisjunctionIndex") -> "int64_t": r""" Returns the penalty of the node disjunction of index 'index'.""" return _pywrapcp.RoutingModel_GetDisjunctionPenalty(self, index) def GetDisjunctionMaxCardinality(self, index: "operations_research::RoutingModel::DisjunctionIndex") -> "int64_t": r""" Returns the maximum number of possible active nodes of the node disjunction of index 'index'. """ return _pywrapcp.RoutingModel_GetDisjunctionMaxCardinality(self, index) def GetNumberOfDisjunctions(self) -> "int": r""" Returns the number of node disjunctions in the model.""" return _pywrapcp.RoutingModel_GetNumberOfDisjunctions(self) def GetPerfectBinaryDisjunctions(self) -> "std::vector< std::pair< int64_t,int64_t > >": r""" Returns the list of all perfect binary disjunctions, as pairs of variable indices: a disjunction is "perfect" when its variables do not appear in any other disjunction. Each pair is sorted (lowest variable index first), and the output vector is also sorted (lowest pairs first). """ return _pywrapcp.RoutingModel_GetPerfectBinaryDisjunctions(self) def IgnoreDisjunctionsAlreadyForcedToZero(self) -> "void": r""" SPECIAL: Makes the solver ignore all the disjunctions whose active variables are all trivially zero (i.e. Max() == 0), by setting their max_cardinality to 0. This can be useful when using the BaseBinaryDisjunctionNeighborhood operators, in the context of arc-based routing. """ return _pywrapcp.RoutingModel_IgnoreDisjunctionsAlreadyForcedToZero(self) def AddSoftSameVehicleConstraint(self, indices: "std::vector< int64_t > const &", cost: "int64_t") -> "void": r""" Adds a soft constraint to force a set of variable indices to be on the same vehicle. If all nodes are not on the same vehicle, each extra vehicle used adds 'cost' to the cost function. """ return _pywrapcp.RoutingModel_AddSoftSameVehicleConstraint(self, indices, cost) def SetAllowedVehiclesForIndex(self, vehicles: "std::vector< int > const &", index: "int64_t") -> "void": r""" Sets the vehicles which can visit a given node. If the node is in a disjunction, this will not prevent it from being unperformed. Specifying an empty vector of vehicles has no effect (all vehicles will be allowed to visit the node). """ return _pywrapcp.RoutingModel_SetAllowedVehiclesForIndex(self, vehicles, index) def IsVehicleAllowedForIndex(self, vehicle: "int", index: "int64_t") -> "bool": r""" Returns true if a vehicle is allowed to visit a given node.""" return _pywrapcp.RoutingModel_IsVehicleAllowedForIndex(self, vehicle, index) def AddPickupAndDelivery(self, pickup: "int64_t", delivery: "int64_t") -> "void": r""" Notifies that index1 and index2 form a pair of nodes which should belong to the same route. This methods helps the search find better solutions, especially in the local search phase. It should be called each time you have an equality constraint linking the vehicle variables of two node (including for instance pickup and delivery problems): Solver* const solver = routing.solver(); int64_t index1 = manager.NodeToIndex(node1); int64_t index2 = manager.NodeToIndex(node2); solver->AddConstraint(solver->MakeEquality( routing.VehicleVar(index1), routing.VehicleVar(index2))); routing.AddPickupAndDelivery(index1, index2); """ return _pywrapcp.RoutingModel_AddPickupAndDelivery(self, pickup, delivery) def AddPickupAndDeliverySets(self, pickup_disjunction: "operations_research::RoutingModel::DisjunctionIndex", delivery_disjunction: "operations_research::RoutingModel::DisjunctionIndex") -> "void": r""" Same as AddPickupAndDelivery but notifying that the performed node from the disjunction of index 'pickup_disjunction' is on the same route as the performed node from the disjunction of index 'delivery_disjunction'. """ return _pywrapcp.RoutingModel_AddPickupAndDeliverySets(self, pickup_disjunction, delivery_disjunction) def GetPickupIndexPairs(self, node_index: "int64_t") -> "std::vector< std::pair< int,int > > const &": r""" Returns pairs for which the node is a pickup; the first element of each pair is the index in the pickup and delivery pairs list in which the pickup appears, the second element is its index in the pickups list. """ return _pywrapcp.RoutingModel_GetPickupIndexPairs(self, node_index) def GetDeliveryIndexPairs(self, node_index: "int64_t") -> "std::vector< std::pair< int,int > > const &": r""" Same as above for deliveries.""" return _pywrapcp.RoutingModel_GetDeliveryIndexPairs(self, node_index) def SetPickupAndDeliveryPolicyOfAllVehicles(self, policy: "operations_research::RoutingModel::PickupAndDeliveryPolicy") -> "void": r""" Sets the Pickup and delivery policy of all vehicles. It is equivalent to calling SetPickupAndDeliveryPolicyOfVehicle on all vehicles. """ return _pywrapcp.RoutingModel_SetPickupAndDeliveryPolicyOfAllVehicles(self, policy) def SetPickupAndDeliveryPolicyOfVehicle(self, policy: "operations_research::RoutingModel::PickupAndDeliveryPolicy", vehicle: "int") -> "void": return _pywrapcp.RoutingModel_SetPickupAndDeliveryPolicyOfVehicle(self, policy, vehicle) def GetPickupAndDeliveryPolicyOfVehicle(self, vehicle: "int") -> "operations_research::RoutingModel::PickupAndDeliveryPolicy": return _pywrapcp.RoutingModel_GetPickupAndDeliveryPolicyOfVehicle(self, vehicle) def GetNumOfSingletonNodes(self) -> "int": r""" Returns the number of non-start/end nodes which do not appear in a pickup/delivery pair. """ return _pywrapcp.RoutingModel_GetNumOfSingletonNodes(self) TYPE_ADDED_TO_VEHICLE = _pywrapcp.RoutingModel_TYPE_ADDED_TO_VEHICLE r""" When visited, the number of types 'T' on the vehicle increases by one.""" ADDED_TYPE_REMOVED_FROM_VEHICLE = _pywrapcp.RoutingModel_ADDED_TYPE_REMOVED_FROM_VEHICLE r""" When visited, one instance of type 'T' previously added to the route (TYPE_ADDED_TO_VEHICLE), if any, is removed from the vehicle. If the type was not previously added to the route or all added instances have already been removed, this visit has no effect on the types. """ TYPE_ON_VEHICLE_UP_TO_VISIT = _pywrapcp.RoutingModel_TYPE_ON_VEHICLE_UP_TO_VISIT r""" With the following policy, the visit enforces that type 'T' is considered on the route from its start until this node is visited. """ TYPE_SIMULTANEOUSLY_ADDED_AND_REMOVED = _pywrapcp.RoutingModel_TYPE_SIMULTANEOUSLY_ADDED_AND_REMOVED r""" The visit doesn't have an impact on the number of types 'T' on the route, as it's (virtually) added and removed directly. This policy can be used for visits which are part of an incompatibility or requirement set without affecting the type count on the route. """ def SetVisitType(self, index: "int64_t", type: "int", type_policy: "operations_research::RoutingModel::VisitTypePolicy") -> "void": return _pywrapcp.RoutingModel_SetVisitType(self, index, type, type_policy) def GetVisitType(self, index: "int64_t") -> "int": return _pywrapcp.RoutingModel_GetVisitType(self, index) def GetSingleNodesOfType(self, type: "int") -> "std::vector< int > const &": return _pywrapcp.RoutingModel_GetSingleNodesOfType(self, type) def GetPairIndicesOfType(self, type: "int") -> "std::vector< int > const &": return _pywrapcp.RoutingModel_GetPairIndicesOfType(self, type) def GetVisitTypePolicy(self, index: "int64_t") -> "operations_research::RoutingModel::VisitTypePolicy": return _pywrapcp.RoutingModel_GetVisitTypePolicy(self, index) def CloseVisitTypes(self) -> "void": r""" This function should be called once all node visit types have been set and prior to adding any incompatibilities/requirements. "close" types. """ return _pywrapcp.RoutingModel_CloseVisitTypes(self) def GetNumberOfVisitTypes(self) -> "int": return _pywrapcp.RoutingModel_GetNumberOfVisitTypes(self) def AddHardTypeIncompatibility(self, type1: "int", type2: "int") -> "void": r""" Incompatibilities: Two nodes with "hard" incompatible types cannot share the same route at all, while with a "temporal" incompatibility they can't be on the same route at the same time. """ return _pywrapcp.RoutingModel_AddHardTypeIncompatibility(self, type1, type2) def AddTemporalTypeIncompatibility(self, type1: "int", type2: "int") -> "void": return _pywrapcp.RoutingModel_AddTemporalTypeIncompatibility(self, type1, type2) def GetHardTypeIncompatibilitiesOfType(self, type: "int") -> "absl::flat_hash_set< int > const &": r""" Returns visit types incompatible with a given type.""" return _pywrapcp.RoutingModel_GetHardTypeIncompatibilitiesOfType(self, type) def GetTemporalTypeIncompatibilitiesOfType(self, type: "int") -> "absl::flat_hash_set< int > const &": return _pywrapcp.RoutingModel_GetTemporalTypeIncompatibilitiesOfType(self, type) def HasHardTypeIncompatibilities(self) -> "bool": r""" Returns true iff any hard (resp. temporal) type incompatibilities have been added to the model. """ return _pywrapcp.RoutingModel_HasHardTypeIncompatibilities(self) def HasTemporalTypeIncompatibilities(self) -> "bool": return _pywrapcp.RoutingModel_HasTemporalTypeIncompatibilities(self) def AddSameVehicleRequiredTypeAlternatives(self, dependent_type: "int", required_type_alternatives: "absl::flat_hash_set< int >") -> "void": r""" Requirements: NOTE: As of 2019-04, cycles in the requirement graph are not supported, and lead to the dependent nodes being skipped if possible (otherwise the model is considered infeasible). The following functions specify that "dependent_type" requires at least one of the types in "required_type_alternatives". For same-vehicle requirements, a node of dependent type type_D requires at least one node of type type_R among the required alternatives on the same route. """ return _pywrapcp.RoutingModel_AddSameVehicleRequiredTypeAlternatives(self, dependent_type, required_type_alternatives) def AddRequiredTypeAlternativesWhenAddingType(self, dependent_type: "int", required_type_alternatives: "absl::flat_hash_set< int >") -> "void": r""" If type_D depends on type_R when adding type_D, any node_D of type_D and VisitTypePolicy TYPE_ADDED_TO_VEHICLE or TYPE_SIMULTANEOUSLY_ADDED_AND_REMOVED requires at least one type_R on its vehicle at the time node_D is visited. """ return _pywrapcp.RoutingModel_AddRequiredTypeAlternativesWhenAddingType(self, dependent_type, required_type_alternatives) def AddRequiredTypeAlternativesWhenRemovingType(self, dependent_type: "int", required_type_alternatives: "absl::flat_hash_set< int >") -> "void": r""" The following requirements apply when visiting dependent nodes that remove their type from the route, i.e. type_R must be on the vehicle when type_D of VisitTypePolicy ADDED_TYPE_REMOVED_FROM_VEHICLE, TYPE_ON_VEHICLE_UP_TO_VISIT or TYPE_SIMULTANEOUSLY_ADDED_AND_REMOVED is visited. """ return _pywrapcp.RoutingModel_AddRequiredTypeAlternativesWhenRemovingType(self, dependent_type, required_type_alternatives) def GetSameVehicleRequiredTypeAlternativesOfType(self, type: "int") -> "std::vector< absl::flat_hash_set< int > > const &": r""" Returns the set of same-vehicle requirement alternatives for the given type. """ return _pywrapcp.RoutingModel_GetSameVehicleRequiredTypeAlternativesOfType(self, type) def GetRequiredTypeAlternativesWhenAddingType(self, type: "int") -> "std::vector< absl::flat_hash_set< int > > const &": r""" Returns the set of requirement alternatives when adding the given type.""" return _pywrapcp.RoutingModel_GetRequiredTypeAlternativesWhenAddingType(self, type) def GetRequiredTypeAlternativesWhenRemovingType(self, type: "int") -> "std::vector< absl::flat_hash_set< int > > const &": r""" Returns the set of requirement alternatives when removing the given type.""" return _pywrapcp.RoutingModel_GetRequiredTypeAlternativesWhenRemovingType(self, type) def HasSameVehicleTypeRequirements(self) -> "bool": r""" Returns true iff any same-route (resp. temporal) type requirements have been added to the model. """ return _pywrapcp.RoutingModel_HasSameVehicleTypeRequirements(self) def HasTemporalTypeRequirements(self) -> "bool": return _pywrapcp.RoutingModel_HasTemporalTypeRequirements(self) def HasTypeRegulations(self) -> "bool": r""" Returns true iff the model has any incompatibilities or requirements set on node types. """ return _pywrapcp.RoutingModel_HasTypeRegulations(self) def UnperformedPenalty(self, var_index: "int64_t") -> "int64_t": r""" Get the "unperformed" penalty of a node. This is only well defined if the node is only part of a single Disjunction involving only itself, and that disjunction has a penalty. In all other cases, including forced active nodes, this returns 0. """ return _pywrapcp.RoutingModel_UnperformedPenalty(self, var_index) def UnperformedPenaltyOrValue(self, default_value: "int64_t", var_index: "int64_t") -> "int64_t": r""" Same as above except that it returns default_value instead of 0 when penalty is not well defined (default value is passed as first argument to simplify the usage of the method in a callback). """ return _pywrapcp.RoutingModel_UnperformedPenaltyOrValue(self, default_value, var_index) def GetDepot(self) -> "int64_t": r""" Returns the variable index of the first starting or ending node of all routes. If all routes start and end at the same node (single depot), this is the node returned. """ return _pywrapcp.RoutingModel_GetDepot(self) def SetMaximumNumberOfActiveVehicles(self, max_active_vehicles: "int") -> "void": r""" Constrains the maximum number of active vehicles, aka the number of vehicles which do not have an empty route. For instance, this can be used to limit the number of routes in the case where there are fewer drivers than vehicles and that the fleet of vehicle is heterogeneous. """ return _pywrapcp.RoutingModel_SetMaximumNumberOfActiveVehicles(self, max_active_vehicles) def GetMaximumNumberOfActiveVehicles(self) -> "int": r""" Returns the maximum number of active vehicles.""" return _pywrapcp.RoutingModel_GetMaximumNumberOfActiveVehicles(self) def SetArcCostEvaluatorOfAllVehicles(self, evaluator_index: "int") -> "void": r""" Sets the cost function of the model such that the cost of a segment of a route between node 'from' and 'to' is evaluator(from, to), whatever the route or vehicle performing the route. """ return _pywrapcp.RoutingModel_SetArcCostEvaluatorOfAllVehicles(self, evaluator_index) def SetArcCostEvaluatorOfVehicle(self, evaluator_index: "int", vehicle: "int") -> "void": r""" Sets the cost function for a given vehicle route.""" return _pywrapcp.RoutingModel_SetArcCostEvaluatorOfVehicle(self, evaluator_index, vehicle) def SetFixedCostOfAllVehicles(self, cost: "int64_t") -> "void": r""" Sets the fixed cost of all vehicle routes. It is equivalent to calling SetFixedCostOfVehicle on all vehicle routes. """ return _pywrapcp.RoutingModel_SetFixedCostOfAllVehicles(self, cost) def SetFixedCostOfVehicle(self, cost: "int64_t", vehicle: "int") -> "void": r""" Sets the fixed cost of one vehicle route.""" return _pywrapcp.RoutingModel_SetFixedCostOfVehicle(self, cost, vehicle) def GetFixedCostOfVehicle(self, vehicle: "int") -> "int64_t": r""" Returns the route fixed cost taken into account if the route of the vehicle is not empty, aka there's at least one node on the route other than the first and last nodes. """ return _pywrapcp.RoutingModel_GetFixedCostOfVehicle(self, vehicle) def SetAmortizedCostFactorsOfAllVehicles(self, linear_cost_factor: "int64_t", quadratic_cost_factor: "int64_t") -> "void": r""" The following methods set the linear and quadratic cost factors of vehicles (must be positive values). The default value of these parameters is zero for all vehicles. When set, the cost_ of the model will contain terms aiming at reducing the number of vehicles used in the model, by adding the following to the objective for every vehicle v: INDICATOR(v used in the model) * [linear_cost_factor_of_vehicle_[v] - quadratic_cost_factor_of_vehicle_[v]*(square of length of route v)] i.e. for every used vehicle, we add the linear factor as fixed cost, and subtract the square of the route length multiplied by the quadratic factor. This second term aims at making the routes as dense as possible. Sets the linear and quadratic cost factor of all vehicles. """ return _pywrapcp.RoutingModel_SetAmortizedCostFactorsOfAllVehicles(self, linear_cost_factor, quadratic_cost_factor) def SetAmortizedCostFactorsOfVehicle(self, linear_cost_factor: "int64_t", quadratic_cost_factor: "int64_t", vehicle: "int") -> "void": r""" Sets the linear and quadratic cost factor of the given vehicle.""" return _pywrapcp.RoutingModel_SetAmortizedCostFactorsOfVehicle(self, linear_cost_factor, quadratic_cost_factor, vehicle) def GetAmortizedLinearCostFactorOfVehicles(self) -> "std::vector< int64_t > const &": return _pywrapcp.RoutingModel_GetAmortizedLinearCostFactorOfVehicles(self) def GetAmortizedQuadraticCostFactorOfVehicles(self) -> "std::vector< int64_t > const &": return _pywrapcp.RoutingModel_GetAmortizedQuadraticCostFactorOfVehicles(self) def ConsiderEmptyRouteCostsForVehicle(self, consider_costs: "bool", vehicle: "int") -> "void": return _pywrapcp.RoutingModel_ConsiderEmptyRouteCostsForVehicle(self, consider_costs, vehicle) def AreEmptyRouteCostsConsideredForVehicle(self, vehicle: "int") -> "bool": return _pywrapcp.RoutingModel_AreEmptyRouteCostsConsideredForVehicle(self, vehicle) def SetFirstSolutionEvaluator(self, evaluator: "operations_research::Solver::IndexEvaluator2") -> "void": r""" Gets/sets the evaluator used during the search. Only relevant when RoutingSearchParameters.first_solution_strategy = EVALUATOR_STRATEGY. Takes ownership of evaluator. """ return _pywrapcp.RoutingModel_SetFirstSolutionEvaluator(self, evaluator) def AddLocalSearchOperator(self, ls_operator: "LocalSearchOperator") -> "void": r""" Adds a local search operator to the set of operators used to solve the vehicle routing problem. """ return _pywrapcp.RoutingModel_AddLocalSearchOperator(self, ls_operator) def AddSearchMonitor(self, monitor: "SearchMonitor") -> "void": r""" Adds a search monitor to the search used to solve the routing model.""" return _pywrapcp.RoutingModel_AddSearchMonitor(self, monitor) def AddAtSolutionCallback(self, callback: "std::function< void () >") -> "void": r""" Adds a callback called each time a solution is found during the search. This is a shortcut to creating a monitor to call the callback on AtSolution() and adding it with AddSearchMonitor. """ return _pywrapcp.RoutingModel_AddAtSolutionCallback(self, callback) def AddVariableMinimizedByFinalizer(self, var: "IntVar") -> "void": r""" Adds a variable to minimize in the solution finalizer. The solution finalizer is called each time a solution is found during the search and allows to instantiate secondary variables (such as dimension cumul variables). """ return _pywrapcp.RoutingModel_AddVariableMinimizedByFinalizer(self, var) def AddVariableMaximizedByFinalizer(self, var: "IntVar") -> "void": r""" Adds a variable to maximize in the solution finalizer (see above for information on the solution finalizer). """ return _pywrapcp.RoutingModel_AddVariableMaximizedByFinalizer(self, var) def AddWeightedVariableMinimizedByFinalizer(self, var: "IntVar", cost: "int64_t") -> "void": r""" Adds a variable to minimize in the solution finalizer, with a weighted priority: the higher the more priority it has. """ return _pywrapcp.RoutingModel_AddWeightedVariableMinimizedByFinalizer(self, var, cost) def AddVariableTargetToFinalizer(self, var: "IntVar", target: "int64_t") -> "void": r""" Add a variable to set the closest possible to the target value in the solution finalizer. """ return _pywrapcp.RoutingModel_AddVariableTargetToFinalizer(self, var, target) def CloseModel(self) -> "void": r""" Closes the current routing model; after this method is called, no modification to the model can be done, but RoutesToAssignment becomes available. Note that CloseModel() is automatically called by Solve() and other methods that produce solution. This is equivalent to calling CloseModelWithParameters(DefaultRoutingSearchParameters()). """ return _pywrapcp.RoutingModel_CloseModel(self) def CloseModelWithParameters(self, search_parameters: "operations_research::RoutingSearchParameters const &") -> "void": r""" Same as above taking search parameters (as of 10/2015 some the parameters have to be set when closing the model). """ return _pywrapcp.RoutingModel_CloseModelWithParameters(self, search_parameters) def Solve(self, assignment: "Assignment"=None) -> "operations_research::Assignment const *": r""" Solves the current routing model; closes the current model. This is equivalent to calling SolveWithParameters(DefaultRoutingSearchParameters()) or SolveFromAssignmentWithParameters(assignment, DefaultRoutingSearchParameters()). """ return _pywrapcp.RoutingModel_Solve(self, assignment) def SolveWithParameters(self, search_parameters: "operations_research::RoutingSearchParameters const &", solutions: "std::vector< operations_research::Assignment const * > *"=None) -> "operations_research::Assignment const *": r""" Solves the current routing model with the given parameters. If 'solutions' is specified, it will contain the k best solutions found during the search (from worst to best, including the one returned by this method), where k corresponds to the 'number_of_solutions_to_collect' in 'search_parameters'. Note that the Assignment returned by the method and the ones in solutions are owned by the underlying solver and should not be deleted. """ return _pywrapcp.RoutingModel_SolveWithParameters(self, search_parameters, solutions) def SolveFromAssignmentWithParameters(self, assignment: "Assignment", search_parameters: "operations_research::RoutingSearchParameters const &", solutions: "std::vector< operations_research::Assignment const * > *"=None) -> "operations_research::Assignment const *": r""" Same as above, except that if assignment is not null, it will be used as the initial solution. """ return _pywrapcp.RoutingModel_SolveFromAssignmentWithParameters(self, assignment, search_parameters, solutions) def SolveFromAssignmentsWithParameters(self, assignments: "std::vector< operations_research::Assignment const * > const &", search_parameters: "operations_research::RoutingSearchParameters const &", solutions: "std::vector< operations_research::Assignment const * > *"=None) -> "operations_research::Assignment const *": r""" Same as above but will try all assignments in order as first solutions until one succeeds. """ return _pywrapcp.RoutingModel_SolveFromAssignmentsWithParameters(self, assignments, search_parameters, solutions) def SetAssignmentFromOtherModelAssignment(self, target_assignment: "Assignment", source_model: "RoutingModel", source_assignment: "Assignment") -> "void": r""" Given a "source_model" and its "source_assignment", resets "target_assignment" with the IntVar variables (nexts_, and vehicle_vars_ if costs aren't homogeneous across vehicles) of "this" model, with the values set according to those in "other_assignment". The objective_element of target_assignment is set to this->cost_. """ return _pywrapcp.RoutingModel_SetAssignmentFromOtherModelAssignment(self, target_assignment, source_model, source_assignment) def ComputeLowerBound(self) -> "int64_t": r""" Computes a lower bound to the routing problem solving a linear assignment problem. The routing model must be closed before calling this method. Note that problems with node disjunction constraints (including optional nodes) and non-homogenous costs are not supported (the method returns 0 in these cases). """ return _pywrapcp.RoutingModel_ComputeLowerBound(self) def status(self) -> "operations_research::RoutingModel::Status": r""" Returns the current status of the routing model.""" return _pywrapcp.RoutingModel_status(self) def ApplyLocks(self, locks: "std::vector< int64_t > const &") -> "operations_research::IntVar *": r""" Applies a lock chain to the next search. 'locks' represents an ordered vector of nodes representing a partial route which will be fixed during the next search; it will constrain next variables such that: next[locks[i]] == locks[i+1]. Returns the next variable at the end of the locked chain; this variable is not locked. An assignment containing the locks can be obtained by calling PreAssignment(). """ return _pywrapcp.RoutingModel_ApplyLocks(self, locks) def ApplyLocksToAllVehicles(self, locks: "std::vector< std::vector< int64_t > > const &", close_routes: "bool") -> "bool": r""" Applies lock chains to all vehicles to the next search, such that locks[p] is the lock chain for route p. Returns false if the locks do not contain valid routes; expects that the routes do not contain the depots, i.e. there are empty vectors in place of empty routes. If close_routes is set to true, adds the end nodes to the route of each vehicle and deactivates other nodes. An assignment containing the locks can be obtained by calling PreAssignment(). """ return _pywrapcp.RoutingModel_ApplyLocksToAllVehicles(self, locks, close_routes) def PreAssignment(self) -> "operations_research::Assignment const *const": r""" Returns an assignment used to fix some of the variables of the problem. In practice, this assignment locks partial routes of the problem. This can be used in the context of locking the parts of the routes which have already been driven in online routing problems. """ return _pywrapcp.RoutingModel_PreAssignment(self) def MutablePreAssignment(self) -> "operations_research::Assignment *": return _pywrapcp.RoutingModel_MutablePreAssignment(self) def WriteAssignment(self, file_name: "std::string const &") -> "bool": r""" Writes the current solution to a file containing an AssignmentProto. Returns false if the file cannot be opened or if there is no current solution. """ return _pywrapcp.RoutingModel_WriteAssignment(self, file_name) def ReadAssignment(self, file_name: "std::string const &") -> "operations_research::Assignment *": r""" Reads an assignment from a file and returns the current solution. Returns nullptr if the file cannot be opened or if the assignment is not valid. """ return _pywrapcp.RoutingModel_ReadAssignment(self, file_name) def RestoreAssignment(self, solution: "Assignment") -> "operations_research::Assignment *": r""" Restores an assignment as a solution in the routing model and returns the new solution. Returns nullptr if the assignment is not valid. """ return _pywrapcp.RoutingModel_RestoreAssignment(self, solution) def ReadAssignmentFromRoutes(self, routes: "std::vector< std::vector< int64_t > > const &", ignore_inactive_indices: "bool") -> "operations_research::Assignment *": r""" Restores the routes as the current solution. Returns nullptr if the solution cannot be restored (routes do not contain a valid solution). Note that calling this method will run the solver to assign values to the dimension variables; this may take considerable amount of time, especially when using dimensions with slack. """ return _pywrapcp.RoutingModel_ReadAssignmentFromRoutes(self, routes, ignore_inactive_indices) def RoutesToAssignment(self, routes: "std::vector< std::vector< int64_t > > const &", ignore_inactive_indices: "bool", close_routes: "bool", assignment: "Assignment") -> "bool": r""" Fills an assignment from a specification of the routes of the vehicles. The routes are specified as lists of variable indices that appear on the routes of the vehicles. The indices of the outer vector in 'routes' correspond to vehicles IDs, the inner vector contains the variable indices on the routes for the given vehicle. The inner vectors must not contain the start and end indices, as these are determined by the routing model. Sets the value of NextVars in the assignment, adding the variables to the assignment if necessary. The method does not touch other variables in the assignment. The method can only be called after the model is closed. With ignore_inactive_indices set to false, this method will fail (return nullptr) in case some of the route contain indices that are deactivated in the model; when set to true, these indices will be skipped. Returns true if routes were successfully loaded. However, such assignment still might not be a valid solution to the routing problem due to more complex constraints; it is advisible to call solver()->CheckSolution() afterwards. """ return _pywrapcp.RoutingModel_RoutesToAssignment(self, routes, ignore_inactive_indices, close_routes, assignment) def AssignmentToRoutes(self, assignment: "Assignment", routes: "std::vector< std::vector< int64_t > > *const") -> "void": r""" Converts the solution in the given assignment to routes for all vehicles. Expects that assignment contains a valid solution (i.e. routes for all vehicles end with an end index for that vehicle). """ return _pywrapcp.RoutingModel_AssignmentToRoutes(self, assignment, routes) def CompactAssignment(self, assignment: "Assignment") -> "operations_research::Assignment *": r""" Converts the solution in the given assignment to routes for all vehicles. If the returned vector is route_indices, route_indices[i][j] is the index for jth location visited on route i. Note that contrary to AssignmentToRoutes, the vectors do include start and end locations. Returns a compacted version of the given assignment, in which all vehicles with id lower or equal to some N have non-empty routes, and all vehicles with id greater than N have empty routes. Does not take ownership of the returned object. If found, the cost of the compact assignment is the same as in the original assignment and it preserves the values of 'active' variables. Returns nullptr if a compact assignment was not found. This method only works in homogenous mode, and it only swaps equivalent vehicles (vehicles with the same start and end nodes). When creating the compact assignment, the empty plan is replaced by the route assigned to the compatible vehicle with the highest id. Note that with more complex constraints on vehicle variables, this method might fail even if a compact solution exists. This method changes the vehicle and dimension variables as necessary. While compacting the solution, only basic checks on vehicle variables are performed; if one of these checks fails no attempts to repair it are made (instead, the method returns nullptr). """ return _pywrapcp.RoutingModel_CompactAssignment(self, assignment) def CompactAndCheckAssignment(self, assignment: "Assignment") -> "operations_research::Assignment *": r""" Same as CompactAssignment() but also checks the validity of the final compact solution; if it is not valid, no attempts to repair it are made (instead, the method returns nullptr). """ return _pywrapcp.RoutingModel_CompactAndCheckAssignment(self, assignment) def AddToAssignment(self, var: "IntVar") -> "void": r""" Adds an extra variable to the vehicle routing assignment.""" return _pywrapcp.RoutingModel_AddToAssignment(self, var) def AddIntervalToAssignment(self, interval: "IntervalVar") -> "void": return _pywrapcp.RoutingModel_AddIntervalToAssignment(self, interval) def PackCumulsOfOptimizerDimensionsFromAssignment(self, original_assignment: "Assignment", duration_limit: "absl::Duration") -> "operations_research::Assignment const *": r""" For every dimension in the model with an optimizer in local/global_dimension_optimizers_, this method tries to pack the cumul values of the dimension, such that: - The cumul costs (span costs, soft lower and upper bound costs, etc) are minimized. - The cumuls of the ends of the routes are minimized for this given minimal cumul cost. - Given these minimal end cumuls, the route start cumuls are maximized. Returns the assignment resulting from allocating these packed cumuls with the solver, and nullptr if these cumuls could not be set by the solver. """ return _pywrapcp.RoutingModel_PackCumulsOfOptimizerDimensionsFromAssignment(self, original_assignment, duration_limit) def AddLocalSearchFilter(self, filter: "LocalSearchFilter") -> "void": r""" Adds a custom local search filter to the list of filters used to speed up local search by pruning unfeasible variable assignments. Calling this method after the routing model has been closed (CloseModel() or Solve() has been called) has no effect. The routing model does not take ownership of the filter. """ return _pywrapcp.RoutingModel_AddLocalSearchFilter(self, filter) def Start(self, vehicle: "int") -> "int64_t": r""" Model inspection. Returns the variable index of the starting node of a vehicle route. """ return _pywrapcp.RoutingModel_Start(self, vehicle) def End(self, vehicle: "int") -> "int64_t": r""" Returns the variable index of the ending node of a vehicle route.""" return _pywrapcp.RoutingModel_End(self, vehicle) def IsStart(self, index: "int64_t") -> "bool": r""" Returns true if 'index' represents the first node of a route.""" return _pywrapcp.RoutingModel_IsStart(self, index) def IsEnd(self, index: "int64_t") -> "bool": r""" Returns true if 'index' represents the last node of a route.""" return _pywrapcp.RoutingModel_IsEnd(self, index) def VehicleIndex(self, index: "int64_t") -> "int": r""" Returns the vehicle of the given start/end index, and -1 if the given index is not a vehicle start/end. """ return _pywrapcp.RoutingModel_VehicleIndex(self, index) def Next(self, assignment: "Assignment", index: "int64_t") -> "int64_t": r""" Assignment inspection Returns the variable index of the node directly after the node corresponding to 'index' in 'assignment'. """ return _pywrapcp.RoutingModel_Next(self, assignment, index) def IsVehicleUsed(self, assignment: "Assignment", vehicle: "int") -> "bool": r""" Returns true if the route of 'vehicle' is non empty in 'assignment'.""" return _pywrapcp.RoutingModel_IsVehicleUsed(self, assignment, vehicle) def NextVar(self, index: "int64_t") -> "operations_research::IntVar *": r""" Returns the next variable of the node corresponding to index. Note that NextVar(index) == index is equivalent to ActiveVar(index) == 0. """ return _pywrapcp.RoutingModel_NextVar(self, index) def ActiveVar(self, index: "int64_t") -> "operations_research::IntVar *": r""" Returns the active variable of the node corresponding to index.""" return _pywrapcp.RoutingModel_ActiveVar(self, index) def ActiveVehicleVar(self, vehicle: "int") -> "operations_research::IntVar *": r""" Returns the active variable of the vehicle. It will be equal to 1 iff the route of the vehicle is not empty, 0 otherwise. """ return _pywrapcp.RoutingModel_ActiveVehicleVar(self, vehicle) def VehicleCostsConsideredVar(self, vehicle: "int") -> "operations_research::IntVar *": r""" Returns the variable specifying whether or not costs are considered for vehicle. """ return _pywrapcp.RoutingModel_VehicleCostsConsideredVar(self, vehicle) def VehicleVar(self, index: "int64_t") -> "operations_research::IntVar *": r""" Returns the vehicle variable of the node corresponding to index. Note that VehicleVar(index) == -1 is equivalent to ActiveVar(index) == 0. """ return _pywrapcp.RoutingModel_VehicleVar(self, index) def CostVar(self) -> "operations_research::IntVar *": r""" Returns the global cost variable which is being minimized.""" return _pywrapcp.RoutingModel_CostVar(self) def GetArcCostForVehicle(self, from_index: "int64_t", to_index: "int64_t", vehicle: "int64_t") -> "int64_t": r""" Returns the cost of the transit arc between two nodes for a given vehicle. Input are variable indices of node. This returns 0 if vehicle < 0. """ return _pywrapcp.RoutingModel_GetArcCostForVehicle(self, from_index, to_index, vehicle) def CostsAreHomogeneousAcrossVehicles(self) -> "bool": r""" Whether costs are homogeneous across all vehicles.""" return _pywrapcp.RoutingModel_CostsAreHomogeneousAcrossVehicles(self) def GetHomogeneousCost(self, from_index: "int64_t", to_index: "int64_t") -> "int64_t": r""" Returns the cost of the segment between two nodes supposing all vehicle costs are the same (returns the cost for the first vehicle otherwise). """ return _pywrapcp.RoutingModel_GetHomogeneousCost(self, from_index, to_index) def GetArcCostForFirstSolution(self, from_index: "int64_t", to_index: "int64_t") -> "int64_t": r""" Returns the cost of the arc in the context of the first solution strategy. This is typically a simplification of the actual cost; see the .cc. """ return _pywrapcp.RoutingModel_GetArcCostForFirstSolution(self, from_index, to_index) def GetArcCostForClass(self, from_index: "int64_t", to_index: "int64_t", cost_class_index: "int64_t") -> "int64_t": r""" Returns the cost of the segment between two nodes for a given cost class. Input are variable indices of nodes and the cost class. Unlike GetArcCostForVehicle(), if cost_class is kNoCost, then the returned cost won't necessarily be zero: only some of the components of the cost that depend on the cost class will be omited. See the code for details. """ return _pywrapcp.RoutingModel_GetArcCostForClass(self, from_index, to_index, cost_class_index) def GetCostClassIndexOfVehicle(self, vehicle: "int64_t") -> "operations_research::RoutingModel::CostClassIndex": r""" Get the cost class index of the given vehicle.""" return _pywrapcp.RoutingModel_GetCostClassIndexOfVehicle(self, vehicle) def HasVehicleWithCostClassIndex(self, cost_class_index: "operations_research::RoutingModel::CostClassIndex") -> "bool": r""" Returns true iff the model contains a vehicle with the given cost_class_index. """ return _pywrapcp.RoutingModel_HasVehicleWithCostClassIndex(self, cost_class_index) def GetCostClassesCount(self) -> "int": r""" Returns the number of different cost classes in the model.""" return _pywrapcp.RoutingModel_GetCostClassesCount(self) def GetNonZeroCostClassesCount(self) -> "int": r""" Ditto, minus the 'always zero', built-in cost class.""" return _pywrapcp.RoutingModel_GetNonZeroCostClassesCount(self) def GetVehicleClassIndexOfVehicle(self, vehicle: "int64_t") -> "operations_research::RoutingModel::VehicleClassIndex": return _pywrapcp.RoutingModel_GetVehicleClassIndexOfVehicle(self, vehicle) def GetVehicleClassesCount(self) -> "int": r""" Returns the number of different vehicle classes in the model.""" return _pywrapcp.RoutingModel_GetVehicleClassesCount(self) def GetSameVehicleIndicesOfIndex(self, node: "int") -> "std::vector< int > const &": r""" Returns variable indices of nodes constrained to be on the same route.""" return _pywrapcp.RoutingModel_GetSameVehicleIndicesOfIndex(self, node) def GetVehicleTypeContainer(self) -> "operations_research::RoutingModel::VehicleTypeContainer const &": return _pywrapcp.RoutingModel_GetVehicleTypeContainer(self) def ArcIsMoreConstrainedThanArc(self, _from: "int64_t", to1: "int64_t", to2: "int64_t") -> "bool": r""" Returns whether the arc from->to1 is more constrained than from->to2, taking into account, in order: - whether the destination node isn't an end node - whether the destination node is mandatory - whether the destination node is bound to the same vehicle as the source - the "primary constrained" dimension (see SetPrimaryConstrainedDimension) It then breaks ties using, in order: - the arc cost (taking unperformed penalties into account) - the size of the vehicle vars of "to1" and "to2" (lowest size wins) - the value: the lowest value of the indices to1 and to2 wins. See the .cc for details. The more constrained arc is typically preferable when building a first solution. This method is intended to be used as a callback for the BestValueByComparisonSelector value selector. Args: from: the variable index of the source node to1: the variable index of the first candidate destination node. to2: the variable index of the second candidate destination node. """ return _pywrapcp.RoutingModel_ArcIsMoreConstrainedThanArc(self, _from, to1, to2) def DebugOutputAssignment(self, solution_assignment: "Assignment", dimension_to_print: "std::string const &") -> "std::string": r""" Print some debugging information about an assignment, including the feasible intervals of the CumulVar for dimension "dimension_to_print" at each step of the routes. If "dimension_to_print" is omitted, all dimensions will be printed. """ return _pywrapcp.RoutingModel_DebugOutputAssignment(self, solution_assignment, dimension_to_print) def solver(self) -> "operations_research::Solver *": r""" Returns a vector cumul_bounds, for which cumul_bounds[i][j] is a pair containing the minimum and maximum of the CumulVar of the jth node on route i. - cumul_bounds[i][j].first is the minimum. - cumul_bounds[i][j].second is the maximum. Returns the underlying constraint solver. Can be used to add extra constraints and/or modify search algoithms. """ return _pywrapcp.RoutingModel_solver(self) def CheckLimit(self) -> "bool": r""" Returns true if the search limit has been crossed.""" return _pywrapcp.RoutingModel_CheckLimit(self) def RemainingTime(self) -> "absl::Duration": r""" Returns the time left in the search limit.""" return _pywrapcp.RoutingModel_RemainingTime(self) def nodes(self) -> "int": r""" Sizes and indices Returns the number of nodes in the model. """ return _pywrapcp.RoutingModel_nodes(self) def vehicles(self) -> "int": r""" Returns the number of vehicle routes in the model.""" return _pywrapcp.RoutingModel_vehicles(self) def Size(self) -> "int64_t": r""" Returns the number of next variables in the model.""" return _pywrapcp.RoutingModel_Size(self) def GetNumberOfDecisionsInFirstSolution(self, search_parameters: "operations_research::RoutingSearchParameters const &") -> "int64_t": r""" Returns statistics on first solution search, number of decisions sent to filters, number of decisions rejected by filters. """ return _pywrapcp.RoutingModel_GetNumberOfDecisionsInFirstSolution(self, search_parameters) def GetNumberOfRejectsInFirstSolution(self, search_parameters: "operations_research::RoutingSearchParameters const &") -> "int64_t": return _pywrapcp.RoutingModel_GetNumberOfRejectsInFirstSolution(self, search_parameters) def GetAutomaticFirstSolutionStrategy(self) -> "operations_research::FirstSolutionStrategy::Value": r""" Returns the automatic first solution strategy selected.""" return _pywrapcp.RoutingModel_GetAutomaticFirstSolutionStrategy(self) def IsMatchingModel(self) -> "bool": r""" Returns true if a vehicle/node matching problem is detected.""" return _pywrapcp.RoutingModel_IsMatchingModel(self) def MakeGuidedSlackFinalizer(self, dimension: "RoutingDimension", initializer: "std::function< int64_t (int64_t) >") -> "operations_research::DecisionBuilder *": r""" The next few members are in the public section only for testing purposes. MakeGuidedSlackFinalizer creates a DecisionBuilder for the slacks of a dimension using a callback to choose which values to start with. The finalizer works only when all next variables in the model have been fixed. It has the following two characteristics: 1. It follows the routes defined by the nexts variables when choosing a variable to make a decision on. 2. When it comes to choose a value for the slack of node i, the decision builder first calls the callback with argument i, and supposingly the returned value is x it creates decisions slack[i] = x, slack[i] = x + 1, slack[i] = x - 1, slack[i] = x + 2, etc. """ return _pywrapcp.RoutingModel_MakeGuidedSlackFinalizer(self, dimension, initializer) def MakeSelfDependentDimensionFinalizer(self, dimension: "RoutingDimension") -> "operations_research::DecisionBuilder *": r""" MakeSelfDependentDimensionFinalizer is a finalizer for the slacks of a self-dependent dimension. It makes an extensive use of the caches of the state dependent transits. In detail, MakeSelfDependentDimensionFinalizer returns a composition of a local search decision builder with a greedy descent operator for the cumul of the start of each route and a guided slack finalizer. Provided there are no time windows and the maximum slacks are large enough, once the cumul of the start of route is fixed, the guided finalizer can find optimal values of the slacks for the rest of the route in time proportional to the length of the route. Therefore the composed finalizer generally works in time O(log(t)*n*m), where t is the latest possible departute time, n is the number of nodes in the network and m is the number of vehicles. """ return _pywrapcp.RoutingModel_MakeSelfDependentDimensionFinalizer(self, dimension)Class variables
var ADDED_TYPE_REMOVED_FROM_VEHICLE-
When visited, one instance of type 'T' previously added to the route (TYPE_ADDED_TO_VEHICLE), if any, is removed from the vehicle. If the type was not previously added to the route or all added instances have already been removed, this visit has no effect on the types.
var PICKUP_AND_DELIVERY_FIFO-
Deliveries must be performed in the same order as pickups.
var PICKUP_AND_DELIVERY_LIFO-
Deliveries must be performed in reverse order of pickups.
var PICKUP_AND_DELIVERY_NO_ORDER-
Any precedence is accepted.
var ROUTING_FAIL-
No solution found to the problem after calling RoutingModel::Solve().
var ROUTING_FAIL_TIMEOUT-
Time limit reached before finding a solution with RoutingModel::Solve().
var ROUTING_INVALID-
Model, model parameters or flags are not valid.
var ROUTING_NOT_SOLVED-
Problem not solved yet (before calling RoutingModel::Solve()).
var ROUTING_SUCCESS-
Problem solved successfully after calling RoutingModel::Solve().
var TYPE_ADDED_TO_VEHICLE-
When visited, the number of types 'T' on the vehicle increases by one.
var TYPE_ON_VEHICLE_UP_TO_VISIT-
With the following policy, the visit enforces that type 'T' is considered on the route from its start until this node is visited.
var TYPE_SIMULTANEOUSLY_ADDED_AND_REMOVED-
The visit doesn't have an impact on the number of types 'T' on the route, as it's (virtually) added and removed directly. This policy can be used for visits which are part of an incompatibility or requirement set without affecting the type count on the route.
var kNoDimensionvar kNoDisjunctionvar kNoPenalty
Instance variables
var thisown-
The membership flag
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thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
Methods
def ActiveVar(self, index: int64_t) ‑> operations_research::IntVar *-
Returns the active variable of the node corresponding to index.
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def ActiveVar(self, index: "int64_t") -> "operations_research::IntVar *": r""" Returns the active variable of the node corresponding to index.""" return _pywrapcp.RoutingModel_ActiveVar(self, index) def ActiveVehicleVar(self, vehicle: int) ‑> operations_research::IntVar *-
Returns the active variable of the vehicle. It will be equal to 1 iff the route of the vehicle is not empty, 0 otherwise.
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def ActiveVehicleVar(self, vehicle: "int") -> "operations_research::IntVar *": r""" Returns the active variable of the vehicle. It will be equal to 1 iff the route of the vehicle is not empty, 0 otherwise. """ return _pywrapcp.RoutingModel_ActiveVehicleVar(self, vehicle) def AddAtSolutionCallback(self, callback: std::function< void () >) ‑> void-
Adds a callback called each time a solution is found during the search. This is a shortcut to creating a monitor to call the callback on AtSolution() and adding it with AddSearchMonitor.
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def AddAtSolutionCallback(self, callback: "std::function< void () >") -> "void": r""" Adds a callback called each time a solution is found during the search. This is a shortcut to creating a monitor to call the callback on AtSolution() and adding it with AddSearchMonitor. """ return _pywrapcp.RoutingModel_AddAtSolutionCallback(self, callback) def AddConstantDimension(self, value: int64_t, capacity: int64_t, fix_start_cumul_to_zero: bool, name: std::string const &) ‑> std::pair< int,bool >-
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def AddConstantDimension(self, value: "int64_t", capacity: "int64_t", fix_start_cumul_to_zero: "bool", name: "std::string const &") -> "std::pair< int,bool >": return _pywrapcp.RoutingModel_AddConstantDimension(self, value, capacity, fix_start_cumul_to_zero, name) def AddConstantDimensionWithSlack(self, value: int64_t, capacity: int64_t, slack_max: int64_t, fix_start_cumul_to_zero: bool, name: std::string const &) ‑> std::pair< int,bool >-
Creates a dimension where the transit variable is constrained to be equal to 'value'; 'capacity' is the upper bound of the cumul variables. 'name' is the name used to reference the dimension; this name is used to get cumul and transit variables from the routing model. Returns a pair consisting of an index to the registered unary transit callback and a bool denoting whether the dimension has been created. It is false if a dimension with the same name has already been created (and doesn't create the new dimension but still register a new callback).
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def AddConstantDimensionWithSlack(self, value: "int64_t", capacity: "int64_t", slack_max: "int64_t", fix_start_cumul_to_zero: "bool", name: "std::string const &") -> "std::pair< int,bool >": r""" Creates a dimension where the transit variable is constrained to be equal to 'value'; 'capacity' is the upper bound of the cumul variables. 'name' is the name used to reference the dimension; this name is used to get cumul and transit variables from the routing model. Returns a pair consisting of an index to the registered unary transit callback and a bool denoting whether the dimension has been created. It is false if a dimension with the same name has already been created (and doesn't create the new dimension but still register a new callback). """ return _pywrapcp.RoutingModel_AddConstantDimensionWithSlack(self, value, capacity, slack_max, fix_start_cumul_to_zero, name) def AddDimension(self, evaluator_index: int, slack_max: int64_t, capacity: int64_t, fix_start_cumul_to_zero: bool, name: std::string const &) ‑> bool-
Model creation Methods to add dimensions to routes; dimensions represent quantities accumulated at nodes along the routes. They represent quantities such as weights or volumes carried along the route, or distance or times. Quantities at a node are represented by "cumul" variables and the increase or decrease of quantities between nodes are represented by "transit" variables. These variables are linked as follows: if j == next(i), cumul(j) = cumul(i) + transit(i) + slack(i) where slack is a positive slack variable (can represent waiting times for a time dimension). Setting the value of fix_start_cumul_to_zero to true will force the "cumul" variable of the start node of all vehicles to be equal to 0. Creates a dimension where the transit variable is constrained to be equal to evaluator(i, next(i)); 'slack_max' is the upper bound of the slack variable and 'capacity' is the upper bound of the cumul variables. 'name' is the name used to reference the dimension; this name is used to get cumul and transit variables from the routing model. Returns false if a dimension with the same name has already been created (and doesn't create the new dimension). Takes ownership of the callback 'evaluator'.
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def AddDimension(self, evaluator_index: "int", slack_max: "int64_t", capacity: "int64_t", fix_start_cumul_to_zero: "bool", name: "std::string const &") -> "bool": r""" Model creation Methods to add dimensions to routes; dimensions represent quantities accumulated at nodes along the routes. They represent quantities such as weights or volumes carried along the route, or distance or times. Quantities at a node are represented by "cumul" variables and the increase or decrease of quantities between nodes are represented by "transit" variables. These variables are linked as follows: if j == next(i), cumul(j) = cumul(i) + transit(i) + slack(i) where slack is a positive slack variable (can represent waiting times for a time dimension). Setting the value of fix_start_cumul_to_zero to true will force the "cumul" variable of the start node of all vehicles to be equal to 0. Creates a dimension where the transit variable is constrained to be equal to evaluator(i, next(i)); 'slack_max' is the upper bound of the slack variable and 'capacity' is the upper bound of the cumul variables. 'name' is the name used to reference the dimension; this name is used to get cumul and transit variables from the routing model. Returns false if a dimension with the same name has already been created (and doesn't create the new dimension). Takes ownership of the callback 'evaluator'. """ return _pywrapcp.RoutingModel_AddDimension(self, evaluator_index, slack_max, capacity, fix_start_cumul_to_zero, name) def AddDimensionWithVehicleCapacity(self, evaluator_index: int, slack_max: int64_t, vehicle_capacities: std::vector< int64_t >, fix_start_cumul_to_zero: bool, name: std::string const &) ‑> bool-
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def AddDimensionWithVehicleCapacity(self, evaluator_index: "int", slack_max: "int64_t", vehicle_capacities: "std::vector< int64_t >", fix_start_cumul_to_zero: "bool", name: "std::string const &") -> "bool": return _pywrapcp.RoutingModel_AddDimensionWithVehicleCapacity(self, evaluator_index, slack_max, vehicle_capacities, fix_start_cumul_to_zero, name) def AddDimensionWithVehicleTransitAndCapacity(self, evaluator_indices: std::vector< int > const &, slack_max: int64_t, vehicle_capacities: std::vector< int64_t >, fix_start_cumul_to_zero: bool, name: std::string const &) ‑> bool-
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def AddDimensionWithVehicleTransitAndCapacity(self, evaluator_indices: "std::vector< int > const &", slack_max: "int64_t", vehicle_capacities: "std::vector< int64_t >", fix_start_cumul_to_zero: "bool", name: "std::string const &") -> "bool": return _pywrapcp.RoutingModel_AddDimensionWithVehicleTransitAndCapacity(self, evaluator_indices, slack_max, vehicle_capacities, fix_start_cumul_to_zero, name) def AddDimensionWithVehicleTransits(self, evaluator_indices: std::vector< int > const &, slack_max: int64_t, capacity: int64_t, fix_start_cumul_to_zero: bool, name: std::string const &) ‑> bool-
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def AddDimensionWithVehicleTransits(self, evaluator_indices: "std::vector< int > const &", slack_max: "int64_t", capacity: "int64_t", fix_start_cumul_to_zero: "bool", name: "std::string const &") -> "bool": return _pywrapcp.RoutingModel_AddDimensionWithVehicleTransits(self, evaluator_indices, slack_max, capacity, fix_start_cumul_to_zero, name) def AddDisjunction(self, *args) ‑> operations_research::RoutingModel::DisjunctionIndex-
Adds a disjunction constraint on the indices: exactly 'max_cardinality' of the indices are active. Start and end indices of any vehicle cannot be part of a disjunction.
If a penalty is given, at most 'max_cardinality' of the indices can be active, and if less are active, 'penalty' is payed per inactive index. This is equivalent to adding the constraint: p + Sum(i)active[i] == max_cardinality where p is an integer variable, and the following cost to the cost function: p * penalty. 'penalty' must be positive to make the disjunction optional; a negative penalty will force 'max_cardinality' indices of the disjunction to be performed, and therefore p == 0. Note: passing a vector with a single index will model an optional index with a penalty cost if it is not visited.
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def AddDisjunction(self, *args) -> "operations_research::RoutingModel::DisjunctionIndex": r""" Adds a disjunction constraint on the indices: exactly 'max_cardinality' of the indices are active. Start and end indices of any vehicle cannot be part of a disjunction. If a penalty is given, at most 'max_cardinality' of the indices can be active, and if less are active, 'penalty' is payed per inactive index. This is equivalent to adding the constraint: p + Sum(i)active[i] == max_cardinality where p is an integer variable, and the following cost to the cost function: p * penalty. 'penalty' must be positive to make the disjunction optional; a negative penalty will force 'max_cardinality' indices of the disjunction to be performed, and therefore p == 0. Note: passing a vector with a single index will model an optional index with a penalty cost if it is not visited. """ return _pywrapcp.RoutingModel_AddDisjunction(self, *args) def AddHardTypeIncompatibility(self, type1: int, type2: int) ‑> void-
Incompatibilities: Two nodes with "hard" incompatible types cannot share the same route at all, while with a "temporal" incompatibility they can't be on the same route at the same time.
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def AddHardTypeIncompatibility(self, type1: "int", type2: "int") -> "void": r""" Incompatibilities: Two nodes with "hard" incompatible types cannot share the same route at all, while with a "temporal" incompatibility they can't be on the same route at the same time. """ return _pywrapcp.RoutingModel_AddHardTypeIncompatibility(self, type1, type2) def AddIntervalToAssignment(self, interval: IntervalVar) ‑> void-
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def AddIntervalToAssignment(self, interval: "IntervalVar") -> "void": return _pywrapcp.RoutingModel_AddIntervalToAssignment(self, interval) def AddLocalSearchFilter(self, filter: LocalSearchFilter) ‑> void-
Adds a custom local search filter to the list of filters used to speed up local search by pruning unfeasible variable assignments. Calling this method after the routing model has been closed (CloseModel() or Solve() has been called) has no effect. The routing model does not take ownership of the filter.
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def AddLocalSearchFilter(self, filter: "LocalSearchFilter") -> "void": r""" Adds a custom local search filter to the list of filters used to speed up local search by pruning unfeasible variable assignments. Calling this method after the routing model has been closed (CloseModel() or Solve() has been called) has no effect. The routing model does not take ownership of the filter. """ return _pywrapcp.RoutingModel_AddLocalSearchFilter(self, filter) def AddLocalSearchOperator(self, ls_operator: LocalSearchOperator) ‑> void-
Adds a local search operator to the set of operators used to solve the vehicle routing problem.
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def AddLocalSearchOperator(self, ls_operator: "LocalSearchOperator") -> "void": r""" Adds a local search operator to the set of operators used to solve the vehicle routing problem. """ return _pywrapcp.RoutingModel_AddLocalSearchOperator(self, ls_operator) def AddMatrixDimension(self, values: std::vector< std::vector< int64_t > >, capacity: int64_t, fix_start_cumul_to_zero: bool, name: std::string const &) ‑> std::pair< int,bool >-
Creates a dimension where the transit variable is constrained to be equal to 'values[i][next(i)]' for node i; 'capacity' is the upper bound of the cumul variables. 'name' is the name used to reference the dimension; this name is used to get cumul and transit variables from the routing model. Returns a pair consisting of an index to the registered transit callback and a bool denoting whether the dimension has been created. It is false if a dimension with the same name has already been created (and doesn't create the new dimension but still register a new callback).
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def AddMatrixDimension(self, values: "std::vector< std::vector< int64_t > >", capacity: "int64_t", fix_start_cumul_to_zero: "bool", name: "std::string const &") -> "std::pair< int,bool >": r""" Creates a dimension where the transit variable is constrained to be equal to 'values[i][next(i)]' for node i; 'capacity' is the upper bound of the cumul variables. 'name' is the name used to reference the dimension; this name is used to get cumul and transit variables from the routing model. Returns a pair consisting of an index to the registered transit callback and a bool denoting whether the dimension has been created. It is false if a dimension with the same name has already been created (and doesn't create the new dimension but still register a new callback). """ return _pywrapcp.RoutingModel_AddMatrixDimension(self, values, capacity, fix_start_cumul_to_zero, name) def AddPickupAndDelivery(self, pickup: int64_t, delivery: int64_t) ‑> void-
Notifies that index1 and index2 form a pair of nodes which should belong to the same route. This methods helps the search find better solutions, especially in the local search phase. It should be called each time you have an equality constraint linking the vehicle variables of two node (including for instance pickup and delivery problems): Solver* const solver = routing.solver(); int64_t index1 = manager.NodeToIndex(node1); int64_t index2 = manager.NodeToIndex(node2); solver->AddConstraint(solver->MakeEquality( routing.VehicleVar(index1), routing.VehicleVar(index2))); routing.AddPickupAndDelivery(index1, index2);
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def AddPickupAndDelivery(self, pickup: "int64_t", delivery: "int64_t") -> "void": r""" Notifies that index1 and index2 form a pair of nodes which should belong to the same route. This methods helps the search find better solutions, especially in the local search phase. It should be called each time you have an equality constraint linking the vehicle variables of two node (including for instance pickup and delivery problems): Solver* const solver = routing.solver(); int64_t index1 = manager.NodeToIndex(node1); int64_t index2 = manager.NodeToIndex(node2); solver->AddConstraint(solver->MakeEquality( routing.VehicleVar(index1), routing.VehicleVar(index2))); routing.AddPickupAndDelivery(index1, index2); """ return _pywrapcp.RoutingModel_AddPickupAndDelivery(self, pickup, delivery) def AddPickupAndDeliverySets(self, pickup_disjunction: operations_research::RoutingModel::DisjunctionIndex, delivery_disjunction: operations_research::RoutingModel::DisjunctionIndex) ‑> void-
Same as AddPickupAndDelivery but notifying that the performed node from the disjunction of index 'pickup_disjunction' is on the same route as the performed node from the disjunction of index 'delivery_disjunction'.
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def AddPickupAndDeliverySets(self, pickup_disjunction: "operations_research::RoutingModel::DisjunctionIndex", delivery_disjunction: "operations_research::RoutingModel::DisjunctionIndex") -> "void": r""" Same as AddPickupAndDelivery but notifying that the performed node from the disjunction of index 'pickup_disjunction' is on the same route as the performed node from the disjunction of index 'delivery_disjunction'. """ return _pywrapcp.RoutingModel_AddPickupAndDeliverySets(self, pickup_disjunction, delivery_disjunction) def AddRequiredTypeAlternativesWhenAddingType(self, dependent_type: int, required_type_alternatives: absl::flat_hash_set< int >) ‑> void-
If type_D depends on type_R when adding type_D, any node_D of type_D and VisitTypePolicy TYPE_ADDED_TO_VEHICLE or TYPE_SIMULTANEOUSLY_ADDED_AND_REMOVED requires at least one type_R on its vehicle at the time node_D is visited.
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def AddRequiredTypeAlternativesWhenAddingType(self, dependent_type: "int", required_type_alternatives: "absl::flat_hash_set< int >") -> "void": r""" If type_D depends on type_R when adding type_D, any node_D of type_D and VisitTypePolicy TYPE_ADDED_TO_VEHICLE or TYPE_SIMULTANEOUSLY_ADDED_AND_REMOVED requires at least one type_R on its vehicle at the time node_D is visited. """ return _pywrapcp.RoutingModel_AddRequiredTypeAlternativesWhenAddingType(self, dependent_type, required_type_alternatives) def AddRequiredTypeAlternativesWhenRemovingType(self, dependent_type: int, required_type_alternatives: absl::flat_hash_set< int >) ‑> void-
The following requirements apply when visiting dependent nodes that remove their type from the route, i.e. type_R must be on the vehicle when type_D of VisitTypePolicy ADDED_TYPE_REMOVED_FROM_VEHICLE, TYPE_ON_VEHICLE_UP_TO_VISIT or TYPE_SIMULTANEOUSLY_ADDED_AND_REMOVED is visited.
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def AddRequiredTypeAlternativesWhenRemovingType(self, dependent_type: "int", required_type_alternatives: "absl::flat_hash_set< int >") -> "void": r""" The following requirements apply when visiting dependent nodes that remove their type from the route, i.e. type_R must be on the vehicle when type_D of VisitTypePolicy ADDED_TYPE_REMOVED_FROM_VEHICLE, TYPE_ON_VEHICLE_UP_TO_VISIT or TYPE_SIMULTANEOUSLY_ADDED_AND_REMOVED is visited. """ return _pywrapcp.RoutingModel_AddRequiredTypeAlternativesWhenRemovingType(self, dependent_type, required_type_alternatives) def AddSameVehicleRequiredTypeAlternatives(self, dependent_type: int, required_type_alternatives: absl::flat_hash_set< int >) ‑> void-
Requirements: NOTE: As of 2019-04, cycles in the requirement graph are not supported, and lead to the dependent nodes being skipped if possible (otherwise the model is considered infeasible). The following functions specify that "dependent_type" requires at least one of the types in "required_type_alternatives".
For same-vehicle requirements, a node of dependent type type_D requires at least one node of type type_R among the required alternatives on the same route.
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def AddSameVehicleRequiredTypeAlternatives(self, dependent_type: "int", required_type_alternatives: "absl::flat_hash_set< int >") -> "void": r""" Requirements: NOTE: As of 2019-04, cycles in the requirement graph are not supported, and lead to the dependent nodes being skipped if possible (otherwise the model is considered infeasible). The following functions specify that "dependent_type" requires at least one of the types in "required_type_alternatives". For same-vehicle requirements, a node of dependent type type_D requires at least one node of type type_R among the required alternatives on the same route. """ return _pywrapcp.RoutingModel_AddSameVehicleRequiredTypeAlternatives(self, dependent_type, required_type_alternatives) def AddSearchMonitor(self, monitor: SearchMonitor) ‑> void-
Adds a search monitor to the search used to solve the routing model.
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def AddSearchMonitor(self, monitor: "SearchMonitor") -> "void": r""" Adds a search monitor to the search used to solve the routing model.""" return _pywrapcp.RoutingModel_AddSearchMonitor(self, monitor) def AddSoftSameVehicleConstraint(self, indices: std::vector< int64_t > const &, cost: int64_t) ‑> void-
Adds a soft constraint to force a set of variable indices to be on the same vehicle. If all nodes are not on the same vehicle, each extra vehicle used adds 'cost' to the cost function.
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def AddSoftSameVehicleConstraint(self, indices: "std::vector< int64_t > const &", cost: "int64_t") -> "void": r""" Adds a soft constraint to force a set of variable indices to be on the same vehicle. If all nodes are not on the same vehicle, each extra vehicle used adds 'cost' to the cost function. """ return _pywrapcp.RoutingModel_AddSoftSameVehicleConstraint(self, indices, cost) def AddTemporalTypeIncompatibility(self, type1: int, type2: int) ‑> void-
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def AddTemporalTypeIncompatibility(self, type1: "int", type2: "int") -> "void": return _pywrapcp.RoutingModel_AddTemporalTypeIncompatibility(self, type1, type2) def AddToAssignment(self, var: IntVar) ‑> void-
Adds an extra variable to the vehicle routing assignment.
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def AddToAssignment(self, var: "IntVar") -> "void": r""" Adds an extra variable to the vehicle routing assignment.""" return _pywrapcp.RoutingModel_AddToAssignment(self, var) def AddVariableMaximizedByFinalizer(self, var: IntVar) ‑> void-
Adds a variable to maximize in the solution finalizer (see above for information on the solution finalizer).
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def AddVariableMaximizedByFinalizer(self, var: "IntVar") -> "void": r""" Adds a variable to maximize in the solution finalizer (see above for information on the solution finalizer). """ return _pywrapcp.RoutingModel_AddVariableMaximizedByFinalizer(self, var) def AddVariableMinimizedByFinalizer(self, var: IntVar) ‑> void-
Adds a variable to minimize in the solution finalizer. The solution finalizer is called each time a solution is found during the search and allows to instantiate secondary variables (such as dimension cumul variables).
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def AddVariableMinimizedByFinalizer(self, var: "IntVar") -> "void": r""" Adds a variable to minimize in the solution finalizer. The solution finalizer is called each time a solution is found during the search and allows to instantiate secondary variables (such as dimension cumul variables). """ return _pywrapcp.RoutingModel_AddVariableMinimizedByFinalizer(self, var) def AddVariableTargetToFinalizer(self, var: IntVar, target: int64_t) ‑> void-
Add a variable to set the closest possible to the target value in the solution finalizer.
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def AddVariableTargetToFinalizer(self, var: "IntVar", target: "int64_t") -> "void": r""" Add a variable to set the closest possible to the target value in the solution finalizer. """ return _pywrapcp.RoutingModel_AddVariableTargetToFinalizer(self, var, target) def AddVectorDimension(self, values: std::vector< int64_t >, capacity: int64_t, fix_start_cumul_to_zero: bool, name: std::string const &) ‑> std::pair< int,bool >-
Creates a dimension where the transit variable is constrained to be equal to 'values[i]' for node i; 'capacity' is the upper bound of the cumul variables. 'name' is the name used to reference the dimension; this name is used to get cumul and transit variables from the routing model. Returns a pair consisting of an index to the registered unary transit callback and a bool denoting whether the dimension has been created. It is false if a dimension with the same name has already been created (and doesn't create the new dimension but still register a new callback).
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def AddVectorDimension(self, values: "std::vector< int64_t >", capacity: "int64_t", fix_start_cumul_to_zero: "bool", name: "std::string const &") -> "std::pair< int,bool >": r""" Creates a dimension where the transit variable is constrained to be equal to 'values[i]' for node i; 'capacity' is the upper bound of the cumul variables. 'name' is the name used to reference the dimension; this name is used to get cumul and transit variables from the routing model. Returns a pair consisting of an index to the registered unary transit callback and a bool denoting whether the dimension has been created. It is false if a dimension with the same name has already been created (and doesn't create the new dimension but still register a new callback). """ return _pywrapcp.RoutingModel_AddVectorDimension(self, values, capacity, fix_start_cumul_to_zero, name) def AddWeightedVariableMinimizedByFinalizer(self, var: IntVar, cost: int64_t) ‑> void-
Adds a variable to minimize in the solution finalizer, with a weighted priority: the higher the more priority it has.
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def AddWeightedVariableMinimizedByFinalizer(self, var: "IntVar", cost: "int64_t") -> "void": r""" Adds a variable to minimize in the solution finalizer, with a weighted priority: the higher the more priority it has. """ return _pywrapcp.RoutingModel_AddWeightedVariableMinimizedByFinalizer(self, var, cost) def ApplyLocks(self, locks: std::vector< int64_t > const &) ‑> operations_research::IntVar *-
Applies a lock chain to the next search. 'locks' represents an ordered vector of nodes representing a partial route which will be fixed during the next search; it will constrain next variables such that: next[locks[i]] == locks[i+1].
Returns the next variable at the end of the locked chain; this variable is not locked. An assignment containing the locks can be obtained by calling PreAssignment().
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def ApplyLocks(self, locks: "std::vector< int64_t > const &") -> "operations_research::IntVar *": r""" Applies a lock chain to the next search. 'locks' represents an ordered vector of nodes representing a partial route which will be fixed during the next search; it will constrain next variables such that: next[locks[i]] == locks[i+1]. Returns the next variable at the end of the locked chain; this variable is not locked. An assignment containing the locks can be obtained by calling PreAssignment(). """ return _pywrapcp.RoutingModel_ApplyLocks(self, locks) def ApplyLocksToAllVehicles(self, locks: std::vector< std::vector< int64_t > > const &, close_routes: bool) ‑> bool-
Applies lock chains to all vehicles to the next search, such that locks[p] is the lock chain for route p. Returns false if the locks do not contain valid routes; expects that the routes do not contain the depots, i.e. there are empty vectors in place of empty routes. If close_routes is set to true, adds the end nodes to the route of each vehicle and deactivates other nodes. An assignment containing the locks can be obtained by calling PreAssignment().
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def ApplyLocksToAllVehicles(self, locks: "std::vector< std::vector< int64_t > > const &", close_routes: "bool") -> "bool": r""" Applies lock chains to all vehicles to the next search, such that locks[p] is the lock chain for route p. Returns false if the locks do not contain valid routes; expects that the routes do not contain the depots, i.e. there are empty vectors in place of empty routes. If close_routes is set to true, adds the end nodes to the route of each vehicle and deactivates other nodes. An assignment containing the locks can be obtained by calling PreAssignment(). """ return _pywrapcp.RoutingModel_ApplyLocksToAllVehicles(self, locks, close_routes) def ArcIsMoreConstrainedThanArc(self, _from: int64_t, to1: int64_t, to2: int64_t) ‑> bool-
Returns whether the arc from->to1 is more constrained than from->to2, taking into account, in order: - whether the destination node isn't an end node - whether the destination node is mandatory - whether the destination node is bound to the same vehicle as the source - the "primary constrained" dimension (see SetPrimaryConstrainedDimension) It then breaks ties using, in order: - the arc cost (taking unperformed penalties into account) - the size of the vehicle vars of "to1" and "to2" (lowest size wins) - the value: the lowest value of the indices to1 and to2 wins. See the .cc for details. The more constrained arc is typically preferable when building a first solution. This method is intended to be used as a callback for the BestValueByComparisonSelector value selector.
Args
from- the variable index of the source node
to1- the variable index of the first candidate destination node.
to2- the variable index of the second candidate destination node.
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def ArcIsMoreConstrainedThanArc(self, _from: "int64_t", to1: "int64_t", to2: "int64_t") -> "bool": r""" Returns whether the arc from->to1 is more constrained than from->to2, taking into account, in order: - whether the destination node isn't an end node - whether the destination node is mandatory - whether the destination node is bound to the same vehicle as the source - the "primary constrained" dimension (see SetPrimaryConstrainedDimension) It then breaks ties using, in order: - the arc cost (taking unperformed penalties into account) - the size of the vehicle vars of "to1" and "to2" (lowest size wins) - the value: the lowest value of the indices to1 and to2 wins. See the .cc for details. The more constrained arc is typically preferable when building a first solution. This method is intended to be used as a callback for the BestValueByComparisonSelector value selector. Args: from: the variable index of the source node to1: the variable index of the first candidate destination node. to2: the variable index of the second candidate destination node. """ return _pywrapcp.RoutingModel_ArcIsMoreConstrainedThanArc(self, _from, to1, to2) def AreEmptyRouteCostsConsideredForVehicle(self, vehicle: int) ‑> bool-
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def AreEmptyRouteCostsConsideredForVehicle(self, vehicle: "int") -> "bool": return _pywrapcp.RoutingModel_AreEmptyRouteCostsConsideredForVehicle(self, vehicle) def AssignmentToRoutes(self, assignment: Assignment, routes: std::vector< std::vector< int64_t > > *const) ‑> void-
Converts the solution in the given assignment to routes for all vehicles. Expects that assignment contains a valid solution (i.e. routes for all vehicles end with an end index for that vehicle).
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def AssignmentToRoutes(self, assignment: "Assignment", routes: "std::vector< std::vector< int64_t > > *const") -> "void": r""" Converts the solution in the given assignment to routes for all vehicles. Expects that assignment contains a valid solution (i.e. routes for all vehicles end with an end index for that vehicle). """ return _pywrapcp.RoutingModel_AssignmentToRoutes(self, assignment, routes) def CheckLimit(self) ‑> bool-
Returns true if the search limit has been crossed.
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def CheckLimit(self) -> "bool": r""" Returns true if the search limit has been crossed.""" return _pywrapcp.RoutingModel_CheckLimit(self) def CloseModel(self) ‑> void-
Closes the current routing model; after this method is called, no modification to the model can be done, but RoutesToAssignment becomes available. Note that CloseModel() is automatically called by Solve() and other methods that produce solution. This is equivalent to calling CloseModelWithParameters(DefaultRoutingSearchParameters()).
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def CloseModel(self) -> "void": r""" Closes the current routing model; after this method is called, no modification to the model can be done, but RoutesToAssignment becomes available. Note that CloseModel() is automatically called by Solve() and other methods that produce solution. This is equivalent to calling CloseModelWithParameters(DefaultRoutingSearchParameters()). """ return _pywrapcp.RoutingModel_CloseModel(self) def CloseModelWithParameters(self, search_parameters: operations_research::RoutingSearchParameters const &) ‑> void-
Same as above taking search parameters (as of 10/2015 some the parameters have to be set when closing the model).
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def CloseModelWithParameters(self, search_parameters: "operations_research::RoutingSearchParameters const &") -> "void": r""" Same as above taking search parameters (as of 10/2015 some the parameters have to be set when closing the model). """ return _pywrapcp.RoutingModel_CloseModelWithParameters(self, search_parameters) def CloseVisitTypes(self) ‑> void-
This function should be called once all node visit types have been set and prior to adding any incompatibilities/requirements. "close" types.
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def CloseVisitTypes(self) -> "void": r""" This function should be called once all node visit types have been set and prior to adding any incompatibilities/requirements. "close" types. """ return _pywrapcp.RoutingModel_CloseVisitTypes(self) def CompactAndCheckAssignment(self, assignment: Assignment) ‑> operations_research::Assignment *-
Same as CompactAssignment() but also checks the validity of the final compact solution; if it is not valid, no attempts to repair it are made (instead, the method returns nullptr).
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def CompactAndCheckAssignment(self, assignment: "Assignment") -> "operations_research::Assignment *": r""" Same as CompactAssignment() but also checks the validity of the final compact solution; if it is not valid, no attempts to repair it are made (instead, the method returns nullptr). """ return _pywrapcp.RoutingModel_CompactAndCheckAssignment(self, assignment) def CompactAssignment(self, assignment: Assignment) ‑> operations_research::Assignment *-
Converts the solution in the given assignment to routes for all vehicles. If the returned vector is route_indices, route_indices[i][j] is the index for jth location visited on route i. Note that contrary to AssignmentToRoutes, the vectors do include start and end locations. Returns a compacted version of the given assignment, in which all vehicles with id lower or equal to some N have non-empty routes, and all vehicles with id greater than N have empty routes. Does not take ownership of the returned object. If found, the cost of the compact assignment is the same as in the original assignment and it preserves the values of 'active' variables. Returns nullptr if a compact assignment was not found. This method only works in homogenous mode, and it only swaps equivalent vehicles (vehicles with the same start and end nodes). When creating the compact assignment, the empty plan is replaced by the route assigned to the compatible vehicle with the highest id. Note that with more complex constraints on vehicle variables, this method might fail even if a compact solution exists. This method changes the vehicle and dimension variables as necessary. While compacting the solution, only basic checks on vehicle variables are performed; if one of these checks fails no attempts to repair it are made (instead, the method returns nullptr).
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def CompactAssignment(self, assignment: "Assignment") -> "operations_research::Assignment *": r""" Converts the solution in the given assignment to routes for all vehicles. If the returned vector is route_indices, route_indices[i][j] is the index for jth location visited on route i. Note that contrary to AssignmentToRoutes, the vectors do include start and end locations. Returns a compacted version of the given assignment, in which all vehicles with id lower or equal to some N have non-empty routes, and all vehicles with id greater than N have empty routes. Does not take ownership of the returned object. If found, the cost of the compact assignment is the same as in the original assignment and it preserves the values of 'active' variables. Returns nullptr if a compact assignment was not found. This method only works in homogenous mode, and it only swaps equivalent vehicles (vehicles with the same start and end nodes). When creating the compact assignment, the empty plan is replaced by the route assigned to the compatible vehicle with the highest id. Note that with more complex constraints on vehicle variables, this method might fail even if a compact solution exists. This method changes the vehicle and dimension variables as necessary. While compacting the solution, only basic checks on vehicle variables are performed; if one of these checks fails no attempts to repair it are made (instead, the method returns nullptr). """ return _pywrapcp.RoutingModel_CompactAssignment(self, assignment) def ComputeLowerBound(self) ‑> int64_t-
Computes a lower bound to the routing problem solving a linear assignment problem. The routing model must be closed before calling this method. Note that problems with node disjunction constraints (including optional nodes) and non-homogenous costs are not supported (the method returns 0 in these cases).
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def ComputeLowerBound(self) -> "int64_t": r""" Computes a lower bound to the routing problem solving a linear assignment problem. The routing model must be closed before calling this method. Note that problems with node disjunction constraints (including optional nodes) and non-homogenous costs are not supported (the method returns 0 in these cases). """ return _pywrapcp.RoutingModel_ComputeLowerBound(self) def ConsiderEmptyRouteCostsForVehicle(self, consider_costs: bool, vehicle: int) ‑> void-
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def ConsiderEmptyRouteCostsForVehicle(self, consider_costs: "bool", vehicle: "int") -> "void": return _pywrapcp.RoutingModel_ConsiderEmptyRouteCostsForVehicle(self, consider_costs, vehicle) def CostVar(self) ‑> operations_research::IntVar *-
Returns the global cost variable which is being minimized.
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def CostVar(self) -> "operations_research::IntVar *": r""" Returns the global cost variable which is being minimized.""" return _pywrapcp.RoutingModel_CostVar(self) def CostsAreHomogeneousAcrossVehicles(self) ‑> bool-
Whether costs are homogeneous across all vehicles.
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def CostsAreHomogeneousAcrossVehicles(self) -> "bool": r""" Whether costs are homogeneous across all vehicles.""" return _pywrapcp.RoutingModel_CostsAreHomogeneousAcrossVehicles(self) def DebugOutputAssignment(self, solution_assignment: Assignment, dimension_to_print: std::string const &) ‑> std::string-
Print some debugging information about an assignment, including the feasible intervals of the CumulVar for dimension "dimension_to_print" at each step of the routes. If "dimension_to_print" is omitted, all dimensions will be printed.
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def DebugOutputAssignment(self, solution_assignment: "Assignment", dimension_to_print: "std::string const &") -> "std::string": r""" Print some debugging information about an assignment, including the feasible intervals of the CumulVar for dimension "dimension_to_print" at each step of the routes. If "dimension_to_print" is omitted, all dimensions will be printed. """ return _pywrapcp.RoutingModel_DebugOutputAssignment(self, solution_assignment, dimension_to_print) def End(self, vehicle: int) ‑> int64_t-
Returns the variable index of the ending node of a vehicle route.
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def End(self, vehicle: "int") -> "int64_t": r""" Returns the variable index of the ending node of a vehicle route.""" return _pywrapcp.RoutingModel_End(self, vehicle) def GetAllDimensionNames(self) ‑> std::vector< std::string >-
Outputs the names of all dimensions added to the routing engine.
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def GetAllDimensionNames(self) -> "std::vector< std::string >": r""" Outputs the names of all dimensions added to the routing engine.""" return _pywrapcp.RoutingModel_GetAllDimensionNames(self) def GetAmortizedLinearCostFactorOfVehicles(self) ‑> std::vector< int64_t > const &-
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def GetAmortizedLinearCostFactorOfVehicles(self) -> "std::vector< int64_t > const &": return _pywrapcp.RoutingModel_GetAmortizedLinearCostFactorOfVehicles(self) def GetAmortizedQuadraticCostFactorOfVehicles(self) ‑> std::vector< int64_t > const &-
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def GetAmortizedQuadraticCostFactorOfVehicles(self) -> "std::vector< int64_t > const &": return _pywrapcp.RoutingModel_GetAmortizedQuadraticCostFactorOfVehicles(self) def GetArcCostForClass(self, from_index: int64_t, to_index: int64_t, cost_class_index: int64_t) ‑> int64_t-
Returns the cost of the segment between two nodes for a given cost class. Input are variable indices of nodes and the cost class. Unlike GetArcCostForVehicle(), if cost_class is kNoCost, then the returned cost won't necessarily be zero: only some of the components of the cost that depend on the cost class will be omited. See the code for details.
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def GetArcCostForClass(self, from_index: "int64_t", to_index: "int64_t", cost_class_index: "int64_t") -> "int64_t": r""" Returns the cost of the segment between two nodes for a given cost class. Input are variable indices of nodes and the cost class. Unlike GetArcCostForVehicle(), if cost_class is kNoCost, then the returned cost won't necessarily be zero: only some of the components of the cost that depend on the cost class will be omited. See the code for details. """ return _pywrapcp.RoutingModel_GetArcCostForClass(self, from_index, to_index, cost_class_index) def GetArcCostForFirstSolution(self, from_index: int64_t, to_index: int64_t) ‑> int64_t-
Returns the cost of the arc in the context of the first solution strategy. This is typically a simplification of the actual cost; see the .cc.
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def GetArcCostForFirstSolution(self, from_index: "int64_t", to_index: "int64_t") -> "int64_t": r""" Returns the cost of the arc in the context of the first solution strategy. This is typically a simplification of the actual cost; see the .cc. """ return _pywrapcp.RoutingModel_GetArcCostForFirstSolution(self, from_index, to_index) def GetArcCostForVehicle(self, from_index: int64_t, to_index: int64_t, vehicle: int64_t) ‑> int64_t-
Returns the cost of the transit arc between two nodes for a given vehicle. Input are variable indices of node. This returns 0 if vehicle < 0.
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def GetArcCostForVehicle(self, from_index: "int64_t", to_index: "int64_t", vehicle: "int64_t") -> "int64_t": r""" Returns the cost of the transit arc between two nodes for a given vehicle. Input are variable indices of node. This returns 0 if vehicle < 0. """ return _pywrapcp.RoutingModel_GetArcCostForVehicle(self, from_index, to_index, vehicle) def GetAutomaticFirstSolutionStrategy(self) ‑> operations_research::FirstSolutionStrategy::Value-
Returns the automatic first solution strategy selected.
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def GetAutomaticFirstSolutionStrategy(self) -> "operations_research::FirstSolutionStrategy::Value": r""" Returns the automatic first solution strategy selected.""" return _pywrapcp.RoutingModel_GetAutomaticFirstSolutionStrategy(self) def GetCostClassIndexOfVehicle(self, vehicle: int64_t) ‑> operations_research::RoutingModel::CostClassIndex-
Get the cost class index of the given vehicle.
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def GetCostClassIndexOfVehicle(self, vehicle: "int64_t") -> "operations_research::RoutingModel::CostClassIndex": r""" Get the cost class index of the given vehicle.""" return _pywrapcp.RoutingModel_GetCostClassIndexOfVehicle(self, vehicle) def GetCostClassesCount(self) ‑> int-
Returns the number of different cost classes in the model.
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def GetCostClassesCount(self) -> "int": r""" Returns the number of different cost classes in the model.""" return _pywrapcp.RoutingModel_GetCostClassesCount(self) def GetDeliveryIndexPairs(self, node_index: int64_t) ‑> std::vector< std::pair< int,int > > const &-
Same as above for deliveries.
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def GetDeliveryIndexPairs(self, node_index: "int64_t") -> "std::vector< std::pair< int,int > > const &": r""" Same as above for deliveries.""" return _pywrapcp.RoutingModel_GetDeliveryIndexPairs(self, node_index) def GetDepot(self) ‑> int64_t-
Returns the variable index of the first starting or ending node of all routes. If all routes start and end at the same node (single depot), this is the node returned.
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def GetDepot(self) -> "int64_t": r""" Returns the variable index of the first starting or ending node of all routes. If all routes start and end at the same node (single depot), this is the node returned. """ return _pywrapcp.RoutingModel_GetDepot(self) def GetDimensionOrDie(self, dimension_name: std::string const &) ‑> operations_research::RoutingDimension const &-
Returns a dimension from its name. Dies if the dimension does not exist.
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def GetDimensionOrDie(self, dimension_name: "std::string const &") -> "operations_research::RoutingDimension const &": r""" Returns a dimension from its name. Dies if the dimension does not exist.""" return _pywrapcp.RoutingModel_GetDimensionOrDie(self, dimension_name) def GetDimensions(self) ‑> std::vector< operations_research::RoutingDimension * > const &-
Returns all dimensions of the model.
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def GetDimensions(self) -> "std::vector< operations_research::RoutingDimension * > const &": r""" Returns all dimensions of the model.""" return _pywrapcp.RoutingModel_GetDimensions(self) def GetDimensionsWithSoftOrSpanCosts(self) ‑> std::vector< operations_research::RoutingDimension * >-
Returns dimensions with soft or vehicle span costs.
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def GetDimensionsWithSoftOrSpanCosts(self) -> "std::vector< operations_research::RoutingDimension * >": r""" Returns dimensions with soft or vehicle span costs.""" return _pywrapcp.RoutingModel_GetDimensionsWithSoftOrSpanCosts(self) def GetDisjunctionIndices(self, index: int64_t) ‑> std::vector< operations_research::RoutingModel::DisjunctionIndex > const &-
Returns the indices of the disjunctions to which an index belongs.
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def GetDisjunctionIndices(self, index: "int64_t") -> "std::vector< operations_research::RoutingModel::DisjunctionIndex > const &": r""" Returns the indices of the disjunctions to which an index belongs.""" return _pywrapcp.RoutingModel_GetDisjunctionIndices(self, index) def GetDisjunctionMaxCardinality(self, index: operations_research::RoutingModel::DisjunctionIndex) ‑> int64_t-
Returns the maximum number of possible active nodes of the node disjunction of index 'index'.
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def GetDisjunctionMaxCardinality(self, index: "operations_research::RoutingModel::DisjunctionIndex") -> "int64_t": r""" Returns the maximum number of possible active nodes of the node disjunction of index 'index'. """ return _pywrapcp.RoutingModel_GetDisjunctionMaxCardinality(self, index) def GetDisjunctionPenalty(self, index: operations_research::RoutingModel::DisjunctionIndex) ‑> int64_t-
Returns the penalty of the node disjunction of index 'index'.
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def GetDisjunctionPenalty(self, index: "operations_research::RoutingModel::DisjunctionIndex") -> "int64_t": r""" Returns the penalty of the node disjunction of index 'index'.""" return _pywrapcp.RoutingModel_GetDisjunctionPenalty(self, index) def GetFixedCostOfVehicle(self, vehicle: int) ‑> int64_t-
Returns the route fixed cost taken into account if the route of the vehicle is not empty, aka there's at least one node on the route other than the first and last nodes.
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def GetFixedCostOfVehicle(self, vehicle: "int") -> "int64_t": r""" Returns the route fixed cost taken into account if the route of the vehicle is not empty, aka there's at least one node on the route other than the first and last nodes. """ return _pywrapcp.RoutingModel_GetFixedCostOfVehicle(self, vehicle) def GetGlobalDimensionCumulOptimizers(self) ‑> std::vector< std::unique_ptr< operations_research::GlobalDimensionCumulOptimizer > > const &-
Returns [global|local]dimension_optimizers, which are empty if the model has not been closed.
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def GetGlobalDimensionCumulOptimizers(self) -> "std::vector< std::unique_ptr< operations_research::GlobalDimensionCumulOptimizer > > const &": r""" Returns [global|local]_dimension_optimizers_, which are empty if the model has not been closed. """ return _pywrapcp.RoutingModel_GetGlobalDimensionCumulOptimizers(self) def GetHardTypeIncompatibilitiesOfType(self, type: int) ‑> absl::flat_hash_set< int > const &-
Returns visit types incompatible with a given type.
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def GetHardTypeIncompatibilitiesOfType(self, type: "int") -> "absl::flat_hash_set< int > const &": r""" Returns visit types incompatible with a given type.""" return _pywrapcp.RoutingModel_GetHardTypeIncompatibilitiesOfType(self, type) def GetHomogeneousCost(self, from_index: int64_t, to_index: int64_t) ‑> int64_t-
Returns the cost of the segment between two nodes supposing all vehicle costs are the same (returns the cost for the first vehicle otherwise).
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def GetHomogeneousCost(self, from_index: "int64_t", to_index: "int64_t") -> "int64_t": r""" Returns the cost of the segment between two nodes supposing all vehicle costs are the same (returns the cost for the first vehicle otherwise). """ return _pywrapcp.RoutingModel_GetHomogeneousCost(self, from_index, to_index) def GetLocalDimensionCumulMPOptimizers(self) ‑> std::vector< std::unique_ptr< operations_research::LocalDimensionCumulOptimizer > > const &-
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def GetLocalDimensionCumulMPOptimizers(self) -> "std::vector< std::unique_ptr< operations_research::LocalDimensionCumulOptimizer > > const &": return _pywrapcp.RoutingModel_GetLocalDimensionCumulMPOptimizers(self) def GetLocalDimensionCumulOptimizers(self) ‑> std::vector< std::unique_ptr< operations_research::LocalDimensionCumulOptimizer > > const &-
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def GetLocalDimensionCumulOptimizers(self) -> "std::vector< std::unique_ptr< operations_research::LocalDimensionCumulOptimizer > > const &": return _pywrapcp.RoutingModel_GetLocalDimensionCumulOptimizers(self) def GetMaximumNumberOfActiveVehicles(self) ‑> int-
Returns the maximum number of active vehicles.
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def GetMaximumNumberOfActiveVehicles(self) -> "int": r""" Returns the maximum number of active vehicles.""" return _pywrapcp.RoutingModel_GetMaximumNumberOfActiveVehicles(self) def GetMutableDimension(self, dimension_name: std::string const &) ‑> operations_research::RoutingDimension *-
Returns a dimension from its name. Returns nullptr if the dimension does not exist.
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def GetMutableDimension(self, dimension_name: "std::string const &") -> "operations_research::RoutingDimension *": r""" Returns a dimension from its name. Returns nullptr if the dimension does not exist. """ return _pywrapcp.RoutingModel_GetMutableDimension(self, dimension_name) def GetMutableGlobalCumulOptimizer(self, dimension: RoutingDimension) ‑> operations_research::GlobalDimensionCumulOptimizer *-
Returns the global/local dimension cumul optimizer for a given dimension, or nullptr if there is none.
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def GetMutableGlobalCumulOptimizer(self, dimension: "RoutingDimension") -> "operations_research::GlobalDimensionCumulOptimizer *": r""" Returns the global/local dimension cumul optimizer for a given dimension, or nullptr if there is none. """ return _pywrapcp.RoutingModel_GetMutableGlobalCumulOptimizer(self, dimension) def GetMutableLocalCumulMPOptimizer(self, dimension: RoutingDimension) ‑> operations_research::LocalDimensionCumulOptimizer *-
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def GetMutableLocalCumulMPOptimizer(self, dimension: "RoutingDimension") -> "operations_research::LocalDimensionCumulOptimizer *": return _pywrapcp.RoutingModel_GetMutableLocalCumulMPOptimizer(self, dimension) def GetMutableLocalCumulOptimizer(self, dimension: RoutingDimension) ‑> operations_research::LocalDimensionCumulOptimizer *-
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def GetMutableLocalCumulOptimizer(self, dimension: "RoutingDimension") -> "operations_research::LocalDimensionCumulOptimizer *": return _pywrapcp.RoutingModel_GetMutableLocalCumulOptimizer(self, dimension) def GetNonZeroCostClassesCount(self) ‑> int-
Ditto, minus the 'always zero', built-in cost class.
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def GetNonZeroCostClassesCount(self) -> "int": r""" Ditto, minus the 'always zero', built-in cost class.""" return _pywrapcp.RoutingModel_GetNonZeroCostClassesCount(self) def GetNumOfSingletonNodes(self) ‑> int-
Returns the number of non-start/end nodes which do not appear in a pickup/delivery pair.
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def GetNumOfSingletonNodes(self) -> "int": r""" Returns the number of non-start/end nodes which do not appear in a pickup/delivery pair. """ return _pywrapcp.RoutingModel_GetNumOfSingletonNodes(self) def GetNumberOfDecisionsInFirstSolution(self, search_parameters: operations_research::RoutingSearchParameters const &) ‑> int64_t-
Returns statistics on first solution search, number of decisions sent to filters, number of decisions rejected by filters.
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def GetNumberOfDecisionsInFirstSolution(self, search_parameters: "operations_research::RoutingSearchParameters const &") -> "int64_t": r""" Returns statistics on first solution search, number of decisions sent to filters, number of decisions rejected by filters. """ return _pywrapcp.RoutingModel_GetNumberOfDecisionsInFirstSolution(self, search_parameters) def GetNumberOfDisjunctions(self) ‑> int-
Returns the number of node disjunctions in the model.
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def GetNumberOfDisjunctions(self) -> "int": r""" Returns the number of node disjunctions in the model.""" return _pywrapcp.RoutingModel_GetNumberOfDisjunctions(self) def GetNumberOfRejectsInFirstSolution(self, search_parameters: operations_research::RoutingSearchParameters const &) ‑> int64_t-
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def GetNumberOfRejectsInFirstSolution(self, search_parameters: "operations_research::RoutingSearchParameters const &") -> "int64_t": return _pywrapcp.RoutingModel_GetNumberOfRejectsInFirstSolution(self, search_parameters) def GetNumberOfVisitTypes(self) ‑> int-
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def GetNumberOfVisitTypes(self) -> "int": return _pywrapcp.RoutingModel_GetNumberOfVisitTypes(self) def GetPairIndicesOfType(self, type: int) ‑> std::vector< int > const &-
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def GetPairIndicesOfType(self, type: "int") -> "std::vector< int > const &": return _pywrapcp.RoutingModel_GetPairIndicesOfType(self, type) def GetPerfectBinaryDisjunctions(self) ‑> std::vector< std::pair< int64_t,int64_t > >-
Returns the list of all perfect binary disjunctions, as pairs of variable indices: a disjunction is "perfect" when its variables do not appear in any other disjunction. Each pair is sorted (lowest variable index first), and the output vector is also sorted (lowest pairs first).
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def GetPerfectBinaryDisjunctions(self) -> "std::vector< std::pair< int64_t,int64_t > >": r""" Returns the list of all perfect binary disjunctions, as pairs of variable indices: a disjunction is "perfect" when its variables do not appear in any other disjunction. Each pair is sorted (lowest variable index first), and the output vector is also sorted (lowest pairs first). """ return _pywrapcp.RoutingModel_GetPerfectBinaryDisjunctions(self) def GetPickupAndDeliveryPolicyOfVehicle(self, vehicle: int) ‑> operations_research::RoutingModel::PickupAndDeliveryPolicy-
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def GetPickupAndDeliveryPolicyOfVehicle(self, vehicle: "int") -> "operations_research::RoutingModel::PickupAndDeliveryPolicy": return _pywrapcp.RoutingModel_GetPickupAndDeliveryPolicyOfVehicle(self, vehicle) def GetPickupIndexPairs(self, node_index: int64_t) ‑> std::vector< std::pair< int,int > > const &-
Returns pairs for which the node is a pickup; the first element of each pair is the index in the pickup and delivery pairs list in which the pickup appears, the second element is its index in the pickups list.
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def GetPickupIndexPairs(self, node_index: "int64_t") -> "std::vector< std::pair< int,int > > const &": r""" Returns pairs for which the node is a pickup; the first element of each pair is the index in the pickup and delivery pairs list in which the pickup appears, the second element is its index in the pickups list. """ return _pywrapcp.RoutingModel_GetPickupIndexPairs(self, node_index) def GetPrimaryConstrainedDimension(self) ‑> std::string const &-
Get the primary constrained dimension, or an empty string if it is unset.
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def GetPrimaryConstrainedDimension(self) -> "std::string const &": r""" Get the primary constrained dimension, or an empty string if it is unset.""" return _pywrapcp.RoutingModel_GetPrimaryConstrainedDimension(self) def GetRequiredTypeAlternativesWhenAddingType(self, type: int) ‑> std::vector< absl::flat_hash_set< int > > const &-
Returns the set of requirement alternatives when adding the given type.
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def GetRequiredTypeAlternativesWhenAddingType(self, type: "int") -> "std::vector< absl::flat_hash_set< int > > const &": r""" Returns the set of requirement alternatives when adding the given type.""" return _pywrapcp.RoutingModel_GetRequiredTypeAlternativesWhenAddingType(self, type) def GetRequiredTypeAlternativesWhenRemovingType(self, type: int) ‑> std::vector< absl::flat_hash_set< int > > const &-
Returns the set of requirement alternatives when removing the given type.
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def GetRequiredTypeAlternativesWhenRemovingType(self, type: "int") -> "std::vector< absl::flat_hash_set< int > > const &": r""" Returns the set of requirement alternatives when removing the given type.""" return _pywrapcp.RoutingModel_GetRequiredTypeAlternativesWhenRemovingType(self, type) def GetSameVehicleIndicesOfIndex(self, node: int) ‑> std::vector< int > const &-
Returns variable indices of nodes constrained to be on the same route.
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def GetSameVehicleIndicesOfIndex(self, node: "int") -> "std::vector< int > const &": r""" Returns variable indices of nodes constrained to be on the same route.""" return _pywrapcp.RoutingModel_GetSameVehicleIndicesOfIndex(self, node) def GetSameVehicleRequiredTypeAlternativesOfType(self, type: int) ‑> std::vector< absl::flat_hash_set< int > > const &-
Returns the set of same-vehicle requirement alternatives for the given type.
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def GetSameVehicleRequiredTypeAlternativesOfType(self, type: "int") -> "std::vector< absl::flat_hash_set< int > > const &": r""" Returns the set of same-vehicle requirement alternatives for the given type. """ return _pywrapcp.RoutingModel_GetSameVehicleRequiredTypeAlternativesOfType(self, type) def GetSingleNodesOfType(self, type: int) ‑> std::vector< int > const &-
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def GetSingleNodesOfType(self, type: "int") -> "std::vector< int > const &": return _pywrapcp.RoutingModel_GetSingleNodesOfType(self, type) def GetTemporalTypeIncompatibilitiesOfType(self, type: int) ‑> absl::flat_hash_set< int > const &-
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def GetTemporalTypeIncompatibilitiesOfType(self, type: "int") -> "absl::flat_hash_set< int > const &": return _pywrapcp.RoutingModel_GetTemporalTypeIncompatibilitiesOfType(self, type) def GetVehicleClassIndexOfVehicle(self, vehicle: int64_t) ‑> operations_research::RoutingModel::VehicleClassIndex-
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def GetVehicleClassIndexOfVehicle(self, vehicle: "int64_t") -> "operations_research::RoutingModel::VehicleClassIndex": return _pywrapcp.RoutingModel_GetVehicleClassIndexOfVehicle(self, vehicle) def GetVehicleClassesCount(self) ‑> int-
Returns the number of different vehicle classes in the model.
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def GetVehicleClassesCount(self) -> "int": r""" Returns the number of different vehicle classes in the model.""" return _pywrapcp.RoutingModel_GetVehicleClassesCount(self) def GetVehicleTypeContainer(self) ‑> operations_research::RoutingModel::VehicleTypeContainer const &-
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def GetVehicleTypeContainer(self) -> "operations_research::RoutingModel::VehicleTypeContainer const &": return _pywrapcp.RoutingModel_GetVehicleTypeContainer(self) def GetVisitType(self, index: int64_t) ‑> int-
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def GetVisitType(self, index: "int64_t") -> "int": return _pywrapcp.RoutingModel_GetVisitType(self, index) def GetVisitTypePolicy(self, index: int64_t) ‑> operations_research::RoutingModel::VisitTypePolicy-
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def GetVisitTypePolicy(self, index: "int64_t") -> "operations_research::RoutingModel::VisitTypePolicy": return _pywrapcp.RoutingModel_GetVisitTypePolicy(self, index) def HasDimension(self, dimension_name: std::string const &) ‑> bool-
Returns true if a dimension exists for a given dimension name.
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def HasDimension(self, dimension_name: "std::string const &") -> "bool": r""" Returns true if a dimension exists for a given dimension name.""" return _pywrapcp.RoutingModel_HasDimension(self, dimension_name) def HasHardTypeIncompatibilities(self) ‑> bool-
Returns true iff any hard (resp. temporal) type incompatibilities have been added to the model.
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def HasHardTypeIncompatibilities(self) -> "bool": r""" Returns true iff any hard (resp. temporal) type incompatibilities have been added to the model. """ return _pywrapcp.RoutingModel_HasHardTypeIncompatibilities(self) def HasSameVehicleTypeRequirements(self) ‑> bool-
Returns true iff any same-route (resp. temporal) type requirements have been added to the model.
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def HasSameVehicleTypeRequirements(self) -> "bool": r""" Returns true iff any same-route (resp. temporal) type requirements have been added to the model. """ return _pywrapcp.RoutingModel_HasSameVehicleTypeRequirements(self) def HasTemporalTypeIncompatibilities(self) ‑> bool-
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def HasTemporalTypeIncompatibilities(self) -> "bool": return _pywrapcp.RoutingModel_HasTemporalTypeIncompatibilities(self) def HasTemporalTypeRequirements(self) ‑> bool-
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def HasTemporalTypeRequirements(self) -> "bool": return _pywrapcp.RoutingModel_HasTemporalTypeRequirements(self) def HasTypeRegulations(self) ‑> bool-
Returns true iff the model has any incompatibilities or requirements set on node types.
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def HasTypeRegulations(self) -> "bool": r""" Returns true iff the model has any incompatibilities or requirements set on node types. """ return _pywrapcp.RoutingModel_HasTypeRegulations(self) def HasVehicleWithCostClassIndex(self, cost_class_index: operations_research::RoutingModel::CostClassIndex) ‑> bool-
Returns true iff the model contains a vehicle with the given cost_class_index.
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def HasVehicleWithCostClassIndex(self, cost_class_index: "operations_research::RoutingModel::CostClassIndex") -> "bool": r""" Returns true iff the model contains a vehicle with the given cost_class_index. """ return _pywrapcp.RoutingModel_HasVehicleWithCostClassIndex(self, cost_class_index) def IgnoreDisjunctionsAlreadyForcedToZero(self) ‑> void-
SPECIAL: Makes the solver ignore all the disjunctions whose active variables are all trivially zero (i.e. Max() == 0), by setting their max_cardinality to 0. This can be useful when using the BaseBinaryDisjunctionNeighborhood operators, in the context of arc-based routing.
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def IgnoreDisjunctionsAlreadyForcedToZero(self) -> "void": r""" SPECIAL: Makes the solver ignore all the disjunctions whose active variables are all trivially zero (i.e. Max() == 0), by setting their max_cardinality to 0. This can be useful when using the BaseBinaryDisjunctionNeighborhood operators, in the context of arc-based routing. """ return _pywrapcp.RoutingModel_IgnoreDisjunctionsAlreadyForcedToZero(self) def IsEnd(self, index: int64_t) ‑> bool-
Returns true if 'index' represents the last node of a route.
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def IsEnd(self, index: "int64_t") -> "bool": r""" Returns true if 'index' represents the last node of a route.""" return _pywrapcp.RoutingModel_IsEnd(self, index) def IsMatchingModel(self) ‑> bool-
Returns true if a vehicle/node matching problem is detected.
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def IsMatchingModel(self) -> "bool": r""" Returns true if a vehicle/node matching problem is detected.""" return _pywrapcp.RoutingModel_IsMatchingModel(self) def IsStart(self, index: int64_t) ‑> bool-
Returns true if 'index' represents the first node of a route.
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def IsStart(self, index: "int64_t") -> "bool": r""" Returns true if 'index' represents the first node of a route.""" return _pywrapcp.RoutingModel_IsStart(self, index) def IsVehicleAllowedForIndex(self, vehicle: int, index: int64_t) ‑> bool-
Returns true if a vehicle is allowed to visit a given node.
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def IsVehicleAllowedForIndex(self, vehicle: "int", index: "int64_t") -> "bool": r""" Returns true if a vehicle is allowed to visit a given node.""" return _pywrapcp.RoutingModel_IsVehicleAllowedForIndex(self, vehicle, index) def IsVehicleUsed(self, assignment: Assignment, vehicle: int) ‑> bool-
Returns true if the route of 'vehicle' is non empty in 'assignment'.
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def IsVehicleUsed(self, assignment: "Assignment", vehicle: "int") -> "bool": r""" Returns true if the route of 'vehicle' is non empty in 'assignment'.""" return _pywrapcp.RoutingModel_IsVehicleUsed(self, assignment, vehicle) def MakeGuidedSlackFinalizer(self, dimension: RoutingDimension, initializer: std::function< int64_t (int64_t) >) ‑> operations_research::DecisionBuilder *-
The next few members are in the public section only for testing purposes.
MakeGuidedSlackFinalizer creates a DecisionBuilder for the slacks of a dimension using a callback to choose which values to start with. The finalizer works only when all next variables in the model have been fixed. It has the following two characteristics: 1. It follows the routes defined by the nexts variables when choosing a variable to make a decision on. 2. When it comes to choose a value for the slack of node i, the decision builder first calls the callback with argument i, and supposingly the returned value is x it creates decisions slack[i] = x, slack[i] = x + 1, slack[i] = x - 1, slack[i] = x + 2, etc.
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def MakeGuidedSlackFinalizer(self, dimension: "RoutingDimension", initializer: "std::function< int64_t (int64_t) >") -> "operations_research::DecisionBuilder *": r""" The next few members are in the public section only for testing purposes. MakeGuidedSlackFinalizer creates a DecisionBuilder for the slacks of a dimension using a callback to choose which values to start with. The finalizer works only when all next variables in the model have been fixed. It has the following two characteristics: 1. It follows the routes defined by the nexts variables when choosing a variable to make a decision on. 2. When it comes to choose a value for the slack of node i, the decision builder first calls the callback with argument i, and supposingly the returned value is x it creates decisions slack[i] = x, slack[i] = x + 1, slack[i] = x - 1, slack[i] = x + 2, etc. """ return _pywrapcp.RoutingModel_MakeGuidedSlackFinalizer(self, dimension, initializer) def MakePathSpansAndTotalSlacks(self, dimension: RoutingDimension, spans: std::vector< operations_research::IntVar * >, total_slacks: std::vector< operations_research::IntVar * >) ‑> operations_research::Constraint *-
For every vehicle of the routing model: - if total_slacks[vehicle] is not nullptr, constrains it to be the sum of slacks on that vehicle, that is, dimension->CumulVar(end) - dimension->CumulVar(start) - sum_{node in path of vehicle} dimension->FixedTransitVar(node). - if spans[vehicle] is not nullptr, constrains it to be dimension->CumulVar(end) - dimension->CumulVar(start) This does stronger propagation than a decomposition, and takes breaks into account.
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def MakePathSpansAndTotalSlacks(self, dimension: "RoutingDimension", spans: "std::vector< operations_research::IntVar * >", total_slacks: "std::vector< operations_research::IntVar * >") -> "operations_research::Constraint *": r""" For every vehicle of the routing model: - if total_slacks[vehicle] is not nullptr, constrains it to be the sum of slacks on that vehicle, that is, dimension->CumulVar(end) - dimension->CumulVar(start) - sum_{node in path of vehicle} dimension->FixedTransitVar(node). - if spans[vehicle] is not nullptr, constrains it to be dimension->CumulVar(end) - dimension->CumulVar(start) This does stronger propagation than a decomposition, and takes breaks into account. """ return _pywrapcp.RoutingModel_MakePathSpansAndTotalSlacks(self, dimension, spans, total_slacks) def MakeSelfDependentDimensionFinalizer(self, dimension: RoutingDimension) ‑> operations_research::DecisionBuilder *-
MakeSelfDependentDimensionFinalizer is a finalizer for the slacks of a self-dependent dimension. It makes an extensive use of the caches of the state dependent transits. In detail, MakeSelfDependentDimensionFinalizer returns a composition of a local search decision builder with a greedy descent operator for the cumul of the start of each route and a guided slack finalizer. Provided there are no time windows and the maximum slacks are large enough, once the cumul of the start of route is fixed, the guided finalizer can find optimal values of the slacks for the rest of the route in time proportional to the length of the route. Therefore the composed finalizer generally works in time O(log(t)nm), where t is the latest possible departute time, n is the number of nodes in the network and m is the number of vehicles.
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def MakeSelfDependentDimensionFinalizer(self, dimension: "RoutingDimension") -> "operations_research::DecisionBuilder *": r""" MakeSelfDependentDimensionFinalizer is a finalizer for the slacks of a self-dependent dimension. It makes an extensive use of the caches of the state dependent transits. In detail, MakeSelfDependentDimensionFinalizer returns a composition of a local search decision builder with a greedy descent operator for the cumul of the start of each route and a guided slack finalizer. Provided there are no time windows and the maximum slacks are large enough, once the cumul of the start of route is fixed, the guided finalizer can find optimal values of the slacks for the rest of the route in time proportional to the length of the route. Therefore the composed finalizer generally works in time O(log(t)*n*m), where t is the latest possible departute time, n is the number of nodes in the network and m is the number of vehicles. """ return _pywrapcp.RoutingModel_MakeSelfDependentDimensionFinalizer(self, dimension) def MutablePreAssignment(self) ‑> operations_research::Assignment *-
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def MutablePreAssignment(self) -> "operations_research::Assignment *": return _pywrapcp.RoutingModel_MutablePreAssignment(self) def Next(self, assignment: Assignment, index: int64_t) ‑> int64_t-
Assignment inspection Returns the variable index of the node directly after the node corresponding to 'index' in 'assignment'.
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def Next(self, assignment: "Assignment", index: "int64_t") -> "int64_t": r""" Assignment inspection Returns the variable index of the node directly after the node corresponding to 'index' in 'assignment'. """ return _pywrapcp.RoutingModel_Next(self, assignment, index) def NextVar(self, index: int64_t) ‑> operations_research::IntVar *-
Returns the next variable of the node corresponding to index. Note that NextVar(index) == index is equivalent to ActiveVar(index) == 0.
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def NextVar(self, index: "int64_t") -> "operations_research::IntVar *": r""" Returns the next variable of the node corresponding to index. Note that NextVar(index) == index is equivalent to ActiveVar(index) == 0. """ return _pywrapcp.RoutingModel_NextVar(self, index) def PackCumulsOfOptimizerDimensionsFromAssignment(self, original_assignment: Assignment, duration_limit: absl::Duration) ‑> operations_research::Assignment const *-
For every dimension in the model with an optimizer in local/global_dimension_optimizers_, this method tries to pack the cumul values of the dimension, such that: - The cumul costs (span costs, soft lower and upper bound costs, etc) are minimized. - The cumuls of the ends of the routes are minimized for this given minimal cumul cost. - Given these minimal end cumuls, the route start cumuls are maximized. Returns the assignment resulting from allocating these packed cumuls with the solver, and nullptr if these cumuls could not be set by the solver.
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def PackCumulsOfOptimizerDimensionsFromAssignment(self, original_assignment: "Assignment", duration_limit: "absl::Duration") -> "operations_research::Assignment const *": r""" For every dimension in the model with an optimizer in local/global_dimension_optimizers_, this method tries to pack the cumul values of the dimension, such that: - The cumul costs (span costs, soft lower and upper bound costs, etc) are minimized. - The cumuls of the ends of the routes are minimized for this given minimal cumul cost. - Given these minimal end cumuls, the route start cumuls are maximized. Returns the assignment resulting from allocating these packed cumuls with the solver, and nullptr if these cumuls could not be set by the solver. """ return _pywrapcp.RoutingModel_PackCumulsOfOptimizerDimensionsFromAssignment(self, original_assignment, duration_limit) def PreAssignment(self) ‑> operations_research::Assignment const *const-
Returns an assignment used to fix some of the variables of the problem. In practice, this assignment locks partial routes of the problem. This can be used in the context of locking the parts of the routes which have already been driven in online routing problems.
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def PreAssignment(self) -> "operations_research::Assignment const *const": r""" Returns an assignment used to fix some of the variables of the problem. In practice, this assignment locks partial routes of the problem. This can be used in the context of locking the parts of the routes which have already been driven in online routing problems. """ return _pywrapcp.RoutingModel_PreAssignment(self) def ReadAssignment(self, file_name: std::string const &) ‑> operations_research::Assignment *-
Reads an assignment from a file and returns the current solution. Returns nullptr if the file cannot be opened or if the assignment is not valid.
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def ReadAssignment(self, file_name: "std::string const &") -> "operations_research::Assignment *": r""" Reads an assignment from a file and returns the current solution. Returns nullptr if the file cannot be opened or if the assignment is not valid. """ return _pywrapcp.RoutingModel_ReadAssignment(self, file_name) def ReadAssignmentFromRoutes(self, routes: std::vector< std::vector< int64_t > > const &, ignore_inactive_indices: bool) ‑> operations_research::Assignment *-
Restores the routes as the current solution. Returns nullptr if the solution cannot be restored (routes do not contain a valid solution). Note that calling this method will run the solver to assign values to the dimension variables; this may take considerable amount of time, especially when using dimensions with slack.
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def ReadAssignmentFromRoutes(self, routes: "std::vector< std::vector< int64_t > > const &", ignore_inactive_indices: "bool") -> "operations_research::Assignment *": r""" Restores the routes as the current solution. Returns nullptr if the solution cannot be restored (routes do not contain a valid solution). Note that calling this method will run the solver to assign values to the dimension variables; this may take considerable amount of time, especially when using dimensions with slack. """ return _pywrapcp.RoutingModel_ReadAssignmentFromRoutes(self, routes, ignore_inactive_indices) def RegisterPositiveTransitCallback(self, callback: operations_research::RoutingModel::TransitCallback2) ‑> int-
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def RegisterPositiveTransitCallback(self, callback: "operations_research::RoutingModel::TransitCallback2") -> "int": return _pywrapcp.RoutingModel_RegisterPositiveTransitCallback(self, callback) def RegisterPositiveUnaryTransitCallback(self, callback: operations_research::RoutingModel::TransitCallback1) ‑> int-
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def RegisterPositiveUnaryTransitCallback(self, callback: "operations_research::RoutingModel::TransitCallback1") -> "int": return _pywrapcp.RoutingModel_RegisterPositiveUnaryTransitCallback(self, callback) def RegisterTransitCallback(self, callback: operations_research::RoutingModel::TransitCallback2) ‑> int-
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def RegisterTransitCallback(self, callback: "operations_research::RoutingModel::TransitCallback2") -> "int": return _pywrapcp.RoutingModel_RegisterTransitCallback(self, callback) def RegisterTransitMatrix(self, values: std::vector< std::vector< int64_t > >) ‑> int-
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def RegisterTransitMatrix(self, values: "std::vector< std::vector< int64_t > >") -> "int": return _pywrapcp.RoutingModel_RegisterTransitMatrix(self, values) def RegisterUnaryTransitCallback(self, callback: operations_research::RoutingModel::TransitCallback1) ‑> int-
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def RegisterUnaryTransitCallback(self, callback: "operations_research::RoutingModel::TransitCallback1") -> "int": return _pywrapcp.RoutingModel_RegisterUnaryTransitCallback(self, callback) def RegisterUnaryTransitVector(self, values: std::vector< int64_t >) ‑> int-
Registers 'callback' and returns its index.
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def RegisterUnaryTransitVector(self, values: "std::vector< int64_t >") -> "int": r""" Registers 'callback' and returns its index.""" return _pywrapcp.RoutingModel_RegisterUnaryTransitVector(self, values) def RemainingTime(self) ‑> absl::Duration-
Returns the time left in the search limit.
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def RemainingTime(self) -> "absl::Duration": r""" Returns the time left in the search limit.""" return _pywrapcp.RoutingModel_RemainingTime(self) def RestoreAssignment(self, solution: Assignment) ‑> operations_research::Assignment *-
Restores an assignment as a solution in the routing model and returns the new solution. Returns nullptr if the assignment is not valid.
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def RestoreAssignment(self, solution: "Assignment") -> "operations_research::Assignment *": r""" Restores an assignment as a solution in the routing model and returns the new solution. Returns nullptr if the assignment is not valid. """ return _pywrapcp.RoutingModel_RestoreAssignment(self, solution) def RoutesToAssignment(self, routes: std::vector< std::vector< int64_t > > const &, ignore_inactive_indices: bool, close_routes: bool, assignment: Assignment) ‑> bool-
Fills an assignment from a specification of the routes of the vehicles. The routes are specified as lists of variable indices that appear on the routes of the vehicles. The indices of the outer vector in 'routes' correspond to vehicles IDs, the inner vector contains the variable indices on the routes for the given vehicle. The inner vectors must not contain the start and end indices, as these are determined by the routing model. Sets the value of NextVars in the assignment, adding the variables to the assignment if necessary. The method does not touch other variables in the assignment. The method can only be called after the model is closed. With ignore_inactive_indices set to false, this method will fail (return nullptr) in case some of the route contain indices that are deactivated in the model; when set to true, these indices will be skipped. Returns true if routes were successfully loaded. However, such assignment still might not be a valid solution to the routing problem due to more complex constraints; it is advisible to call solver()->CheckSolution() afterwards.
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def RoutesToAssignment(self, routes: "std::vector< std::vector< int64_t > > const &", ignore_inactive_indices: "bool", close_routes: "bool", assignment: "Assignment") -> "bool": r""" Fills an assignment from a specification of the routes of the vehicles. The routes are specified as lists of variable indices that appear on the routes of the vehicles. The indices of the outer vector in 'routes' correspond to vehicles IDs, the inner vector contains the variable indices on the routes for the given vehicle. The inner vectors must not contain the start and end indices, as these are determined by the routing model. Sets the value of NextVars in the assignment, adding the variables to the assignment if necessary. The method does not touch other variables in the assignment. The method can only be called after the model is closed. With ignore_inactive_indices set to false, this method will fail (return nullptr) in case some of the route contain indices that are deactivated in the model; when set to true, these indices will be skipped. Returns true if routes were successfully loaded. However, such assignment still might not be a valid solution to the routing problem due to more complex constraints; it is advisible to call solver()->CheckSolution() afterwards. """ return _pywrapcp.RoutingModel_RoutesToAssignment(self, routes, ignore_inactive_indices, close_routes, assignment) def SetAllowedVehiclesForIndex(self, vehicles: std::vector< int > const &, index: int64_t) ‑> void-
Sets the vehicles which can visit a given node. If the node is in a disjunction, this will not prevent it from being unperformed. Specifying an empty vector of vehicles has no effect (all vehicles will be allowed to visit the node).
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def SetAllowedVehiclesForIndex(self, vehicles: "std::vector< int > const &", index: "int64_t") -> "void": r""" Sets the vehicles which can visit a given node. If the node is in a disjunction, this will not prevent it from being unperformed. Specifying an empty vector of vehicles has no effect (all vehicles will be allowed to visit the node). """ return _pywrapcp.RoutingModel_SetAllowedVehiclesForIndex(self, vehicles, index) def SetAmortizedCostFactorsOfAllVehicles(self, linear_cost_factor: int64_t, quadratic_cost_factor: int64_t) ‑> void-
The following methods set the linear and quadratic cost factors of vehicles (must be positive values). The default value of these parameters is zero for all vehicles.
When set, the cost_ of the model will contain terms aiming at reducing the number of vehicles used in the model, by adding the following to the objective for every vehicle v: INDICATOR(v used in the model) * [linear_cost_factor_of_vehicle_[v] - quadratic_cost_factor_of_vehicle_[v]*(square of length of route v)] i.e. for every used vehicle, we add the linear factor as fixed cost, and subtract the square of the route length multiplied by the quadratic factor. This second term aims at making the routes as dense as possible.
Sets the linear and quadratic cost factor of all vehicles.
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def SetAmortizedCostFactorsOfAllVehicles(self, linear_cost_factor: "int64_t", quadratic_cost_factor: "int64_t") -> "void": r""" The following methods set the linear and quadratic cost factors of vehicles (must be positive values). The default value of these parameters is zero for all vehicles. When set, the cost_ of the model will contain terms aiming at reducing the number of vehicles used in the model, by adding the following to the objective for every vehicle v: INDICATOR(v used in the model) * [linear_cost_factor_of_vehicle_[v] - quadratic_cost_factor_of_vehicle_[v]*(square of length of route v)] i.e. for every used vehicle, we add the linear factor as fixed cost, and subtract the square of the route length multiplied by the quadratic factor. This second term aims at making the routes as dense as possible. Sets the linear and quadratic cost factor of all vehicles. """ return _pywrapcp.RoutingModel_SetAmortizedCostFactorsOfAllVehicles(self, linear_cost_factor, quadratic_cost_factor) def SetAmortizedCostFactorsOfVehicle(self, linear_cost_factor: int64_t, quadratic_cost_factor: int64_t, vehicle: int) ‑> void-
Sets the linear and quadratic cost factor of the given vehicle.
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def SetAmortizedCostFactorsOfVehicle(self, linear_cost_factor: "int64_t", quadratic_cost_factor: "int64_t", vehicle: "int") -> "void": r""" Sets the linear and quadratic cost factor of the given vehicle.""" return _pywrapcp.RoutingModel_SetAmortizedCostFactorsOfVehicle(self, linear_cost_factor, quadratic_cost_factor, vehicle) def SetArcCostEvaluatorOfAllVehicles(self, evaluator_index: int) ‑> void-
Sets the cost function of the model such that the cost of a segment of a route between node 'from' and 'to' is evaluator(from, to), whatever the route or vehicle performing the route.
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def SetArcCostEvaluatorOfAllVehicles(self, evaluator_index: "int") -> "void": r""" Sets the cost function of the model such that the cost of a segment of a route between node 'from' and 'to' is evaluator(from, to), whatever the route or vehicle performing the route. """ return _pywrapcp.RoutingModel_SetArcCostEvaluatorOfAllVehicles(self, evaluator_index) def SetArcCostEvaluatorOfVehicle(self, evaluator_index: int, vehicle: int) ‑> void-
Sets the cost function for a given vehicle route.
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def SetArcCostEvaluatorOfVehicle(self, evaluator_index: "int", vehicle: "int") -> "void": r""" Sets the cost function for a given vehicle route.""" return _pywrapcp.RoutingModel_SetArcCostEvaluatorOfVehicle(self, evaluator_index, vehicle) def SetAssignmentFromOtherModelAssignment(self, target_assignment: Assignment, source_model: RoutingModel, source_assignment: Assignment) ‑> void-
Given a "source_model" and its "source_assignment", resets "target_assignment" with the IntVar variables (nexts_, and vehicle_vars_ if costs aren't homogeneous across vehicles) of "this" model, with the values set according to those in "other_assignment". The objective_element of target_assignment is set to this->cost_.
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def SetAssignmentFromOtherModelAssignment(self, target_assignment: "Assignment", source_model: "RoutingModel", source_assignment: "Assignment") -> "void": r""" Given a "source_model" and its "source_assignment", resets "target_assignment" with the IntVar variables (nexts_, and vehicle_vars_ if costs aren't homogeneous across vehicles) of "this" model, with the values set according to those in "other_assignment". The objective_element of target_assignment is set to this->cost_. """ return _pywrapcp.RoutingModel_SetAssignmentFromOtherModelAssignment(self, target_assignment, source_model, source_assignment) def SetFirstSolutionEvaluator(self, evaluator: operations_research::Solver::IndexEvaluator2) ‑> void-
Gets/sets the evaluator used during the search. Only relevant when RoutingSearchParameters.first_solution_strategy = EVALUATOR_STRATEGY. Takes ownership of evaluator.
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def SetFirstSolutionEvaluator(self, evaluator: "operations_research::Solver::IndexEvaluator2") -> "void": r""" Gets/sets the evaluator used during the search. Only relevant when RoutingSearchParameters.first_solution_strategy = EVALUATOR_STRATEGY. Takes ownership of evaluator. """ return _pywrapcp.RoutingModel_SetFirstSolutionEvaluator(self, evaluator) def SetFixedCostOfAllVehicles(self, cost: int64_t) ‑> void-
Sets the fixed cost of all vehicle routes. It is equivalent to calling SetFixedCostOfVehicle on all vehicle routes.
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def SetFixedCostOfAllVehicles(self, cost: "int64_t") -> "void": r""" Sets the fixed cost of all vehicle routes. It is equivalent to calling SetFixedCostOfVehicle on all vehicle routes. """ return _pywrapcp.RoutingModel_SetFixedCostOfAllVehicles(self, cost) def SetFixedCostOfVehicle(self, cost: int64_t, vehicle: int) ‑> void-
Sets the fixed cost of one vehicle route.
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def SetFixedCostOfVehicle(self, cost: "int64_t", vehicle: "int") -> "void": r""" Sets the fixed cost of one vehicle route.""" return _pywrapcp.RoutingModel_SetFixedCostOfVehicle(self, cost, vehicle) def SetMaximumNumberOfActiveVehicles(self, max_active_vehicles: int) ‑> void-
Constrains the maximum number of active vehicles, aka the number of vehicles which do not have an empty route. For instance, this can be used to limit the number of routes in the case where there are fewer drivers than vehicles and that the fleet of vehicle is heterogeneous.
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def SetMaximumNumberOfActiveVehicles(self, max_active_vehicles: "int") -> "void": r""" Constrains the maximum number of active vehicles, aka the number of vehicles which do not have an empty route. For instance, this can be used to limit the number of routes in the case where there are fewer drivers than vehicles and that the fleet of vehicle is heterogeneous. """ return _pywrapcp.RoutingModel_SetMaximumNumberOfActiveVehicles(self, max_active_vehicles) def SetPickupAndDeliveryPolicyOfAllVehicles(self, policy: operations_research::RoutingModel::PickupAndDeliveryPolicy) ‑> void-
Sets the Pickup and delivery policy of all vehicles. It is equivalent to calling SetPickupAndDeliveryPolicyOfVehicle on all vehicles.
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def SetPickupAndDeliveryPolicyOfAllVehicles(self, policy: "operations_research::RoutingModel::PickupAndDeliveryPolicy") -> "void": r""" Sets the Pickup and delivery policy of all vehicles. It is equivalent to calling SetPickupAndDeliveryPolicyOfVehicle on all vehicles. """ return _pywrapcp.RoutingModel_SetPickupAndDeliveryPolicyOfAllVehicles(self, policy) def SetPickupAndDeliveryPolicyOfVehicle(self, policy: operations_research::RoutingModel::PickupAndDeliveryPolicy, vehicle: int) ‑> void-
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def SetPickupAndDeliveryPolicyOfVehicle(self, policy: "operations_research::RoutingModel::PickupAndDeliveryPolicy", vehicle: "int") -> "void": return _pywrapcp.RoutingModel_SetPickupAndDeliveryPolicyOfVehicle(self, policy, vehicle) def SetPrimaryConstrainedDimension(self, dimension_name: std::string const &) ‑> void-
Set the given dimension as "primary constrained". As of August 2013, this is only used by ArcIsMoreConstrainedThanArc(). "dimension" must be the name of an existing dimension, or be empty, in which case there will not be a primary dimension after this call.
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def SetPrimaryConstrainedDimension(self, dimension_name: "std::string const &") -> "void": r""" Set the given dimension as "primary constrained". As of August 2013, this is only used by ArcIsMoreConstrainedThanArc(). "dimension" must be the name of an existing dimension, or be empty, in which case there will not be a primary dimension after this call. """ return _pywrapcp.RoutingModel_SetPrimaryConstrainedDimension(self, dimension_name) def SetVisitType(self, index: int64_t, type: int, type_policy: operations_research::RoutingModel::VisitTypePolicy) ‑> void-
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def SetVisitType(self, index: "int64_t", type: "int", type_policy: "operations_research::RoutingModel::VisitTypePolicy") -> "void": return _pywrapcp.RoutingModel_SetVisitType(self, index, type, type_policy) def Size(self) ‑> int64_t-
Returns the number of next variables in the model.
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def Size(self) -> "int64_t": r""" Returns the number of next variables in the model.""" return _pywrapcp.RoutingModel_Size(self) def Solve(self, assignment: Assignment = None) ‑> operations_research::Assignment const *-
Solves the current routing model; closes the current model. This is equivalent to calling SolveWithParameters(DefaultRoutingSearchParameters()) or SolveFromAssignmentWithParameters(assignment, DefaultRoutingSearchParameters()).
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def Solve(self, assignment: "Assignment"=None) -> "operations_research::Assignment const *": r""" Solves the current routing model; closes the current model. This is equivalent to calling SolveWithParameters(DefaultRoutingSearchParameters()) or SolveFromAssignmentWithParameters(assignment, DefaultRoutingSearchParameters()). """ return _pywrapcp.RoutingModel_Solve(self, assignment) def SolveFromAssignmentWithParameters(self, assignment: Assignment, search_parameters: operations_research::RoutingSearchParameters const &, solutions: std::vector< operations_research::Assignment const * > * = None) ‑> operations_research::Assignment const *-
Same as above, except that if assignment is not null, it will be used as the initial solution.
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def SolveFromAssignmentWithParameters(self, assignment: "Assignment", search_parameters: "operations_research::RoutingSearchParameters const &", solutions: "std::vector< operations_research::Assignment const * > *"=None) -> "operations_research::Assignment const *": r""" Same as above, except that if assignment is not null, it will be used as the initial solution. """ return _pywrapcp.RoutingModel_SolveFromAssignmentWithParameters(self, assignment, search_parameters, solutions) def SolveFromAssignmentsWithParameters(self, assignments: std::vector< operations_research::Assignment const * > const &, search_parameters: operations_research::RoutingSearchParameters const &, solutions: std::vector< operations_research::Assignment const * > * = None) ‑> operations_research::Assignment const *-
Same as above but will try all assignments in order as first solutions until one succeeds.
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def SolveFromAssignmentsWithParameters(self, assignments: "std::vector< operations_research::Assignment const * > const &", search_parameters: "operations_research::RoutingSearchParameters const &", solutions: "std::vector< operations_research::Assignment const * > *"=None) -> "operations_research::Assignment const *": r""" Same as above but will try all assignments in order as first solutions until one succeeds. """ return _pywrapcp.RoutingModel_SolveFromAssignmentsWithParameters(self, assignments, search_parameters, solutions) def SolveWithParameters(self, search_parameters: operations_research::RoutingSearchParameters const &, solutions: std::vector< operations_research::Assignment const * > * = None) ‑> operations_research::Assignment const *-
Solves the current routing model with the given parameters. If 'solutions' is specified, it will contain the k best solutions found during the search (from worst to best, including the one returned by this method), where k corresponds to the 'number_of_solutions_to_collect' in 'search_parameters'. Note that the Assignment returned by the method and the ones in solutions are owned by the underlying solver and should not be deleted.
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def SolveWithParameters(self, search_parameters: "operations_research::RoutingSearchParameters const &", solutions: "std::vector< operations_research::Assignment const * > *"=None) -> "operations_research::Assignment const *": r""" Solves the current routing model with the given parameters. If 'solutions' is specified, it will contain the k best solutions found during the search (from worst to best, including the one returned by this method), where k corresponds to the 'number_of_solutions_to_collect' in 'search_parameters'. Note that the Assignment returned by the method and the ones in solutions are owned by the underlying solver and should not be deleted. """ return _pywrapcp.RoutingModel_SolveWithParameters(self, search_parameters, solutions) def Start(self, vehicle: int) ‑> int64_t-
Model inspection. Returns the variable index of the starting node of a vehicle route.
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def Start(self, vehicle: "int") -> "int64_t": r""" Model inspection. Returns the variable index of the starting node of a vehicle route. """ return _pywrapcp.RoutingModel_Start(self, vehicle) def TransitCallback(self, callback_index: int) ‑> operations_research::RoutingModel::TransitCallback2 const &-
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def TransitCallback(self, callback_index: "int") -> "operations_research::RoutingModel::TransitCallback2 const &": return _pywrapcp.RoutingModel_TransitCallback(self, callback_index) def UnaryTransitCallbackOrNull(self, callback_index: int) ‑> operations_research::RoutingModel::TransitCallback1 const &-
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def UnaryTransitCallbackOrNull(self, callback_index: "int") -> "operations_research::RoutingModel::TransitCallback1 const &": return _pywrapcp.RoutingModel_UnaryTransitCallbackOrNull(self, callback_index) def UnperformedPenalty(self, var_index: int64_t) ‑> int64_t-
Get the "unperformed" penalty of a node. This is only well defined if the node is only part of a single Disjunction involving only itself, and that disjunction has a penalty. In all other cases, including forced active nodes, this returns 0.
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def UnperformedPenalty(self, var_index: "int64_t") -> "int64_t": r""" Get the "unperformed" penalty of a node. This is only well defined if the node is only part of a single Disjunction involving only itself, and that disjunction has a penalty. In all other cases, including forced active nodes, this returns 0. """ return _pywrapcp.RoutingModel_UnperformedPenalty(self, var_index) def UnperformedPenaltyOrValue(self, default_value: int64_t, var_index: int64_t) ‑> int64_t-
Same as above except that it returns default_value instead of 0 when penalty is not well defined (default value is passed as first argument to simplify the usage of the method in a callback).
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def UnperformedPenaltyOrValue(self, default_value: "int64_t", var_index: "int64_t") -> "int64_t": r""" Same as above except that it returns default_value instead of 0 when penalty is not well defined (default value is passed as first argument to simplify the usage of the method in a callback). """ return _pywrapcp.RoutingModel_UnperformedPenaltyOrValue(self, default_value, var_index) def VehicleCostsConsideredVar(self, vehicle: int) ‑> operations_research::IntVar *-
Returns the variable specifying whether or not costs are considered for vehicle.
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def VehicleCostsConsideredVar(self, vehicle: "int") -> "operations_research::IntVar *": r""" Returns the variable specifying whether or not costs are considered for vehicle. """ return _pywrapcp.RoutingModel_VehicleCostsConsideredVar(self, vehicle) def VehicleIndex(self, index: int64_t) ‑> int-
Returns the vehicle of the given start/end index, and -1 if the given index is not a vehicle start/end.
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def VehicleIndex(self, index: "int64_t") -> "int": r""" Returns the vehicle of the given start/end index, and -1 if the given index is not a vehicle start/end. """ return _pywrapcp.RoutingModel_VehicleIndex(self, index) def VehicleVar(self, index: int64_t) ‑> operations_research::IntVar *-
Returns the vehicle variable of the node corresponding to index. Note that VehicleVar(index) == -1 is equivalent to ActiveVar(index) == 0.
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def VehicleVar(self, index: "int64_t") -> "operations_research::IntVar *": r""" Returns the vehicle variable of the node corresponding to index. Note that VehicleVar(index) == -1 is equivalent to ActiveVar(index) == 0. """ return _pywrapcp.RoutingModel_VehicleVar(self, index) def WriteAssignment(self, file_name: std::string const &) ‑> bool-
Writes the current solution to a file containing an AssignmentProto. Returns false if the file cannot be opened or if there is no current solution.
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def WriteAssignment(self, file_name: "std::string const &") -> "bool": r""" Writes the current solution to a file containing an AssignmentProto. Returns false if the file cannot be opened or if there is no current solution. """ return _pywrapcp.RoutingModel_WriteAssignment(self, file_name) def nodes(self) ‑> int-
Sizes and indices Returns the number of nodes in the model.
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def nodes(self) -> "int": r""" Sizes and indices Returns the number of nodes in the model. """ return _pywrapcp.RoutingModel_nodes(self) def solver(self) ‑> operations_research::Solver *-
Returns a vector cumul_bounds, for which cumul_bounds[i][j] is a pair containing the minimum and maximum of the CumulVar of the jth node on route i. - cumul_bounds[i][j].first is the minimum. - cumul_bounds[i][j].second is the maximum. Returns the underlying constraint solver. Can be used to add extra constraints and/or modify search algoithms.
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def solver(self) -> "operations_research::Solver *": r""" Returns a vector cumul_bounds, for which cumul_bounds[i][j] is a pair containing the minimum and maximum of the CumulVar of the jth node on route i. - cumul_bounds[i][j].first is the minimum. - cumul_bounds[i][j].second is the maximum. Returns the underlying constraint solver. Can be used to add extra constraints and/or modify search algoithms. """ return _pywrapcp.RoutingModel_solver(self) def status(self) ‑> operations_research::RoutingModel::Status-
Returns the current status of the routing model.
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def status(self) -> "operations_research::RoutingModel::Status": r""" Returns the current status of the routing model.""" return _pywrapcp.RoutingModel_status(self) def vehicles(self) ‑> int-
Returns the number of vehicle routes in the model.
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def vehicles(self) -> "int": r""" Returns the number of vehicle routes in the model.""" return _pywrapcp.RoutingModel_vehicles(self)
class RoutingModelVisitor-
Routing model visitor.
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class RoutingModelVisitor(BaseObject): r""" Routing model visitor.""" thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") __repr__ = _swig_repr def __init__(self): _pywrapcp.RoutingModelVisitor_swiginit(self, _pywrapcp.new_RoutingModelVisitor()) __swig_destroy__ = _pywrapcp.delete_RoutingModelVisitorAncestors
Class variables
var kLightElementvar kLightElement2var kRemoveValues
Inherited members
class SearchLimit (*args, **kwargs)-
Base class of all search limits.
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class SearchLimit(SearchMonitor): r""" Base class of all search limits.""" thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined - class is abstract") __repr__ = _swig_repr __swig_destroy__ = _pywrapcp.delete_SearchLimit def Crossed(self) -> "bool": r""" Returns true if the limit has been crossed.""" return _pywrapcp.SearchLimit_Crossed(self) def Check(self) -> "bool": r""" This method is called to check the status of the limit. A return value of true indicates that we have indeed crossed the limit. In that case, this method will not be called again and the remaining search will be discarded. """ return _pywrapcp.SearchLimit_Check(self) def Init(self) -> "void": r""" This method is called when the search limit is initialized.""" return _pywrapcp.SearchLimit_Init(self) def EnterSearch(self) -> "void": r""" Internal methods.""" return _pywrapcp.SearchLimit_EnterSearch(self) def BeginNextDecision(self, b: "DecisionBuilder") -> "void": return _pywrapcp.SearchLimit_BeginNextDecision(self, b) def RefuteDecision(self, d: "Decision") -> "void": return _pywrapcp.SearchLimit_RefuteDecision(self, d) def DebugString(self) -> "std::string": return _pywrapcp.SearchLimit_DebugString(self)Ancestors
Methods
def Check(self) ‑> bool-
This method is called to check the status of the limit. A return value of true indicates that we have indeed crossed the limit. In that case, this method will not be called again and the remaining search will be discarded.
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def Check(self) -> "bool": r""" This method is called to check the status of the limit. A return value of true indicates that we have indeed crossed the limit. In that case, this method will not be called again and the remaining search will be discarded. """ return _pywrapcp.SearchLimit_Check(self) def Crossed(self) ‑> bool-
Returns true if the limit has been crossed.
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def Crossed(self) -> "bool": r""" Returns true if the limit has been crossed.""" return _pywrapcp.SearchLimit_Crossed(self) def DebugString(self) ‑> std::string-
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def DebugString(self) -> "std::string": return _pywrapcp.SearchLimit_DebugString(self) def EnterSearch(self) ‑> void-
Internal methods.
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def EnterSearch(self) -> "void": r""" Internal methods.""" return _pywrapcp.SearchLimit_EnterSearch(self) def Init(self) ‑> void-
This method is called when the search limit is initialized.
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def Init(self) -> "void": r""" This method is called when the search limit is initialized.""" return _pywrapcp.SearchLimit_Init(self)
Inherited members
class SearchMonitor (s:Â Solver)-
A search monitor is a simple set of callbacks to monitor all search events
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class SearchMonitor(BaseObject): r""" A search monitor is a simple set of callbacks to monitor all search events""" thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") def __init__(self, s: "Solver"): if self.__class__ == SearchMonitor: _self = None else: _self = self _pywrapcp.SearchMonitor_swiginit(self, _pywrapcp.new_SearchMonitor(_self, s)) __swig_destroy__ = _pywrapcp.delete_SearchMonitor def EnterSearch(self) -> "void": r""" Beginning of the search.""" return _pywrapcp.SearchMonitor_EnterSearch(self) def RestartSearch(self) -> "void": r""" Restart the search.""" return _pywrapcp.SearchMonitor_RestartSearch(self) def ExitSearch(self) -> "void": r""" End of the search.""" return _pywrapcp.SearchMonitor_ExitSearch(self) def BeginNextDecision(self, b: "DecisionBuilder") -> "void": r""" Before calling DecisionBuilder::Next.""" return _pywrapcp.SearchMonitor_BeginNextDecision(self, b) def EndNextDecision(self, b: "DecisionBuilder", d: "Decision") -> "void": r""" After calling DecisionBuilder::Next, along with the returned decision.""" return _pywrapcp.SearchMonitor_EndNextDecision(self, b, d) def ApplyDecision(self, d: "Decision") -> "void": r""" Before applying the decision.""" return _pywrapcp.SearchMonitor_ApplyDecision(self, d) def RefuteDecision(self, d: "Decision") -> "void": r""" Before refuting the decision.""" return _pywrapcp.SearchMonitor_RefuteDecision(self, d) def AfterDecision(self, d: "Decision", apply: "bool") -> "void": r""" Just after refuting or applying the decision, apply is true after Apply. This is called only if the Apply() or Refute() methods have not failed. """ return _pywrapcp.SearchMonitor_AfterDecision(self, d, apply) def BeginFail(self) -> "void": r""" Just when the failure occurs.""" return _pywrapcp.SearchMonitor_BeginFail(self) def EndFail(self) -> "void": r""" After completing the backtrack.""" return _pywrapcp.SearchMonitor_EndFail(self) def BeginInitialPropagation(self) -> "void": r""" Before the initial propagation.""" return _pywrapcp.SearchMonitor_BeginInitialPropagation(self) def EndInitialPropagation(self) -> "void": r""" After the initial propagation.""" return _pywrapcp.SearchMonitor_EndInitialPropagation(self) def AcceptSolution(self) -> "bool": r""" This method is called when a solution is found. It asserts whether the solution is valid. A value of false indicates that the solution should be discarded. """ return _pywrapcp.SearchMonitor_AcceptSolution(self) def AtSolution(self) -> "bool": r""" This method is called when a valid solution is found. If the return value is true, then search will resume after. If the result is false, then search will stop there. """ return _pywrapcp.SearchMonitor_AtSolution(self) def NoMoreSolutions(self) -> "void": r""" When the search tree is finished.""" return _pywrapcp.SearchMonitor_NoMoreSolutions(self) def LocalOptimum(self) -> "bool": r""" When a local optimum is reached. If 'true' is returned, the last solution is discarded and the search proceeds with the next one. """ return _pywrapcp.SearchMonitor_LocalOptimum(self) def AcceptDelta(self, delta: "Assignment", deltadelta: "Assignment") -> "bool": return _pywrapcp.SearchMonitor_AcceptDelta(self, delta, deltadelta) def AcceptNeighbor(self) -> "void": r""" After accepting a neighbor during local search.""" return _pywrapcp.SearchMonitor_AcceptNeighbor(self) def solver(self) -> "operations_research::Solver *": return _pywrapcp.SearchMonitor_solver(self) def __repr__(self) -> "std::string": return _pywrapcp.SearchMonitor___repr__(self) def __str__(self) -> "std::string": return _pywrapcp.SearchMonitor___str__(self) def __disown__(self): self.this.disown() _pywrapcp.disown_SearchMonitor(self) return weakref.proxy(self)Ancestors
Subclasses
Methods
def AcceptDelta(self, delta: Assignment, deltadelta: Assignment) ‑> bool-
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def AcceptDelta(self, delta: "Assignment", deltadelta: "Assignment") -> "bool": return _pywrapcp.SearchMonitor_AcceptDelta(self, delta, deltadelta) def AcceptNeighbor(self) ‑> void-
After accepting a neighbor during local search.
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def AcceptNeighbor(self) -> "void": r""" After accepting a neighbor during local search.""" return _pywrapcp.SearchMonitor_AcceptNeighbor(self) def AcceptSolution(self) ‑> bool-
This method is called when a solution is found. It asserts whether the solution is valid. A value of false indicates that the solution should be discarded.
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def AcceptSolution(self) -> "bool": r""" This method is called when a solution is found. It asserts whether the solution is valid. A value of false indicates that the solution should be discarded. """ return _pywrapcp.SearchMonitor_AcceptSolution(self) def AfterDecision(self, d: Decision, apply: bool) ‑> void-
Just after refuting or applying the decision, apply is true after Apply. This is called only if the Apply() or Refute() methods have not failed.
Expand source code
def AfterDecision(self, d: "Decision", apply: "bool") -> "void": r""" Just after refuting or applying the decision, apply is true after Apply. This is called only if the Apply() or Refute() methods have not failed. """ return _pywrapcp.SearchMonitor_AfterDecision(self, d, apply) def ApplyDecision(self, d: Decision) ‑> void-
Before applying the decision.
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def ApplyDecision(self, d: "Decision") -> "void": r""" Before applying the decision.""" return _pywrapcp.SearchMonitor_ApplyDecision(self, d) def AtSolution(self) ‑> bool-
This method is called when a valid solution is found. If the return value is true, then search will resume after. If the result is false, then search will stop there.
Expand source code
def AtSolution(self) -> "bool": r""" This method is called when a valid solution is found. If the return value is true, then search will resume after. If the result is false, then search will stop there. """ return _pywrapcp.SearchMonitor_AtSolution(self) def BeginFail(self) ‑> void-
Just when the failure occurs.
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def BeginFail(self) -> "void": r""" Just when the failure occurs.""" return _pywrapcp.SearchMonitor_BeginFail(self) def BeginInitialPropagation(self) ‑> void-
Before the initial propagation.
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def BeginInitialPropagation(self) -> "void": r""" Before the initial propagation.""" return _pywrapcp.SearchMonitor_BeginInitialPropagation(self) def BeginNextDecision(self, b: DecisionBuilder) ‑> void-
Before calling DecisionBuilder::Next.
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def BeginNextDecision(self, b: "DecisionBuilder") -> "void": r""" Before calling DecisionBuilder::Next.""" return _pywrapcp.SearchMonitor_BeginNextDecision(self, b) def EndFail(self) ‑> void-
After completing the backtrack.
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def EndFail(self) -> "void": r""" After completing the backtrack.""" return _pywrapcp.SearchMonitor_EndFail(self) def EndInitialPropagation(self) ‑> void-
After the initial propagation.
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def EndInitialPropagation(self) -> "void": r""" After the initial propagation.""" return _pywrapcp.SearchMonitor_EndInitialPropagation(self) def EndNextDecision(self, b: DecisionBuilder, d: Decision) ‑> void-
After calling DecisionBuilder::Next, along with the returned decision.
Expand source code
def EndNextDecision(self, b: "DecisionBuilder", d: "Decision") -> "void": r""" After calling DecisionBuilder::Next, along with the returned decision.""" return _pywrapcp.SearchMonitor_EndNextDecision(self, b, d) def EnterSearch(self) ‑> void-
Beginning of the search.
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def EnterSearch(self) -> "void": r""" Beginning of the search.""" return _pywrapcp.SearchMonitor_EnterSearch(self) def ExitSearch(self) ‑> void-
End of the search.
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def ExitSearch(self) -> "void": r""" End of the search.""" return _pywrapcp.SearchMonitor_ExitSearch(self) def LocalOptimum(self) ‑> bool-
When a local optimum is reached. If 'true' is returned, the last solution is discarded and the search proceeds with the next one.
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def LocalOptimum(self) -> "bool": r""" When a local optimum is reached. If 'true' is returned, the last solution is discarded and the search proceeds with the next one. """ return _pywrapcp.SearchMonitor_LocalOptimum(self) def NoMoreSolutions(self) ‑> void-
When the search tree is finished.
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def NoMoreSolutions(self) -> "void": r""" When the search tree is finished.""" return _pywrapcp.SearchMonitor_NoMoreSolutions(self) def RefuteDecision(self, d: Decision) ‑> void-
Before refuting the decision.
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def RefuteDecision(self, d: "Decision") -> "void": r""" Before refuting the decision.""" return _pywrapcp.SearchMonitor_RefuteDecision(self, d) def RestartSearch(self) ‑> void-
Restart the search.
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def RestartSearch(self) -> "void": r""" Restart the search.""" return _pywrapcp.SearchMonitor_RestartSearch(self) def solver(self) ‑> operations_research::Solver *-
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def solver(self) -> "operations_research::Solver *": return _pywrapcp.SearchMonitor_solver(self)
Inherited members
class SequenceVar (*args, **kwargs)-
A sequence variable is a variable whose domain is a set of possible orderings of the interval variables. It allows ordering of tasks. It has two sets of methods: ComputePossibleFirstsAndLasts(), which returns the list of interval variables that can be ranked first or last; and RankFirst/RankNotFirst/RankLast/RankNotLast, which can be used to create the search decision.
Expand source code
class SequenceVar(PropagationBaseObject): r""" A sequence variable is a variable whose domain is a set of possible orderings of the interval variables. It allows ordering of tasks. It has two sets of methods: ComputePossibleFirstsAndLasts(), which returns the list of interval variables that can be ranked first or last; and RankFirst/RankNotFirst/RankLast/RankNotLast, which can be used to create the search decision. """ thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined") def DebugString(self) -> "std::string": return _pywrapcp.SequenceVar_DebugString(self) def RankFirst(self, index: "int") -> "void": r""" Ranks the index_th interval var first of all unranked interval vars. After that, it will no longer be considered ranked. """ return _pywrapcp.SequenceVar_RankFirst(self, index) def RankNotFirst(self, index: "int") -> "void": r""" Indicates that the index_th interval var will not be ranked first of all currently unranked interval vars. """ return _pywrapcp.SequenceVar_RankNotFirst(self, index) def RankLast(self, index: "int") -> "void": r""" Ranks the index_th interval var first of all unranked interval vars. After that, it will no longer be considered ranked. """ return _pywrapcp.SequenceVar_RankLast(self, index) def RankNotLast(self, index: "int") -> "void": r""" Indicates that the index_th interval var will not be ranked first of all currently unranked interval vars. """ return _pywrapcp.SequenceVar_RankNotLast(self, index) def Interval(self, index: "int") -> "operations_research::IntervalVar *": r""" Returns the index_th interval of the sequence.""" return _pywrapcp.SequenceVar_Interval(self, index) def Next(self, index: "int") -> "operations_research::IntVar *": r""" Returns the next of the index_th interval of the sequence.""" return _pywrapcp.SequenceVar_Next(self, index) def Size(self) -> "int64_t": r""" Returns the number of interval vars in the sequence.""" return _pywrapcp.SequenceVar_Size(self) def __repr__(self) -> "std::string": return _pywrapcp.SequenceVar___repr__(self) def __str__(self) -> "std::string": return _pywrapcp.SequenceVar___str__(self)Ancestors
Methods
def DebugString(self) ‑> std::string-
Expand source code
def DebugString(self) -> "std::string": return _pywrapcp.SequenceVar_DebugString(self) def Interval(self, index: int) ‑> operations_research::IntervalVar *-
Returns the index_th interval of the sequence.
Expand source code
def Interval(self, index: "int") -> "operations_research::IntervalVar *": r""" Returns the index_th interval of the sequence.""" return _pywrapcp.SequenceVar_Interval(self, index) def Next(self, index: int) ‑> operations_research::IntVar *-
Returns the next of the index_th interval of the sequence.
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def Next(self, index: "int") -> "operations_research::IntVar *": r""" Returns the next of the index_th interval of the sequence.""" return _pywrapcp.SequenceVar_Next(self, index) def RankFirst(self, index: int) ‑> void-
Ranks the index_th interval var first of all unranked interval vars. After that, it will no longer be considered ranked.
Expand source code
def RankFirst(self, index: "int") -> "void": r""" Ranks the index_th interval var first of all unranked interval vars. After that, it will no longer be considered ranked. """ return _pywrapcp.SequenceVar_RankFirst(self, index) def RankLast(self, index: int) ‑> void-
Ranks the index_th interval var first of all unranked interval vars. After that, it will no longer be considered ranked.
Expand source code
def RankLast(self, index: "int") -> "void": r""" Ranks the index_th interval var first of all unranked interval vars. After that, it will no longer be considered ranked. """ return _pywrapcp.SequenceVar_RankLast(self, index) def RankNotFirst(self, index: int) ‑> void-
Indicates that the index_th interval var will not be ranked first of all currently unranked interval vars.
Expand source code
def RankNotFirst(self, index: "int") -> "void": r""" Indicates that the index_th interval var will not be ranked first of all currently unranked interval vars. """ return _pywrapcp.SequenceVar_RankNotFirst(self, index) def RankNotLast(self, index: int) ‑> void-
Indicates that the index_th interval var will not be ranked first of all currently unranked interval vars.
Expand source code
def RankNotLast(self, index: "int") -> "void": r""" Indicates that the index_th interval var will not be ranked first of all currently unranked interval vars. """ return _pywrapcp.SequenceVar_RankNotLast(self, index) def Size(self) ‑> int64_t-
Returns the number of interval vars in the sequence.
Expand source code
def Size(self) -> "int64_t": r""" Returns the number of interval vars in the sequence.""" return _pywrapcp.SequenceVar_Size(self)
Inherited members
class SequenceVarContainer (*args, **kwargs)-
Expand source code
class SequenceVarContainer(object): thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined") __repr__ = _swig_repr def Contains(self, var: "SequenceVar") -> "bool": return _pywrapcp.SequenceVarContainer_Contains(self, var) def Element(self, index: "int") -> "operations_research::SequenceVarElement *": return _pywrapcp.SequenceVarContainer_Element(self, index) def Size(self) -> "int": return _pywrapcp.SequenceVarContainer_Size(self) def Store(self) -> "void": return _pywrapcp.SequenceVarContainer_Store(self) def Restore(self) -> "void": return _pywrapcp.SequenceVarContainer_Restore(self) def __eq__(self, container: "SequenceVarContainer") -> "bool": r""" Returns true if this and 'container' both represent the same V* -> E map. Runs in linear time; requires that the == operator on the type E is well defined. """ return _pywrapcp.SequenceVarContainer___eq__(self, container) def __ne__(self, container: "SequenceVarContainer") -> "bool": return _pywrapcp.SequenceVarContainer___ne__(self, container) __swig_destroy__ = _pywrapcp.delete_SequenceVarContainerInstance variables
var thisown-
The membership flag
Expand source code
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
Methods
def Contains(self, var: SequenceVar) ‑> bool-
Expand source code
def Contains(self, var: "SequenceVar") -> "bool": return _pywrapcp.SequenceVarContainer_Contains(self, var) def Element(self, index: int) ‑> operations_research::SequenceVarElement *-
Expand source code
def Element(self, index: "int") -> "operations_research::SequenceVarElement *": return _pywrapcp.SequenceVarContainer_Element(self, index) def Restore(self) ‑> void-
Expand source code
def Restore(self) -> "void": return _pywrapcp.SequenceVarContainer_Restore(self) def Size(self) ‑> int-
Expand source code
def Size(self) -> "int": return _pywrapcp.SequenceVarContainer_Size(self) def Store(self) ‑> void-
Expand source code
def Store(self) -> "void": return _pywrapcp.SequenceVarContainer_Store(self)
class SequenceVarElement (*args, **kwargs)-
The SequenceVarElement stores a partial representation of ranked interval variables in the underlying sequence variable. This representation consists of three vectors: - the forward sequence. That is the list of interval variables ranked first in the sequence. The first element of the backward sequence is the first interval in the sequence variable. - the backward sequence. That is the list of interval variables ranked last in the sequence. The first element of the backward sequence is the last interval in the sequence variable. - The list of unperformed interval variables. Furthermore, if all performed variables are ranked, then by convention, the forward_sequence will contain all such variables and the backward_sequence will be empty.
Expand source code
class SequenceVarElement(AssignmentElement): r""" The SequenceVarElement stores a partial representation of ranked interval variables in the underlying sequence variable. This representation consists of three vectors: - the forward sequence. That is the list of interval variables ranked first in the sequence. The first element of the backward sequence is the first interval in the sequence variable. - the backward sequence. That is the list of interval variables ranked last in the sequence. The first element of the backward sequence is the last interval in the sequence variable. - The list of unperformed interval variables. Furthermore, if all performed variables are ranked, then by convention, the forward_sequence will contain all such variables and the backward_sequence will be empty. """ thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined") __repr__ = _swig_repr def Var(self) -> "operations_research::SequenceVar *": return _pywrapcp.SequenceVarElement_Var(self) def ForwardSequence(self) -> "std::vector< int > const &": return _pywrapcp.SequenceVarElement_ForwardSequence(self) def BackwardSequence(self) -> "std::vector< int > const &": return _pywrapcp.SequenceVarElement_BackwardSequence(self) def Unperformed(self) -> "std::vector< int > const &": return _pywrapcp.SequenceVarElement_Unperformed(self) def SetSequence(self, forward_sequence: "std::vector< int > const &", backward_sequence: "std::vector< int > const &", unperformed: "std::vector< int > const &") -> "void": return _pywrapcp.SequenceVarElement_SetSequence(self, forward_sequence, backward_sequence, unperformed) def SetForwardSequence(self, forward_sequence: "std::vector< int > const &") -> "void": return _pywrapcp.SequenceVarElement_SetForwardSequence(self, forward_sequence) def SetBackwardSequence(self, backward_sequence: "std::vector< int > const &") -> "void": return _pywrapcp.SequenceVarElement_SetBackwardSequence(self, backward_sequence) def SetUnperformed(self, unperformed: "std::vector< int > const &") -> "void": return _pywrapcp.SequenceVarElement_SetUnperformed(self, unperformed) def __eq__(self, element: "SequenceVarElement") -> "bool": return _pywrapcp.SequenceVarElement___eq__(self, element) def __ne__(self, element: "SequenceVarElement") -> "bool": return _pywrapcp.SequenceVarElement___ne__(self, element) __swig_destroy__ = _pywrapcp.delete_SequenceVarElementAncestors
Methods
def BackwardSequence(self) ‑> std::vector< int > const &-
Expand source code
def BackwardSequence(self) -> "std::vector< int > const &": return _pywrapcp.SequenceVarElement_BackwardSequence(self) def ForwardSequence(self) ‑> std::vector< int > const &-
Expand source code
def ForwardSequence(self) -> "std::vector< int > const &": return _pywrapcp.SequenceVarElement_ForwardSequence(self) def SetBackwardSequence(self, backward_sequence: std::vector< int > const &) ‑> void-
Expand source code
def SetBackwardSequence(self, backward_sequence: "std::vector< int > const &") -> "void": return _pywrapcp.SequenceVarElement_SetBackwardSequence(self, backward_sequence) def SetForwardSequence(self, forward_sequence: std::vector< int > const &) ‑> void-
Expand source code
def SetForwardSequence(self, forward_sequence: "std::vector< int > const &") -> "void": return _pywrapcp.SequenceVarElement_SetForwardSequence(self, forward_sequence) def SetSequence(self, forward_sequence: std::vector< int > const &, backward_sequence: std::vector< int > const &, unperformed: std::vector< int > const &) ‑> void-
Expand source code
def SetSequence(self, forward_sequence: "std::vector< int > const &", backward_sequence: "std::vector< int > const &", unperformed: "std::vector< int > const &") -> "void": return _pywrapcp.SequenceVarElement_SetSequence(self, forward_sequence, backward_sequence, unperformed) def SetUnperformed(self, unperformed: std::vector< int > const &) ‑> void-
Expand source code
def SetUnperformed(self, unperformed: "std::vector< int > const &") -> "void": return _pywrapcp.SequenceVarElement_SetUnperformed(self, unperformed) def Unperformed(self) ‑> std::vector< int > const &-
Expand source code
def Unperformed(self) -> "std::vector< int > const &": return _pywrapcp.SequenceVarElement_Unperformed(self) def Var(self) ‑> operations_research::SequenceVar *-
Expand source code
def Var(self) -> "operations_research::SequenceVar *": return _pywrapcp.SequenceVarElement_Var(self)
Inherited members
class SequenceVarLocalSearchOperator (*args, **kwargs)-
Base operator class for operators manipulating variables.
Expand source code
class SequenceVarLocalSearchOperator(SequenceVarLocalSearchOperatorTemplate): thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined - class is abstract") __repr__ = _swig_reprAncestors
Inherited members
class SequenceVarLocalSearchOperatorTemplate (*args, **kwargs)-
Base operator class for operators manipulating variables.
Expand source code
class SequenceVarLocalSearchOperatorTemplate(LocalSearchOperator): r""" Base operator class for operators manipulating variables.""" thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined - class is abstract") __repr__ = _swig_repr def Start(self, assignment: "Assignment") -> "void": r""" This method should not be overridden. Override OnStart() instead which is called before exiting this method. """ return _pywrapcp.SequenceVarLocalSearchOperatorTemplate_Start(self, assignment) def IsIncremental(self) -> "bool": return _pywrapcp.SequenceVarLocalSearchOperatorTemplate_IsIncremental(self) def Size(self) -> "int": return _pywrapcp.SequenceVarLocalSearchOperatorTemplate_Size(self) def Value(self, index: "int64_t") -> "std::vector< int > const &": r""" Returns the value in the current assignment of the variable of given index. """ return _pywrapcp.SequenceVarLocalSearchOperatorTemplate_Value(self, index) def OldValue(self, index: "int64_t") -> "std::vector< int > const &": return _pywrapcp.SequenceVarLocalSearchOperatorTemplate_OldValue(self, index) def SetValue(self, index: "int64_t", value: "std::vector< int > const &") -> "void": return _pywrapcp.SequenceVarLocalSearchOperatorTemplate_SetValue(self, index, value) def OnStart(self) -> "void": r""" Called by Start() after synchronizing the operator with the current assignment. Should be overridden instead of Start() to avoid calling VarLocalSearchOperator::Start explicitly. """ return _pywrapcp.SequenceVarLocalSearchOperatorTemplate_OnStart(self)Ancestors
Subclasses
Methods
def IsIncremental(self) ‑> bool-
Expand source code
def IsIncremental(self) -> "bool": return _pywrapcp.SequenceVarLocalSearchOperatorTemplate_IsIncremental(self) def OldValue(self, index: int64_t) ‑> std::vector< int > const &-
Expand source code
def OldValue(self, index: "int64_t") -> "std::vector< int > const &": return _pywrapcp.SequenceVarLocalSearchOperatorTemplate_OldValue(self, index) def OnStart(self) ‑> void-
Called by Start() after synchronizing the operator with the current assignment. Should be overridden instead of Start() to avoid calling VarLocalSearchOperator::Start explicitly.
Expand source code
def OnStart(self) -> "void": r""" Called by Start() after synchronizing the operator with the current assignment. Should be overridden instead of Start() to avoid calling VarLocalSearchOperator::Start explicitly. """ return _pywrapcp.SequenceVarLocalSearchOperatorTemplate_OnStart(self) def SetValue(self, index: int64_t, value: std::vector< int > const &) ‑> void-
Expand source code
def SetValue(self, index: "int64_t", value: "std::vector< int > const &") -> "void": return _pywrapcp.SequenceVarLocalSearchOperatorTemplate_SetValue(self, index, value) def Size(self) ‑> int-
Expand source code
def Size(self) -> "int": return _pywrapcp.SequenceVarLocalSearchOperatorTemplate_Size(self) def Start(self, assignment: Assignment) ‑> void-
This method should not be overridden. Override OnStart() instead which is called before exiting this method.
Expand source code
def Start(self, assignment: "Assignment") -> "void": r""" This method should not be overridden. Override OnStart() instead which is called before exiting this method. """ return _pywrapcp.SequenceVarLocalSearchOperatorTemplate_Start(self, assignment) def Value(self, index: int64_t) ‑> std::vector< int > const &-
Returns the value in the current assignment of the variable of given index.
Expand source code
def Value(self, index: "int64_t") -> "std::vector< int > const &": r""" Returns the value in the current assignment of the variable of given index. """ return _pywrapcp.SequenceVarLocalSearchOperatorTemplate_Value(self, index)
Inherited members
class SolutionCollector (*args, **kwargs)-
This class is the root class of all solution collectors. It implements a basic query API to be used independently of the collector used.
Expand source code
class SolutionCollector(SearchMonitor): r""" This class is the root class of all solution collectors. It implements a basic query API to be used independently of the collector used. """ thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined") __repr__ = _swig_repr def DebugString(self) -> "std::string": return _pywrapcp.SolutionCollector_DebugString(self) def Add(self, *args) -> "void": return _pywrapcp.SolutionCollector_Add(self, *args) def AddObjective(self, objective: "IntVar") -> "void": return _pywrapcp.SolutionCollector_AddObjective(self, objective) def EnterSearch(self) -> "void": r""" Beginning of the search.""" return _pywrapcp.SolutionCollector_EnterSearch(self) def SolutionCount(self) -> "int": r""" Returns how many solutions were stored during the search.""" return _pywrapcp.SolutionCollector_SolutionCount(self) def Solution(self, n: "int") -> "operations_research::Assignment *": r""" Returns the nth solution.""" return _pywrapcp.SolutionCollector_Solution(self, n) def WallTime(self, n: "int") -> "int64_t": r""" Returns the wall time in ms for the nth solution.""" return _pywrapcp.SolutionCollector_WallTime(self, n) def Branches(self, n: "int") -> "int64_t": r""" Returns the number of branches when the nth solution was found.""" return _pywrapcp.SolutionCollector_Branches(self, n) def Failures(self, n: "int") -> "int64_t": r""" Returns the number of failures encountered at the time of the nth solution. """ return _pywrapcp.SolutionCollector_Failures(self, n) def ObjectiveValue(self, n: "int") -> "int64_t": r""" Returns the objective value of the nth solution.""" return _pywrapcp.SolutionCollector_ObjectiveValue(self, n) def Value(self, n: "int", var: "IntVar") -> "int64_t": r""" This is a shortcut to get the Value of 'var' in the nth solution.""" return _pywrapcp.SolutionCollector_Value(self, n, var) def StartValue(self, n: "int", var: "IntervalVar") -> "int64_t": r""" This is a shortcut to get the StartValue of 'var' in the nth solution.""" return _pywrapcp.SolutionCollector_StartValue(self, n, var) def EndValue(self, n: "int", var: "IntervalVar") -> "int64_t": r""" This is a shortcut to get the EndValue of 'var' in the nth solution.""" return _pywrapcp.SolutionCollector_EndValue(self, n, var) def DurationValue(self, n: "int", var: "IntervalVar") -> "int64_t": r""" This is a shortcut to get the DurationValue of 'var' in the nth solution.""" return _pywrapcp.SolutionCollector_DurationValue(self, n, var) def PerformedValue(self, n: "int", var: "IntervalVar") -> "int64_t": r""" This is a shortcut to get the PerformedValue of 'var' in the nth solution.""" return _pywrapcp.SolutionCollector_PerformedValue(self, n, var) def ForwardSequence(self, n: "int", var: "SequenceVar") -> "std::vector< int > const &": r""" This is a shortcut to get the ForwardSequence of 'var' in the nth solution. The forward sequence is the list of ranked interval variables starting from the start of the sequence. """ return _pywrapcp.SolutionCollector_ForwardSequence(self, n, var) def BackwardSequence(self, n: "int", var: "SequenceVar") -> "std::vector< int > const &": r""" This is a shortcut to get the BackwardSequence of 'var' in the nth solution. The backward sequence is the list of ranked interval variables starting from the end of the sequence. """ return _pywrapcp.SolutionCollector_BackwardSequence(self, n, var) def Unperformed(self, n: "int", var: "SequenceVar") -> "std::vector< int > const &": r""" This is a shortcut to get the list of unperformed of 'var' in the nth solution. """ return _pywrapcp.SolutionCollector_Unperformed(self, n, var)Ancestors
Methods
def Add(self, *args) ‑> void-
Expand source code
def Add(self, *args) -> "void": return _pywrapcp.SolutionCollector_Add(self, *args) def AddObjective(self, objective: IntVar) ‑> void-
Expand source code
def AddObjective(self, objective: "IntVar") -> "void": return _pywrapcp.SolutionCollector_AddObjective(self, objective) def BackwardSequence(self, n: int, var: SequenceVar) ‑> std::vector< int > const &-
This is a shortcut to get the BackwardSequence of 'var' in the nth solution. The backward sequence is the list of ranked interval variables starting from the end of the sequence.
Expand source code
def BackwardSequence(self, n: "int", var: "SequenceVar") -> "std::vector< int > const &": r""" This is a shortcut to get the BackwardSequence of 'var' in the nth solution. The backward sequence is the list of ranked interval variables starting from the end of the sequence. """ return _pywrapcp.SolutionCollector_BackwardSequence(self, n, var) def Branches(self, n: int) ‑> int64_t-
Returns the number of branches when the nth solution was found.
Expand source code
def Branches(self, n: "int") -> "int64_t": r""" Returns the number of branches when the nth solution was found.""" return _pywrapcp.SolutionCollector_Branches(self, n) def DebugString(self) ‑> std::string-
Expand source code
def DebugString(self) -> "std::string": return _pywrapcp.SolutionCollector_DebugString(self) def DurationValue(self, n: int, var: IntervalVar) ‑> int64_t-
This is a shortcut to get the DurationValue of 'var' in the nth solution.
Expand source code
def DurationValue(self, n: "int", var: "IntervalVar") -> "int64_t": r""" This is a shortcut to get the DurationValue of 'var' in the nth solution.""" return _pywrapcp.SolutionCollector_DurationValue(self, n, var) def EndValue(self, n: int, var: IntervalVar) ‑> int64_t-
This is a shortcut to get the EndValue of 'var' in the nth solution.
Expand source code
def EndValue(self, n: "int", var: "IntervalVar") -> "int64_t": r""" This is a shortcut to get the EndValue of 'var' in the nth solution.""" return _pywrapcp.SolutionCollector_EndValue(self, n, var) def Failures(self, n: int) ‑> int64_t-
Returns the number of failures encountered at the time of the nth solution.
Expand source code
def Failures(self, n: "int") -> "int64_t": r""" Returns the number of failures encountered at the time of the nth solution. """ return _pywrapcp.SolutionCollector_Failures(self, n) def ForwardSequence(self, n: int, var: SequenceVar) ‑> std::vector< int > const &-
This is a shortcut to get the ForwardSequence of 'var' in the nth solution. The forward sequence is the list of ranked interval variables starting from the start of the sequence.
Expand source code
def ForwardSequence(self, n: "int", var: "SequenceVar") -> "std::vector< int > const &": r""" This is a shortcut to get the ForwardSequence of 'var' in the nth solution. The forward sequence is the list of ranked interval variables starting from the start of the sequence. """ return _pywrapcp.SolutionCollector_ForwardSequence(self, n, var) def ObjectiveValue(self, n: int) ‑> int64_t-
Returns the objective value of the nth solution.
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def ObjectiveValue(self, n: "int") -> "int64_t": r""" Returns the objective value of the nth solution.""" return _pywrapcp.SolutionCollector_ObjectiveValue(self, n) def PerformedValue(self, n: int, var: IntervalVar) ‑> int64_t-
This is a shortcut to get the PerformedValue of 'var' in the nth solution.
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def PerformedValue(self, n: "int", var: "IntervalVar") -> "int64_t": r""" This is a shortcut to get the PerformedValue of 'var' in the nth solution.""" return _pywrapcp.SolutionCollector_PerformedValue(self, n, var) def Solution(self, n: int) ‑> operations_research::Assignment *-
Returns the nth solution.
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def Solution(self, n: "int") -> "operations_research::Assignment *": r""" Returns the nth solution.""" return _pywrapcp.SolutionCollector_Solution(self, n) def SolutionCount(self) ‑> int-
Returns how many solutions were stored during the search.
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def SolutionCount(self) -> "int": r""" Returns how many solutions were stored during the search.""" return _pywrapcp.SolutionCollector_SolutionCount(self) def StartValue(self, n: int, var: IntervalVar) ‑> int64_t-
This is a shortcut to get the StartValue of 'var' in the nth solution.
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def StartValue(self, n: "int", var: "IntervalVar") -> "int64_t": r""" This is a shortcut to get the StartValue of 'var' in the nth solution.""" return _pywrapcp.SolutionCollector_StartValue(self, n, var) def Unperformed(self, n: int, var: SequenceVar) ‑> std::vector< int > const &-
This is a shortcut to get the list of unperformed of 'var' in the nth solution.
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def Unperformed(self, n: "int", var: "SequenceVar") -> "std::vector< int > const &": r""" This is a shortcut to get the list of unperformed of 'var' in the nth solution. """ return _pywrapcp.SolutionCollector_Unperformed(self, n, var) def Value(self, n: int, var: IntVar) ‑> int64_t-
This is a shortcut to get the Value of 'var' in the nth solution.
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def Value(self, n: "int", var: "IntVar") -> "int64_t": r""" This is a shortcut to get the Value of 'var' in the nth solution.""" return _pywrapcp.SolutionCollector_Value(self, n, var) def WallTime(self, n: int) ‑> int64_t-
Returns the wall time in ms for the nth solution.
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def WallTime(self, n: "int") -> "int64_t": r""" Returns the wall time in ms for the nth solution.""" return _pywrapcp.SolutionCollector_WallTime(self, n)
Inherited members
class Solver (*args)-
Solver Class
A solver represents the main computation engine. It implements the entire range of Constraint Programming protocols: - Reversibility - Propagation - Search
Usually, Constraint Programming code consists of - the creation of the Solver, - the creation of the decision variables of the model, - the creation of the constraints of the model and their addition to the solver() through the AddConstraint() method, - the creation of the main DecisionBuilder class, - the launch of the solve() method with the decision builder.
For the time being, Solver is neither MT_SAFE nor MT_HOT.
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class Solver(object): r""" Solver Class A solver represents the main computation engine. It implements the entire range of Constraint Programming protocols: - Reversibility - Propagation - Search Usually, Constraint Programming code consists of - the creation of the Solver, - the creation of the decision variables of the model, - the creation of the constraints of the model and their addition to the solver() through the AddConstraint() method, - the creation of the main DecisionBuilder class, - the launch of the solve() method with the decision builder. For the time being, Solver is neither MT_SAFE nor MT_HOT. """ thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") __repr__ = _swig_repr INT_VAR_DEFAULT = _pywrapcp.Solver_INT_VAR_DEFAULT r""" The default behavior is CHOOSE_FIRST_UNBOUND.""" INT_VAR_SIMPLE = _pywrapcp.Solver_INT_VAR_SIMPLE r""" The simple selection is CHOOSE_FIRST_UNBOUND.""" CHOOSE_FIRST_UNBOUND = _pywrapcp.Solver_CHOOSE_FIRST_UNBOUND r""" Select the first unbound variable. Variables are considered in the order of the vector of IntVars used to create the selector. """ CHOOSE_RANDOM = _pywrapcp.Solver_CHOOSE_RANDOM r""" Randomly select one of the remaining unbound variables.""" CHOOSE_MIN_SIZE_LOWEST_MIN = _pywrapcp.Solver_CHOOSE_MIN_SIZE_LOWEST_MIN r""" Among unbound variables, select the variable with the smallest size, i.e., the smallest number of possible values. In case of a tie, the selected variables is the one with the lowest min value. In case of a tie, the first one is selected, first being defined by the order in the vector of IntVars used to create the selector. """ CHOOSE_MIN_SIZE_HIGHEST_MIN = _pywrapcp.Solver_CHOOSE_MIN_SIZE_HIGHEST_MIN r""" Among unbound variables, select the variable with the smallest size, i.e., the smallest number of possible values. In case of a tie, the selected variable is the one with the highest min value. In case of a tie, the first one is selected, first being defined by the order in the vector of IntVars used to create the selector. """ CHOOSE_MIN_SIZE_LOWEST_MAX = _pywrapcp.Solver_CHOOSE_MIN_SIZE_LOWEST_MAX r""" Among unbound variables, select the variable with the smallest size, i.e., the smallest number of possible values. In case of a tie, the selected variables is the one with the lowest max value. In case of a tie, the first one is selected, first being defined by the order in the vector of IntVars used to create the selector. """ CHOOSE_MIN_SIZE_HIGHEST_MAX = _pywrapcp.Solver_CHOOSE_MIN_SIZE_HIGHEST_MAX r""" Among unbound variables, select the variable with the smallest size, i.e., the smallest number of possible values. In case of a tie, the selected variable is the one with the highest max value. In case of a tie, the first one is selected, first being defined by the order in the vector of IntVars used to create the selector. """ CHOOSE_LOWEST_MIN = _pywrapcp.Solver_CHOOSE_LOWEST_MIN r""" Among unbound variables, select the variable with the smallest minimal value. In case of a tie, the first one is selected, "first" defined by the order in the vector of IntVars used to create the selector. """ CHOOSE_HIGHEST_MAX = _pywrapcp.Solver_CHOOSE_HIGHEST_MAX r""" Among unbound variables, select the variable with the highest maximal value. In case of a tie, the first one is selected, first being defined by the order in the vector of IntVars used to create the selector. """ CHOOSE_MIN_SIZE = _pywrapcp.Solver_CHOOSE_MIN_SIZE r""" Among unbound variables, select the variable with the smallest size. In case of a tie, the first one is selected, first being defined by the order in the vector of IntVars used to create the selector. """ CHOOSE_MAX_SIZE = _pywrapcp.Solver_CHOOSE_MAX_SIZE r""" Among unbound variables, select the variable with the highest size. In case of a tie, the first one is selected, first being defined by the order in the vector of IntVars used to create the selector. """ CHOOSE_MAX_REGRET_ON_MIN = _pywrapcp.Solver_CHOOSE_MAX_REGRET_ON_MIN r""" Among unbound variables, select the variable with the largest gap between the first and the second values of the domain. """ CHOOSE_PATH = _pywrapcp.Solver_CHOOSE_PATH r""" Selects the next unbound variable on a path, the path being defined by the variables: var[i] corresponds to the index of the next of i. """ INT_VALUE_DEFAULT = _pywrapcp.Solver_INT_VALUE_DEFAULT r""" The default behavior is ASSIGN_MIN_VALUE.""" INT_VALUE_SIMPLE = _pywrapcp.Solver_INT_VALUE_SIMPLE r""" The simple selection is ASSIGN_MIN_VALUE.""" ASSIGN_MIN_VALUE = _pywrapcp.Solver_ASSIGN_MIN_VALUE r""" Selects the min value of the selected variable.""" ASSIGN_MAX_VALUE = _pywrapcp.Solver_ASSIGN_MAX_VALUE r""" Selects the max value of the selected variable.""" ASSIGN_RANDOM_VALUE = _pywrapcp.Solver_ASSIGN_RANDOM_VALUE r""" Selects randomly one of the possible values of the selected variable.""" ASSIGN_CENTER_VALUE = _pywrapcp.Solver_ASSIGN_CENTER_VALUE r""" Selects the first possible value which is the closest to the center of the domain of the selected variable. The center is defined as (min + max) / 2. """ SPLIT_LOWER_HALF = _pywrapcp.Solver_SPLIT_LOWER_HALF r""" Split the domain in two around the center, and choose the lower part first. """ SPLIT_UPPER_HALF = _pywrapcp.Solver_SPLIT_UPPER_HALF r""" Split the domain in two around the center, and choose the lower part first. """ SEQUENCE_DEFAULT = _pywrapcp.Solver_SEQUENCE_DEFAULT SEQUENCE_SIMPLE = _pywrapcp.Solver_SEQUENCE_SIMPLE CHOOSE_MIN_SLACK_RANK_FORWARD = _pywrapcp.Solver_CHOOSE_MIN_SLACK_RANK_FORWARD CHOOSE_RANDOM_RANK_FORWARD = _pywrapcp.Solver_CHOOSE_RANDOM_RANK_FORWARD INTERVAL_DEFAULT = _pywrapcp.Solver_INTERVAL_DEFAULT r""" The default is INTERVAL_SET_TIMES_FORWARD.""" INTERVAL_SIMPLE = _pywrapcp.Solver_INTERVAL_SIMPLE r""" The simple is INTERVAL_SET_TIMES_FORWARD.""" INTERVAL_SET_TIMES_FORWARD = _pywrapcp.Solver_INTERVAL_SET_TIMES_FORWARD r""" Selects the variable with the lowest starting time of all variables, and fixes its starting time to this lowest value. """ INTERVAL_SET_TIMES_BACKWARD = _pywrapcp.Solver_INTERVAL_SET_TIMES_BACKWARD r""" Selects the variable with the highest ending time of all variables, and fixes the ending time to this highest values. """ TWOOPT = _pywrapcp.Solver_TWOOPT r""" Operator which reverses a sub-chain of a path. It is called TwoOpt because it breaks two arcs on the path; resulting paths are called two-optimal. Possible neighbors for the path 1 -> 2 -> 3 -> 4 -> 5 (where (1, 5) are first and last nodes of the path and can therefore not be moved): 1 -> [3 -> 2] -> 4 -> 5 1 -> [4 -> 3 -> 2] -> 5 1 -> 2 -> [4 -> 3] -> 5 """ OROPT = _pywrapcp.Solver_OROPT r""" Relocate: OROPT and RELOCATE. Operator which moves a sub-chain of a path to another position; the specified chain length is the fixed length of the chains being moved. When this length is 1, the operator simply moves a node to another position. Possible neighbors for the path 1 -> 2 -> 3 -> 4 -> 5, for a chain length of 2 (where (1, 5) are first and last nodes of the path and can therefore not be moved): 1 -> 4 -> [2 -> 3] -> 5 1 -> [3 -> 4] -> 2 -> 5 Using Relocate with chain lengths of 1, 2 and 3 together is equivalent to the OrOpt operator on a path. The OrOpt operator is a limited version of 3Opt (breaks 3 arcs on a path). """ RELOCATE = _pywrapcp.Solver_RELOCATE r""" Relocate neighborhood with length of 1 (see OROPT comment).""" EXCHANGE = _pywrapcp.Solver_EXCHANGE r""" Operator which exchanges the positions of two nodes. Possible neighbors for the path 1 -> 2 -> 3 -> 4 -> 5 (where (1, 5) are first and last nodes of the path and can therefore not be moved): 1 -> [3] -> [2] -> 4 -> 5 1 -> [4] -> 3 -> [2] -> 5 1 -> 2 -> [4] -> [3] -> 5 """ CROSS = _pywrapcp.Solver_CROSS r""" Operator which cross exchanges the starting chains of 2 paths, including exchanging the whole paths. First and last nodes are not moved. Possible neighbors for the paths 1 -> 2 -> 3 -> 4 -> 5 and 6 -> 7 -> 8 (where (1, 5) and (6, 8) are first and last nodes of the paths and can therefore not be moved): 1 -> [7] -> 3 -> 4 -> 5 6 -> [2] -> 8 1 -> [7] -> 4 -> 5 6 -> [2 -> 3] -> 8 1 -> [7] -> 5 6 -> [2 -> 3 -> 4] -> 8 """ MAKEACTIVE = _pywrapcp.Solver_MAKEACTIVE r""" Operator which inserts an inactive node into a path. Possible neighbors for the path 1 -> 2 -> 3 -> 4 with 5 inactive (where 1 and 4 are first and last nodes of the path) are: 1 -> [5] -> 2 -> 3 -> 4 1 -> 2 -> [5] -> 3 -> 4 1 -> 2 -> 3 -> [5] -> 4 """ MAKEINACTIVE = _pywrapcp.Solver_MAKEINACTIVE r""" Operator which makes path nodes inactive. Possible neighbors for the path 1 -> 2 -> 3 -> 4 (where 1 and 4 are first and last nodes of the path) are: 1 -> 3 -> 4 with 2 inactive 1 -> 2 -> 4 with 3 inactive """ MAKECHAININACTIVE = _pywrapcp.Solver_MAKECHAININACTIVE r""" Operator which makes a "chain" of path nodes inactive. Possible neighbors for the path 1 -> 2 -> 3 -> 4 (where 1 and 4 are first and last nodes of the path) are: 1 -> 3 -> 4 with 2 inactive 1 -> 2 -> 4 with 3 inactive 1 -> 4 with 2 and 3 inactive """ SWAPACTIVE = _pywrapcp.Solver_SWAPACTIVE r""" Operator which replaces an active node by an inactive one. Possible neighbors for the path 1 -> 2 -> 3 -> 4 with 5 inactive (where 1 and 4 are first and last nodes of the path) are: 1 -> [5] -> 3 -> 4 with 2 inactive 1 -> 2 -> [5] -> 4 with 3 inactive """ EXTENDEDSWAPACTIVE = _pywrapcp.Solver_EXTENDEDSWAPACTIVE r""" Operator which makes an inactive node active and an active one inactive. It is similar to SwapActiveOperator except that it tries to insert the inactive node in all possible positions instead of just the position of the node made inactive. Possible neighbors for the path 1 -> 2 -> 3 -> 4 with 5 inactive (where 1 and 4 are first and last nodes of the path) are: 1 -> [5] -> 3 -> 4 with 2 inactive 1 -> 3 -> [5] -> 4 with 2 inactive 1 -> [5] -> 2 -> 4 with 3 inactive 1 -> 2 -> [5] -> 4 with 3 inactive """ PATHLNS = _pywrapcp.Solver_PATHLNS r""" Operator which relaxes two sub-chains of three consecutive arcs each. Each sub-chain is defined by a start node and the next three arcs. Those six arcs are relaxed to build a new neighbor. PATHLNS explores all possible pairs of starting nodes and so defines n^2 neighbors, n being the number of nodes. Note that the two sub-chains can be part of the same path; they even may overlap. """ FULLPATHLNS = _pywrapcp.Solver_FULLPATHLNS r""" Operator which relaxes one entire path and all inactive nodes, thus defining num_paths neighbors. """ UNACTIVELNS = _pywrapcp.Solver_UNACTIVELNS r""" Operator which relaxes all inactive nodes and one sub-chain of six consecutive arcs. That way the path can be improved by inserting inactive nodes or swapping arcs. """ INCREMENT = _pywrapcp.Solver_INCREMENT r""" Operator which defines one neighbor per variable. Each neighbor tries to increment by one the value of the corresponding variable. When a new solution is found the neighborhood is rebuilt from scratch, i.e., tries to increment values in the variable order. Consider for instance variables x and y. x is incremented one by one to its max, and when it is not possible to increment x anymore, y is incremented once. If this is a solution, then next neighbor tries to increment x. """ DECREMENT = _pywrapcp.Solver_DECREMENT r""" Operator which defines a neighborhood to decrement values. The behavior is the same as INCREMENT, except values are decremented instead of incremented. """ SIMPLELNS = _pywrapcp.Solver_SIMPLELNS r""" Operator which defines one neighbor per variable. Each neighbor relaxes one variable. When a new solution is found the neighborhood is rebuilt from scratch. Consider for instance variables x and y. First x is relaxed and the solver is looking for the best possible solution (with only x relaxed). Then y is relaxed, and the solver is looking for a new solution. If a new solution is found, then the next variable to be relaxed is x. """ GE = _pywrapcp.Solver_GE r""" Move is accepted when the current objective value >= objective.Min.""" LE = _pywrapcp.Solver_LE r""" Move is accepted when the current objective value <= objective.Max.""" EQ = _pywrapcp.Solver_EQ r""" Move is accepted when the current objective value is in the interval objective.Min .. objective.Max. """ DELAYED_PRIORITY = _pywrapcp.Solver_DELAYED_PRIORITY r""" DELAYED_PRIORITY is the lowest priority: Demons will be processed after VAR_PRIORITY and NORMAL_PRIORITY demons. """ VAR_PRIORITY = _pywrapcp.Solver_VAR_PRIORITY r""" VAR_PRIORITY is between DELAYED_PRIORITY and NORMAL_PRIORITY.""" NORMAL_PRIORITY = _pywrapcp.Solver_NORMAL_PRIORITY r""" NORMAL_PRIORITY is the highest priority: Demons will be processed first.""" def __init__(self, *args): _pywrapcp.Solver_swiginit(self, _pywrapcp.new_Solver(*args)) self.__python_constraints = [] __swig_destroy__ = _pywrapcp.delete_Solver def Parameters(self) -> "operations_research::ConstraintSolverParameters": r""" Stored Parameters.""" return _pywrapcp.Solver_Parameters(self) @staticmethod def DefaultSolverParameters() -> "operations_research::ConstraintSolverParameters": r""" Create a ConstraintSolverParameters proto with all the default values.""" return _pywrapcp.Solver_DefaultSolverParameters() def AddConstraint(self, c: "Constraint") -> "void": r""" Adds the constraint 'c' to the model. After calling this method, and until there is a backtrack that undoes the addition, any assignment of variables to values must satisfy the given constraint in order to be considered feasible. There are two fairly different use cases: - the most common use case is modeling: the given constraint is really part of the problem that the user is trying to solve. In this use case, AddConstraint is called outside of search (i.e., with state() == OUTSIDE_SEARCH). Most users should only use AddConstraint in this way. In this case, the constraint will belong to the model forever: it cannot not be removed by backtracking. - a rarer use case is that 'c' is not a real constraint of the model. It may be a constraint generated by a branching decision (a constraint whose goal is to restrict the search space), a symmetry breaking constraint (a constraint that does restrict the search space, but in a way that cannot have an impact on the quality of the solutions in the subtree), or an inferred constraint that, while having no semantic value to the model (it does not restrict the set of solutions), is worth having because we believe it may strengthen the propagation. In these cases, it happens that the constraint is added during the search (i.e., with state() == IN_SEARCH or state() == IN_ROOT_NODE). When a constraint is added during a search, it applies only to the subtree of the search tree rooted at the current node, and will be automatically removed by backtracking. This method does not take ownership of the constraint. If the constraint has been created by any factory method (Solver::MakeXXX), it will automatically be deleted. However, power users who implement their own constraints should do: solver.AddConstraint(solver.RevAlloc(new MyConstraint(...)); """ return _pywrapcp.Solver_AddConstraint(self, c) def Solve(self, *args) -> "bool": return _pywrapcp.Solver_Solve(self, *args) def NewSearch(self, *args) -> "void": return _pywrapcp.Solver_NewSearch(self, *args) def NextSolution(self) -> "bool": return _pywrapcp.Solver_NextSolution(self) def RestartSearch(self) -> "void": return _pywrapcp.Solver_RestartSearch(self) def EndSearch(self) -> "void": return _pywrapcp.Solver_EndSearch(self) def SolveAndCommit(self, *args) -> "bool": return _pywrapcp.Solver_SolveAndCommit(self, *args) def CheckAssignment(self, solution: "Assignment") -> "bool": r""" Checks whether the given assignment satisfies all relevant constraints.""" return _pywrapcp.Solver_CheckAssignment(self, solution) def CheckConstraint(self, ct: "Constraint") -> "bool": r""" Checks whether adding this constraint will lead to an immediate failure. It will return false if the model is already inconsistent, or if adding the constraint makes it inconsistent. """ return _pywrapcp.Solver_CheckConstraint(self, ct) def Fail(self) -> "void": r""" Abandon the current branch in the search tree. A backtrack will follow.""" return _pywrapcp.Solver_Fail(self) @staticmethod def MemoryUsage() -> "int64_t": r""" Current memory usage in bytes""" return _pywrapcp.Solver_MemoryUsage() def WallTime(self) -> "int64_t": r""" DEPRECATED: Use Now() instead. Time elapsed, in ms since the creation of the solver. """ return _pywrapcp.Solver_WallTime(self) def Branches(self) -> "int64_t": r""" The number of branches explored since the creation of the solver.""" return _pywrapcp.Solver_Branches(self) def Solutions(self) -> "int64_t": r""" The number of solutions found since the start of the search.""" return _pywrapcp.Solver_Solutions(self) def Failures(self) -> "int64_t": r""" The number of failures encountered since the creation of the solver.""" return _pywrapcp.Solver_Failures(self) def AcceptedNeighbors(self) -> "int64_t": r""" The number of accepted neighbors.""" return _pywrapcp.Solver_AcceptedNeighbors(self) def Stamp(self) -> "uint64_t": r""" The stamp indicates how many moves in the search tree we have performed. It is useful to detect if we need to update same lazy structures. """ return _pywrapcp.Solver_Stamp(self) def FailStamp(self) -> "uint64_t": r""" The fail_stamp() is incremented after each backtrack.""" return _pywrapcp.Solver_FailStamp(self) def IntVar(self, *args) -> "operations_research::IntVar *": r""" *Overload 1:* MakeIntVar will create the best range based int var for the bounds given. | *Overload 2:* MakeIntVar will create a variable with the given sparse domain. | *Overload 3:* MakeIntVar will create a variable with the given sparse domain. | *Overload 4:* MakeIntVar will create the best range based int var for the bounds given. | *Overload 5:* MakeIntVar will create a variable with the given sparse domain. | *Overload 6:* MakeIntVar will create a variable with the given sparse domain. """ return _pywrapcp.Solver_IntVar(self, *args) def BoolVar(self, *args) -> "operations_research::IntVar *": r""" *Overload 1:* MakeBoolVar will create a variable with a {0, 1} domain. | *Overload 2:* MakeBoolVar will create a variable with a {0, 1} domain. """ return _pywrapcp.Solver_BoolVar(self, *args) def IntConst(self, *args) -> "operations_research::IntVar *": r""" *Overload 1:* IntConst will create a constant expression. | *Overload 2:* IntConst will create a constant expression. """ return _pywrapcp.Solver_IntConst(self, *args) def Sum(self, vars: "std::vector< operations_research::IntVar * > const &") -> "operations_research::IntExpr *": r""" sum of all vars.""" return _pywrapcp.Solver_Sum(self, vars) def ScalProd(self, *args) -> "operations_research::IntExpr *": r""" *Overload 1:* scalar product | *Overload 2:* scalar product """ return _pywrapcp.Solver_ScalProd(self, *args) def MonotonicElement(self, values: "operations_research::Solver::IndexEvaluator1", increasing: "bool", index: "IntVar") -> "operations_research::IntExpr *": r""" Function based element. The constraint takes ownership of the callback. The callback must be monotonic. It must be able to cope with any possible value in the domain of 'index' (potentially negative ones too). Furtermore, monotonicity is not checked. Thus giving a non-monotonic function, or specifying an incorrect increasing parameter will result in undefined behavior. """ return _pywrapcp.Solver_MonotonicElement(self, values, increasing, index) def Element(self, *args) -> "operations_research::IntExpr *": r""" *Overload 1:* values[index] | *Overload 2:* values[index] | *Overload 3:* Function-based element. The constraint takes ownership of the callback. The callback must be able to cope with any possible value in the domain of 'index' (potentially negative ones too). | *Overload 4:* 2D version of function-based element expression, values(expr1, expr2). | *Overload 5:* vars[expr] """ return _pywrapcp.Solver_Element(self, *args) def IndexExpression(self, vars: "std::vector< operations_research::IntVar * > const &", value: "int64_t") -> "operations_research::IntExpr *": r""" Returns the expression expr such that vars[expr] == value. It assumes that vars are all different. """ return _pywrapcp.Solver_IndexExpression(self, vars, value) def Min(self, *args) -> "operations_research::IntExpr *": r""" *Overload 1:* std::min(vars) | *Overload 2:* std::min (left, right) | *Overload 3:* std::min(expr, value) | *Overload 4:* std::min(expr, value) """ return _pywrapcp.Solver_Min(self, *args) def Max(self, *args) -> "operations_research::IntExpr *": r""" *Overload 1:* std::max(vars) | *Overload 2:* std::max(left, right) | *Overload 3:* std::max(expr, value) | *Overload 4:* std::max(expr, value) """ return _pywrapcp.Solver_Max(self, *args) def ConvexPiecewiseExpr(self, expr: "IntExpr", early_cost: "int64_t", early_date: "int64_t", late_date: "int64_t", late_cost: "int64_t") -> "operations_research::IntExpr *": r""" Convex piecewise function.""" return _pywrapcp.Solver_ConvexPiecewiseExpr(self, expr, early_cost, early_date, late_date, late_cost) def SemiContinuousExpr(self, expr: "IntExpr", fixed_charge: "int64_t", step: "int64_t") -> "operations_research::IntExpr *": r""" Semi continuous Expression (x <= 0 -> f(x) = 0; x > 0 -> f(x) = ax + b) a >= 0 and b >= 0 """ return _pywrapcp.Solver_SemiContinuousExpr(self, expr, fixed_charge, step) def ConditionalExpression(self, condition: "IntVar", expr: "IntExpr", unperformed_value: "int64_t") -> "operations_research::IntExpr *": r""" Conditional Expr condition ? expr : unperformed_value""" return _pywrapcp.Solver_ConditionalExpression(self, condition, expr, unperformed_value) def TrueConstraint(self) -> "operations_research::Constraint *": r""" This constraint always succeeds.""" return _pywrapcp.Solver_TrueConstraint(self) def FalseConstraint(self, *args) -> "operations_research::Constraint *": return _pywrapcp.Solver_FalseConstraint(self, *args) def IsEqualCstCt(self, var: "IntExpr", value: "int64_t", boolvar: "IntVar") -> "operations_research::Constraint *": r""" boolvar == (var == value)""" return _pywrapcp.Solver_IsEqualCstCt(self, var, value, boolvar) def IsEqualCstVar(self, var: "IntExpr", value: "int64_t") -> "operations_research::IntVar *": r""" status var of (var == value)""" return _pywrapcp.Solver_IsEqualCstVar(self, var, value) def IsEqualCt(self, v1: "IntExpr", v2: "IntExpr", b: "IntVar") -> "operations_research::Constraint *": r""" b == (v1 == v2)""" return _pywrapcp.Solver_IsEqualCt(self, v1, v2, b) def IsEqualVar(self, v1: "IntExpr", v2: "IntExpr") -> "operations_research::IntVar *": r""" status var of (v1 == v2)""" return _pywrapcp.Solver_IsEqualVar(self, v1, v2) def IsDifferentCstCt(self, var: "IntExpr", value: "int64_t", boolvar: "IntVar") -> "operations_research::Constraint *": r""" boolvar == (var != value)""" return _pywrapcp.Solver_IsDifferentCstCt(self, var, value, boolvar) def IsDifferentCstVar(self, var: "IntExpr", value: "int64_t") -> "operations_research::IntVar *": r""" status var of (var != value)""" return _pywrapcp.Solver_IsDifferentCstVar(self, var, value) def IsDifferentVar(self, v1: "IntExpr", v2: "IntExpr") -> "operations_research::IntVar *": r""" status var of (v1 != v2)""" return _pywrapcp.Solver_IsDifferentVar(self, v1, v2) def IsDifferentCt(self, v1: "IntExpr", v2: "IntExpr", b: "IntVar") -> "operations_research::Constraint *": r""" b == (v1 != v2)""" return _pywrapcp.Solver_IsDifferentCt(self, v1, v2, b) def IsLessOrEqualCstCt(self, var: "IntExpr", value: "int64_t", boolvar: "IntVar") -> "operations_research::Constraint *": r""" boolvar == (var <= value)""" return _pywrapcp.Solver_IsLessOrEqualCstCt(self, var, value, boolvar) def IsLessOrEqualCstVar(self, var: "IntExpr", value: "int64_t") -> "operations_research::IntVar *": r""" status var of (var <= value)""" return _pywrapcp.Solver_IsLessOrEqualCstVar(self, var, value) def IsLessOrEqualVar(self, left: "IntExpr", right: "IntExpr") -> "operations_research::IntVar *": r""" status var of (left <= right)""" return _pywrapcp.Solver_IsLessOrEqualVar(self, left, right) def IsLessOrEqualCt(self, left: "IntExpr", right: "IntExpr", b: "IntVar") -> "operations_research::Constraint *": r""" b == (left <= right)""" return _pywrapcp.Solver_IsLessOrEqualCt(self, left, right, b) def IsGreaterOrEqualCstCt(self, var: "IntExpr", value: "int64_t", boolvar: "IntVar") -> "operations_research::Constraint *": r""" boolvar == (var >= value)""" return _pywrapcp.Solver_IsGreaterOrEqualCstCt(self, var, value, boolvar) def IsGreaterOrEqualCstVar(self, var: "IntExpr", value: "int64_t") -> "operations_research::IntVar *": r""" status var of (var >= value)""" return _pywrapcp.Solver_IsGreaterOrEqualCstVar(self, var, value) def IsGreaterOrEqualVar(self, left: "IntExpr", right: "IntExpr") -> "operations_research::IntVar *": r""" status var of (left >= right)""" return _pywrapcp.Solver_IsGreaterOrEqualVar(self, left, right) def IsGreaterOrEqualCt(self, left: "IntExpr", right: "IntExpr", b: "IntVar") -> "operations_research::Constraint *": r""" b == (left >= right)""" return _pywrapcp.Solver_IsGreaterOrEqualCt(self, left, right, b) def IsGreaterCstCt(self, v: "IntExpr", c: "int64_t", b: "IntVar") -> "operations_research::Constraint *": r""" b == (v > c)""" return _pywrapcp.Solver_IsGreaterCstCt(self, v, c, b) def IsGreaterCstVar(self, var: "IntExpr", value: "int64_t") -> "operations_research::IntVar *": r""" status var of (var > value)""" return _pywrapcp.Solver_IsGreaterCstVar(self, var, value) def IsGreaterVar(self, left: "IntExpr", right: "IntExpr") -> "operations_research::IntVar *": r""" status var of (left > right)""" return _pywrapcp.Solver_IsGreaterVar(self, left, right) def IsGreaterCt(self, left: "IntExpr", right: "IntExpr", b: "IntVar") -> "operations_research::Constraint *": r""" b == (left > right)""" return _pywrapcp.Solver_IsGreaterCt(self, left, right, b) def IsLessCstCt(self, v: "IntExpr", c: "int64_t", b: "IntVar") -> "operations_research::Constraint *": r""" b == (v < c)""" return _pywrapcp.Solver_IsLessCstCt(self, v, c, b) def IsLessCstVar(self, var: "IntExpr", value: "int64_t") -> "operations_research::IntVar *": r""" status var of (var < value)""" return _pywrapcp.Solver_IsLessCstVar(self, var, value) def IsLessVar(self, left: "IntExpr", right: "IntExpr") -> "operations_research::IntVar *": r""" status var of (left < right)""" return _pywrapcp.Solver_IsLessVar(self, left, right) def IsLessCt(self, left: "IntExpr", right: "IntExpr", b: "IntVar") -> "operations_research::Constraint *": r""" b == (left < right)""" return _pywrapcp.Solver_IsLessCt(self, left, right, b) def SumLessOrEqual(self, vars: "std::vector< operations_research::IntVar * > const &", cst: "int64_t") -> "operations_research::Constraint *": r""" Variation on arrays.""" return _pywrapcp.Solver_SumLessOrEqual(self, vars, cst) def SumGreaterOrEqual(self, vars: "std::vector< operations_research::IntVar * > const &", cst: "int64_t") -> "operations_research::Constraint *": return _pywrapcp.Solver_SumGreaterOrEqual(self, vars, cst) def SumEquality(self, *args) -> "operations_research::Constraint *": return _pywrapcp.Solver_SumEquality(self, *args) def ScalProdEquality(self, *args) -> "operations_research::Constraint *": return _pywrapcp.Solver_ScalProdEquality(self, *args) def ScalProdGreaterOrEqual(self, *args) -> "operations_research::Constraint *": return _pywrapcp.Solver_ScalProdGreaterOrEqual(self, *args) def ScalProdLessOrEqual(self, *args) -> "operations_research::Constraint *": return _pywrapcp.Solver_ScalProdLessOrEqual(self, *args) def MinEquality(self, vars: "std::vector< operations_research::IntVar * > const &", min_var: "IntVar") -> "operations_research::Constraint *": return _pywrapcp.Solver_MinEquality(self, vars, min_var) def MaxEquality(self, vars: "std::vector< operations_research::IntVar * > const &", max_var: "IntVar") -> "operations_research::Constraint *": return _pywrapcp.Solver_MaxEquality(self, vars, max_var) def ElementEquality(self, *args) -> "operations_research::Constraint *": return _pywrapcp.Solver_ElementEquality(self, *args) def AbsEquality(self, var: "IntVar", abs_var: "IntVar") -> "operations_research::Constraint *": r""" Creates the constraint abs(var) == abs_var.""" return _pywrapcp.Solver_AbsEquality(self, var, abs_var) def IndexOfConstraint(self, vars: "std::vector< operations_research::IntVar * > const &", index: "IntVar", target: "int64_t") -> "operations_research::Constraint *": r""" This constraint is a special case of the element constraint with an array of integer variables, where the variables are all different and the index variable is constrained such that vars[index] == target. """ return _pywrapcp.Solver_IndexOfConstraint(self, vars, index, target) def ConstraintInitialPropagateCallback(self, ct: "Constraint") -> "operations_research::Demon *": r""" This method is a specialized case of the MakeConstraintDemon method to call the InitiatePropagate of the constraint 'ct'. """ return _pywrapcp.Solver_ConstraintInitialPropagateCallback(self, ct) def DelayedConstraintInitialPropagateCallback(self, ct: "Constraint") -> "operations_research::Demon *": r""" This method is a specialized case of the MakeConstraintDemon method to call the InitiatePropagate of the constraint 'ct' with low priority. """ return _pywrapcp.Solver_DelayedConstraintInitialPropagateCallback(self, ct) def ClosureDemon(self, closure: "operations_research::Solver::Closure") -> "operations_research::Demon *": r""" Creates a demon from a closure.""" return _pywrapcp.Solver_ClosureDemon(self, closure) def BetweenCt(self, expr: "IntExpr", l: "int64_t", u: "int64_t") -> "operations_research::Constraint *": r""" (l <= expr <= u)""" return _pywrapcp.Solver_BetweenCt(self, expr, l, u) def IsBetweenCt(self, expr: "IntExpr", l: "int64_t", u: "int64_t", b: "IntVar") -> "operations_research::Constraint *": r""" b == (l <= expr <= u)""" return _pywrapcp.Solver_IsBetweenCt(self, expr, l, u, b) def IsBetweenVar(self, v: "IntExpr", l: "int64_t", u: "int64_t") -> "operations_research::IntVar *": return _pywrapcp.Solver_IsBetweenVar(self, v, l, u) def MemberCt(self, *args) -> "operations_research::Constraint *": return _pywrapcp.Solver_MemberCt(self, *args) def NotMemberCt(self, *args) -> "operations_research::Constraint *": r""" *Overload 1:* expr not in set. | *Overload 2:* expr should not be in the list of forbidden intervals [start[i]..end[i]]. | *Overload 3:* expr should not be in the list of forbidden intervals [start[i]..end[i]]. """ return _pywrapcp.Solver_NotMemberCt(self, *args) def IsMemberCt(self, *args) -> "operations_research::Constraint *": return _pywrapcp.Solver_IsMemberCt(self, *args) def IsMemberVar(self, *args) -> "operations_research::IntVar *": return _pywrapcp.Solver_IsMemberVar(self, *args) def Count(self, *args) -> "operations_research::Constraint *": r""" *Overload 1:* |{i | vars[i] == value}| == max_count | *Overload 2:* |{i | vars[i] == value}| == max_count """ return _pywrapcp.Solver_Count(self, *args) def Distribute(self, *args) -> "operations_research::Constraint *": r""" *Overload 1:* Aggregated version of count: |{i | v[i] == values[j]}| == cards[j] | *Overload 2:* Aggregated version of count: |{i | v[i] == values[j]}| == cards[j] | *Overload 3:* Aggregated version of count: |{i | v[i] == j}| == cards[j] | *Overload 4:* Aggregated version of count with bounded cardinalities: forall j in 0 .. card_size - 1: card_min <= |{i | v[i] == j}| <= card_max | *Overload 5:* Aggregated version of count with bounded cardinalities: forall j in 0 .. card_size - 1: card_min[j] <= |{i | v[i] == j}| <= card_max[j] | *Overload 6:* Aggregated version of count with bounded cardinalities: forall j in 0 .. card_size - 1: card_min[j] <= |{i | v[i] == j}| <= card_max[j] | *Overload 7:* Aggregated version of count with bounded cardinalities: forall j in 0 .. card_size - 1: card_min[j] <= |{i | v[i] == values[j]}| <= card_max[j] | *Overload 8:* Aggregated version of count with bounded cardinalities: forall j in 0 .. card_size - 1: card_min[j] <= |{i | v[i] == values[j]}| <= card_max[j] """ return _pywrapcp.Solver_Distribute(self, *args) def Deviation(self, vars: "std::vector< operations_research::IntVar * > const &", deviation_var: "IntVar", total_sum: "int64_t") -> "operations_research::Constraint *": r""" Deviation constraint: sum_i |n * vars[i] - total_sum| <= deviation_var and sum_i vars[i] == total_sum n = #vars """ return _pywrapcp.Solver_Deviation(self, vars, deviation_var, total_sum) def AllDifferent(self, *args) -> "operations_research::Constraint *": r""" *Overload 1:* All variables are pairwise different. This corresponds to the stronger version of the propagation algorithm. | *Overload 2:* All variables are pairwise different. If 'stronger_propagation' is true, stronger, and potentially slower propagation will occur. This API will be deprecated in the future. """ return _pywrapcp.Solver_AllDifferent(self, *args) def AllDifferentExcept(self, vars: "std::vector< operations_research::IntVar * > const &", escape_value: "int64_t") -> "operations_research::Constraint *": r""" All variables are pairwise different, unless they are assigned to the escape value. """ return _pywrapcp.Solver_AllDifferentExcept(self, vars, escape_value) def SortingConstraint(self, vars: "std::vector< operations_research::IntVar * > const &", sorted: "std::vector< operations_research::IntVar * > const &") -> "operations_research::Constraint *": r""" Creates a constraint binding the arrays of variables "vars" and "sorted_vars": sorted_vars[0] must be equal to the minimum of all variables in vars, and so on: the value of sorted_vars[i] must be equal to the i-th value of variables invars. This constraint propagates in both directions: from "vars" to "sorted_vars" and vice-versa. Behind the scenes, this constraint maintains that: - sorted is always increasing. - whatever the values of vars, there exists a permutation that injects its values into the sorted variables. For more info, please have a look at: https://mpi-inf.mpg.de/~mehlhorn/ftp/Mehlhorn-Thiel.pdf """ return _pywrapcp.Solver_SortingConstraint(self, vars, sorted) def LexicalLess(self, left: "std::vector< operations_research::IntVar * > const &", right: "std::vector< operations_research::IntVar * > const &") -> "operations_research::Constraint *": r""" Creates a constraint that enforces that left is lexicographically less than right. """ return _pywrapcp.Solver_LexicalLess(self, left, right) def LexicalLessOrEqual(self, left: "std::vector< operations_research::IntVar * > const &", right: "std::vector< operations_research::IntVar * > const &") -> "operations_research::Constraint *": r""" Creates a constraint that enforces that left is lexicographically less than or equal to right. """ return _pywrapcp.Solver_LexicalLessOrEqual(self, left, right) def InversePermutationConstraint(self, left: "std::vector< operations_research::IntVar * > const &", right: "std::vector< operations_research::IntVar * > const &") -> "operations_research::Constraint *": r""" Creates a constraint that enforces that 'left' and 'right' both represent permutations of [0..left.size()-1], and that 'right' is the inverse permutation of 'left', i.e. for all i in [0..left.size()-1], right[left[i]] = i. """ return _pywrapcp.Solver_InversePermutationConstraint(self, left, right) def NullIntersect(self, first_vars: "std::vector< operations_research::IntVar * > const &", second_vars: "std::vector< operations_research::IntVar * > const &") -> "operations_research::Constraint *": r""" Creates a constraint that states that all variables in the first vector are different from all variables in the second group. Thus the set of values in the first vector does not intersect with the set of values in the second vector. """ return _pywrapcp.Solver_NullIntersect(self, first_vars, second_vars) def NullIntersectExcept(self, first_vars: "std::vector< operations_research::IntVar * > const &", second_vars: "std::vector< operations_research::IntVar * > const &", escape_value: "int64_t") -> "operations_research::Constraint *": r""" Creates a constraint that states that all variables in the first vector are different from all variables from the second group, unless they are assigned to the escape value. Thus the set of values in the first vector minus the escape value does not intersect with the set of values in the second vector. """ return _pywrapcp.Solver_NullIntersectExcept(self, first_vars, second_vars, escape_value) def Circuit(self, nexts: "std::vector< operations_research::IntVar * > const &") -> "operations_research::Constraint *": r""" Force the "nexts" variable to create a complete Hamiltonian path.""" return _pywrapcp.Solver_Circuit(self, nexts) def SubCircuit(self, nexts: "std::vector< operations_research::IntVar * > const &") -> "operations_research::Constraint *": r""" Force the "nexts" variable to create a complete Hamiltonian path for those that do not loop upon themselves. """ return _pywrapcp.Solver_SubCircuit(self, nexts) def DelayedPathCumul(self, nexts: "std::vector< operations_research::IntVar * > const &", active: "std::vector< operations_research::IntVar * > const &", cumuls: "std::vector< operations_research::IntVar * > const &", transits: "std::vector< operations_research::IntVar * > const &") -> "operations_research::Constraint *": r""" Delayed version of the same constraint: propagation on the nexts variables is delayed until all constraints have propagated. """ return _pywrapcp.Solver_DelayedPathCumul(self, nexts, active, cumuls, transits) def PathCumul(self, *args) -> "operations_research::Constraint *": r""" *Overload 1:* Creates a constraint which accumulates values along a path such that: cumuls[next[i]] = cumuls[i] + transits[i]. Active variables indicate if the corresponding next variable is active; this could be useful to model unperformed nodes in a routing problem. | *Overload 2:* Creates a constraint which accumulates values along a path such that: cumuls[next[i]] = cumuls[i] + transit_evaluator(i, next[i]). Active variables indicate if the corresponding next variable is active; this could be useful to model unperformed nodes in a routing problem. Ownership of transit_evaluator is taken and it must be a repeatable callback. | *Overload 3:* Creates a constraint which accumulates values along a path such that: cumuls[next[i]] = cumuls[i] + transit_evaluator(i, next[i]) + slacks[i]. Active variables indicate if the corresponding next variable is active; this could be useful to model unperformed nodes in a routing problem. Ownership of transit_evaluator is taken and it must be a repeatable callback. """ return _pywrapcp.Solver_PathCumul(self, *args) def AllowedAssignments(self, *args) -> "operations_research::Constraint *": r""" *Overload 1:* This method creates a constraint where the graph of the relation between the variables is given in extension. There are 'arity' variables involved in the relation and the graph is given by a integer tuple set. | *Overload 2:* Compatibility layer for Python API. """ return _pywrapcp.Solver_AllowedAssignments(self, *args) def TransitionConstraint(self, *args) -> "operations_research::Constraint *": return _pywrapcp.Solver_TransitionConstraint(self, *args) def NonOverlappingBoxesConstraint(self, *args) -> "operations_research::Constraint *": return _pywrapcp.Solver_NonOverlappingBoxesConstraint(self, *args) def Pack(self, vars: "std::vector< operations_research::IntVar * > const &", number_of_bins: "int") -> "operations_research::Pack *": r""" This constraint packs all variables onto 'number_of_bins' variables. For any given variable, a value of 'number_of_bins' indicates that the variable is not assigned to any bin. Dimensions, i.e., cumulative constraints on this packing, can be added directly from the pack class. """ return _pywrapcp.Solver_Pack(self, vars, number_of_bins) def FixedDurationIntervalVar(self, *args) -> "operations_research::IntervalVar *": r""" *Overload 1:* Creates an interval var with a fixed duration. The duration must be greater than 0. If optional is true, then the interval can be performed or unperformed. If optional is false, then the interval is always performed. | *Overload 2:* Creates a performed interval var with a fixed duration. The duration must be greater than 0. | *Overload 3:* Creates an interval var with a fixed duration, and performed_variable. The duration must be greater than 0. """ return _pywrapcp.Solver_FixedDurationIntervalVar(self, *args) def FixedInterval(self, start: "int64_t", duration: "int64_t", name: "std::string const &") -> "operations_research::IntervalVar *": r""" Creates a fixed and performed interval.""" return _pywrapcp.Solver_FixedInterval(self, start, duration, name) def IntervalVar(self, start_min: "int64_t", start_max: "int64_t", duration_min: "int64_t", duration_max: "int64_t", end_min: "int64_t", end_max: "int64_t", optional: "bool", name: "std::string const &") -> "operations_research::IntervalVar *": r""" Creates an interval var by specifying the bounds on start, duration, and end. """ return _pywrapcp.Solver_IntervalVar(self, start_min, start_max, duration_min, duration_max, end_min, end_max, optional, name) def MirrorInterval(self, interval_var: "IntervalVar") -> "operations_research::IntervalVar *": r""" Creates an interval var that is the mirror image of the given one, that is, the interval var obtained by reversing the axis. """ return _pywrapcp.Solver_MirrorInterval(self, interval_var) def FixedDurationStartSyncedOnStartIntervalVar(self, interval_var: "IntervalVar", duration: "int64_t", offset: "int64_t") -> "operations_research::IntervalVar *": r""" Creates an interval var with a fixed duration whose start is synchronized with the start of another interval, with a given offset. The performed status is also in sync with the performed status of the given interval variable. """ return _pywrapcp.Solver_FixedDurationStartSyncedOnStartIntervalVar(self, interval_var, duration, offset) def FixedDurationStartSyncedOnEndIntervalVar(self, interval_var: "IntervalVar", duration: "int64_t", offset: "int64_t") -> "operations_research::IntervalVar *": r""" Creates an interval var with a fixed duration whose start is synchronized with the end of another interval, with a given offset. The performed status is also in sync with the performed status of the given interval variable. """ return _pywrapcp.Solver_FixedDurationStartSyncedOnEndIntervalVar(self, interval_var, duration, offset) def FixedDurationEndSyncedOnStartIntervalVar(self, interval_var: "IntervalVar", duration: "int64_t", offset: "int64_t") -> "operations_research::IntervalVar *": r""" Creates an interval var with a fixed duration whose end is synchronized with the start of another interval, with a given offset. The performed status is also in sync with the performed status of the given interval variable. """ return _pywrapcp.Solver_FixedDurationEndSyncedOnStartIntervalVar(self, interval_var, duration, offset) def FixedDurationEndSyncedOnEndIntervalVar(self, interval_var: "IntervalVar", duration: "int64_t", offset: "int64_t") -> "operations_research::IntervalVar *": r""" Creates an interval var with a fixed duration whose end is synchronized with the end of another interval, with a given offset. The performed status is also in sync with the performed status of the given interval variable. """ return _pywrapcp.Solver_FixedDurationEndSyncedOnEndIntervalVar(self, interval_var, duration, offset) def IntervalRelaxedMin(self, interval_var: "IntervalVar") -> "operations_research::IntervalVar *": r""" Creates and returns an interval variable that wraps around the given one, relaxing the min start and end. Relaxing means making unbounded when optional. If the variable is non-optional, this method returns interval_var. More precisely, such an interval variable behaves as follows: When the underlying must be performed, the returned interval variable behaves exactly as the underlying; When the underlying may or may not be performed, the returned interval variable behaves like the underlying, except that it is unbounded on the min side; When the underlying cannot be performed, the returned interval variable is of duration 0 and must be performed in an interval unbounded on both sides. This is very useful to implement propagators that may only modify the start max or end max. """ return _pywrapcp.Solver_IntervalRelaxedMin(self, interval_var) def IntervalRelaxedMax(self, interval_var: "IntervalVar") -> "operations_research::IntervalVar *": r""" Creates and returns an interval variable that wraps around the given one, relaxing the max start and end. Relaxing means making unbounded when optional. If the variable is non optional, this method returns interval_var. More precisely, such an interval variable behaves as follows: When the underlying must be performed, the returned interval variable behaves exactly as the underlying; When the underlying may or may not be performed, the returned interval variable behaves like the underlying, except that it is unbounded on the max side; When the underlying cannot be performed, the returned interval variable is of duration 0 and must be performed in an interval unbounded on both sides. This is very useful for implementing propagators that may only modify the start min or end min. """ return _pywrapcp.Solver_IntervalRelaxedMax(self, interval_var) def TemporalDisjunction(self, *args) -> "operations_research::Constraint *": r""" *Overload 1:* This constraint implements a temporal disjunction between two interval vars t1 and t2. 'alt' indicates which alternative was chosen (alt == 0 is equivalent to t1 before t2). | *Overload 2:* This constraint implements a temporal disjunction between two interval vars. """ return _pywrapcp.Solver_TemporalDisjunction(self, *args) def DisjunctiveConstraint(self, intervals: "std::vector< operations_research::IntervalVar * > const &", name: "std::string const &") -> "operations_research::DisjunctiveConstraint *": r""" This constraint forces all interval vars into an non-overlapping sequence. Intervals with zero duration can be scheduled anywhere. """ return _pywrapcp.Solver_DisjunctiveConstraint(self, intervals, name) def Cumulative(self, *args) -> "operations_research::Constraint *": r""" *Overload 1:* This constraint forces that, for any integer t, the sum of the demands corresponding to an interval containing t does not exceed the given capacity. Intervals and demands should be vectors of equal size. Demands should only contain non-negative values. Zero values are supported, and the corresponding intervals are filtered out, as they neither impact nor are impacted by this constraint. | *Overload 2:* This constraint forces that, for any integer t, the sum of the demands corresponding to an interval containing t does not exceed the given capacity. Intervals and demands should be vectors of equal size. Demands should only contain non-negative values. Zero values are supported, and the corresponding intervals are filtered out, as they neither impact nor are impacted by this constraint. | *Overload 3:* This constraint forces that, for any integer t, the sum of the demands corresponding to an interval containing t does not exceed the given capacity. Intervals and demands should be vectors of equal size. Demands should only contain non-negative values. Zero values are supported, and the corresponding intervals are filtered out, as they neither impact nor are impacted by this constraint. | *Overload 4:* This constraint enforces that, for any integer t, the sum of the demands corresponding to an interval containing t does not exceed the given capacity. Intervals and demands should be vectors of equal size. Demands should only contain non-negative values. Zero values are supported, and the corresponding intervals are filtered out, as they neither impact nor are impacted by this constraint. | *Overload 5:* This constraint enforces that, for any integer t, the sum of demands corresponding to an interval containing t does not exceed the given capacity. Intervals and demands should be vectors of equal size. Demands should be positive. | *Overload 6:* This constraint enforces that, for any integer t, the sum of demands corresponding to an interval containing t does not exceed the given capacity. Intervals and demands should be vectors of equal size. Demands should be positive. """ return _pywrapcp.Solver_Cumulative(self, *args) def Cover(self, vars: "std::vector< operations_research::IntervalVar * > const &", target_var: "IntervalVar") -> "operations_research::Constraint *": r""" This constraint states that the target_var is the convex hull of the intervals. If none of the interval variables is performed, then the target var is unperformed too. Also, if the target variable is unperformed, then all the intervals variables are unperformed too. """ return _pywrapcp.Solver_Cover(self, vars, target_var) def Assignment(self, *args) -> "operations_research::Assignment *": r""" *Overload 1:* This method creates an empty assignment. | *Overload 2:* This method creates an assignment which is a copy of 'a'. """ return _pywrapcp.Solver_Assignment(self, *args) def FirstSolutionCollector(self, *args) -> "operations_research::SolutionCollector *": r""" *Overload 1:* Collect the first solution of the search. | *Overload 2:* Collect the first solution of the search. The variables will need to be added later. """ return _pywrapcp.Solver_FirstSolutionCollector(self, *args) def LastSolutionCollector(self, *args) -> "operations_research::SolutionCollector *": r""" *Overload 1:* Collect the last solution of the search. | *Overload 2:* Collect the last solution of the search. The variables will need to be added later. """ return _pywrapcp.Solver_LastSolutionCollector(self, *args) def BestValueSolutionCollector(self, *args) -> "operations_research::SolutionCollector *": r""" *Overload 1:* Collect the solution corresponding to the optimal value of the objective of 'assignment'; if 'assignment' does not have an objective no solution is collected. This collector only collects one solution corresponding to the best objective value (the first one found). | *Overload 2:* Collect the solution corresponding to the optimal value of the objective of 'assignment'; if 'assignment' does not have an objective no solution is collected. This collector only collects one solution corresponding to the best objective value (the first one found). The variables will need to be added later. """ return _pywrapcp.Solver_BestValueSolutionCollector(self, *args) def AllSolutionCollector(self, *args) -> "operations_research::SolutionCollector *": r""" *Overload 1:* Collect all solutions of the search. | *Overload 2:* Collect all solutions of the search. The variables will need to be added later. """ return _pywrapcp.Solver_AllSolutionCollector(self, *args) def Minimize(self, v: "IntVar", step: "int64_t") -> "operations_research::OptimizeVar *": r""" Creates a minimization objective.""" return _pywrapcp.Solver_Minimize(self, v, step) def Maximize(self, v: "IntVar", step: "int64_t") -> "operations_research::OptimizeVar *": r""" Creates a maximization objective.""" return _pywrapcp.Solver_Maximize(self, v, step) def Optimize(self, maximize: "bool", v: "IntVar", step: "int64_t") -> "operations_research::OptimizeVar *": r""" Creates a objective with a given sense (true = maximization).""" return _pywrapcp.Solver_Optimize(self, maximize, v, step) def WeightedMinimize(self, *args) -> "operations_research::OptimizeVar *": r""" *Overload 1:* Creates a minimization weighted objective. The actual objective is scalar_prod(sub_objectives, weights). | *Overload 2:* Creates a minimization weighted objective. The actual objective is scalar_prod(sub_objectives, weights). """ return _pywrapcp.Solver_WeightedMinimize(self, *args) def WeightedMaximize(self, *args) -> "operations_research::OptimizeVar *": r""" *Overload 1:* Creates a maximization weigthed objective. | *Overload 2:* Creates a maximization weigthed objective. """ return _pywrapcp.Solver_WeightedMaximize(self, *args) def WeightedOptimize(self, *args) -> "operations_research::OptimizeVar *": r""" *Overload 1:* Creates a weighted objective with a given sense (true = maximization). | *Overload 2:* Creates a weighted objective with a given sense (true = maximization). """ return _pywrapcp.Solver_WeightedOptimize(self, *args) def TabuSearch(self, maximize: "bool", v: "IntVar", step: "int64_t", vars: "std::vector< operations_research::IntVar * > const &", keep_tenure: "int64_t", forbid_tenure: "int64_t", tabu_factor: "double") -> "operations_research::SearchMonitor *": r""" MetaHeuristics which try to get the search out of local optima. Creates a Tabu Search monitor. In the context of local search the behavior is similar to MakeOptimize(), creating an objective in a given sense. The behavior differs once a local optimum is reached: thereafter solutions which degrade the value of the objective are allowed if they are not "tabu". A solution is "tabu" if it doesn't respect the following rules: - improving the best solution found so far - variables in the "keep" list must keep their value, variables in the "forbid" list must not take the value they have in the list. Variables with new values enter the tabu lists after each new solution found and leave the lists after a given number of iterations (called tenure). Only the variables passed to the method can enter the lists. The tabu criterion is softened by the tabu factor which gives the number of "tabu" violations which is tolerated; a factor of 1 means no violations allowed; a factor of 0 means all violations are allowed. """ return _pywrapcp.Solver_TabuSearch(self, maximize, v, step, vars, keep_tenure, forbid_tenure, tabu_factor) def SimulatedAnnealing(self, maximize: "bool", v: "IntVar", step: "int64_t", initial_temperature: "int64_t") -> "operations_research::SearchMonitor *": r""" Creates a Simulated Annealing monitor.""" return _pywrapcp.Solver_SimulatedAnnealing(self, maximize, v, step, initial_temperature) def LubyRestart(self, scale_factor: "int") -> "operations_research::SearchMonitor *": r""" This search monitor will restart the search periodically. At the iteration n, it will restart after scale_factor * Luby(n) failures where Luby is the Luby Strategy (i.e. 1 1 2 1 1 2 4 1 1 2 1 1 2 4 8...). """ return _pywrapcp.Solver_LubyRestart(self, scale_factor) def ConstantRestart(self, frequency: "int") -> "operations_research::SearchMonitor *": r""" This search monitor will restart the search periodically after 'frequency' failures. """ return _pywrapcp.Solver_ConstantRestart(self, frequency) def TimeLimit(self, *args) -> "operations_research::RegularLimit *": return _pywrapcp.Solver_TimeLimit(self, *args) def BranchesLimit(self, branches: "int64_t") -> "operations_research::RegularLimit *": r""" Creates a search limit that constrains the number of branches explored in the search tree. """ return _pywrapcp.Solver_BranchesLimit(self, branches) def FailuresLimit(self, failures: "int64_t") -> "operations_research::RegularLimit *": r""" Creates a search limit that constrains the number of failures that can happen when exploring the search tree. """ return _pywrapcp.Solver_FailuresLimit(self, failures) def SolutionsLimit(self, solutions: "int64_t") -> "operations_research::RegularLimit *": r""" Creates a search limit that constrains the number of solutions found during the search. """ return _pywrapcp.Solver_SolutionsLimit(self, solutions) def Limit(self, *args) -> "operations_research::SearchLimit *": r""" *Overload 1:* Limits the search with the 'time', 'branches', 'failures' and 'solutions' limits. 'smart_time_check' reduces the calls to the wall | *Overload 2:* Creates a search limit from its protobuf description | *Overload 3:* Creates a search limit that is reached when either of the underlying limit is reached. That is, the returned limit is more stringent than both argument limits. """ return _pywrapcp.Solver_Limit(self, *args) def CustomLimit(self, limiter: "std::function< bool () >") -> "operations_research::SearchLimit *": r""" Callback-based search limit. Search stops when limiter returns true; if this happens at a leaf the corresponding solution will be rejected. """ return _pywrapcp.Solver_CustomLimit(self, limiter) def SearchLog(self, *args) -> "operations_research::SearchMonitor *": return _pywrapcp.Solver_SearchLog(self, *args) def SearchTrace(self, prefix: "std::string const &") -> "operations_research::SearchMonitor *": r""" Creates a search monitor that will trace precisely the behavior of the search. Use this only for low level debugging. """ return _pywrapcp.Solver_SearchTrace(self, prefix) def PrintModelVisitor(self) -> "operations_research::ModelVisitor *": r""" Prints the model.""" return _pywrapcp.Solver_PrintModelVisitor(self) def StatisticsModelVisitor(self) -> "operations_research::ModelVisitor *": r""" Displays some nice statistics on the model.""" return _pywrapcp.Solver_StatisticsModelVisitor(self) def AssignVariableValue(self, var: "IntVar", val: "int64_t") -> "operations_research::Decision *": r""" Decisions.""" return _pywrapcp.Solver_AssignVariableValue(self, var, val) def VariableLessOrEqualValue(self, var: "IntVar", value: "int64_t") -> "operations_research::Decision *": return _pywrapcp.Solver_VariableLessOrEqualValue(self, var, value) def VariableGreaterOrEqualValue(self, var: "IntVar", value: "int64_t") -> "operations_research::Decision *": return _pywrapcp.Solver_VariableGreaterOrEqualValue(self, var, value) def SplitVariableDomain(self, var: "IntVar", val: "int64_t", start_with_lower_half: "bool") -> "operations_research::Decision *": return _pywrapcp.Solver_SplitVariableDomain(self, var, val, start_with_lower_half) def AssignVariableValueOrFail(self, var: "IntVar", value: "int64_t") -> "operations_research::Decision *": return _pywrapcp.Solver_AssignVariableValueOrFail(self, var, value) def AssignVariablesValues(self, vars: "std::vector< operations_research::IntVar * > const &", values: "std::vector< int64_t > const &") -> "operations_research::Decision *": return _pywrapcp.Solver_AssignVariablesValues(self, vars, values) def FailDecision(self) -> "operations_research::Decision *": return _pywrapcp.Solver_FailDecision(self) def Decision(self, apply: "operations_research::Solver::Action", refute: "operations_research::Solver::Action") -> "operations_research::Decision *": return _pywrapcp.Solver_Decision(self, apply, refute) def Compose(self, dbs: "std::vector< operations_research::DecisionBuilder * > const &") -> "operations_research::DecisionBuilder *": return _pywrapcp.Solver_Compose(self, dbs) def Try(self, dbs: "std::vector< operations_research::DecisionBuilder * > const &") -> "operations_research::DecisionBuilder *": return _pywrapcp.Solver_Try(self, dbs) def DefaultPhase(self, *args) -> "operations_research::DecisionBuilder *": return _pywrapcp.Solver_DefaultPhase(self, *args) def ScheduleOrPostpone(self, var: "IntervalVar", est: "int64_t", marker: "int64_t *const") -> "operations_research::Decision *": r""" Returns a decision that tries to schedule a task at a given time. On the Apply branch, it will set that interval var as performed and set its start to 'est'. On the Refute branch, it will just update the 'marker' to 'est' + 1. This decision is used in the INTERVAL_SET_TIMES_FORWARD strategy. """ return _pywrapcp.Solver_ScheduleOrPostpone(self, var, est, marker) def ScheduleOrExpedite(self, var: "IntervalVar", est: "int64_t", marker: "int64_t *const") -> "operations_research::Decision *": r""" Returns a decision that tries to schedule a task at a given time. On the Apply branch, it will set that interval var as performed and set its end to 'est'. On the Refute branch, it will just update the 'marker' to 'est' - 1. This decision is used in the INTERVAL_SET_TIMES_BACKWARD strategy. """ return _pywrapcp.Solver_ScheduleOrExpedite(self, var, est, marker) def RankFirstInterval(self, sequence: "SequenceVar", index: "int") -> "operations_research::Decision *": r""" Returns a decision that tries to rank first the ith interval var in the sequence variable. """ return _pywrapcp.Solver_RankFirstInterval(self, sequence, index) def RankLastInterval(self, sequence: "SequenceVar", index: "int") -> "operations_research::Decision *": r""" Returns a decision that tries to rank last the ith interval var in the sequence variable. """ return _pywrapcp.Solver_RankLastInterval(self, sequence, index) def Phase(self, *args) -> "operations_research::DecisionBuilder *": return _pywrapcp.Solver_Phase(self, *args) def DecisionBuilderFromAssignment(self, assignment: "Assignment", db: "DecisionBuilder", vars: "std::vector< operations_research::IntVar * > const &") -> "operations_research::DecisionBuilder *": r""" Returns a decision builder for which the left-most leaf corresponds to assignment, the rest of the tree being explored using 'db'. """ return _pywrapcp.Solver_DecisionBuilderFromAssignment(self, assignment, db, vars) def ConstraintAdder(self, ct: "Constraint") -> "operations_research::DecisionBuilder *": r""" Returns a decision builder that will add the given constraint to the model. """ return _pywrapcp.Solver_ConstraintAdder(self, ct) def SolveOnce(self, db: "DecisionBuilder", monitors: "std::vector< operations_research::SearchMonitor * > const &") -> "operations_research::DecisionBuilder *": return _pywrapcp.Solver_SolveOnce(self, db, monitors) def NestedOptimize(self, *args) -> "operations_research::DecisionBuilder *": return _pywrapcp.Solver_NestedOptimize(self, *args) def RestoreAssignment(self, assignment: "Assignment") -> "operations_research::DecisionBuilder *": r""" Returns a DecisionBuilder which restores an Assignment (calls void Assignment::Restore()) """ return _pywrapcp.Solver_RestoreAssignment(self, assignment) def StoreAssignment(self, assignment: "Assignment") -> "operations_research::DecisionBuilder *": r""" Returns a DecisionBuilder which stores an Assignment (calls void Assignment::Store()) """ return _pywrapcp.Solver_StoreAssignment(self, assignment) def Operator(self, *args) -> "operations_research::LocalSearchOperator *": return _pywrapcp.Solver_Operator(self, *args) def RandomLnsOperator(self, *args) -> "operations_research::LocalSearchOperator *": return _pywrapcp.Solver_RandomLnsOperator(self, *args) def MoveTowardTargetOperator(self, *args) -> "operations_research::LocalSearchOperator *": r""" *Overload 1:* Creates a local search operator that tries to move the assignment of some variables toward a target. The target is given as an Assignment. This operator generates neighbors in which the only difference compared to the current state is that one variable that belongs to the target assignment is set to its target value. | *Overload 2:* Creates a local search operator that tries to move the assignment of some variables toward a target. The target is given either as two vectors: a vector of variables and a vector of associated target values. The two vectors should be of the same length. This operator generates neighbors in which the only difference compared to the current state is that one variable that belongs to the given vector is set to its target value. """ return _pywrapcp.Solver_MoveTowardTargetOperator(self, *args) def ConcatenateOperators(self, *args) -> "operations_research::LocalSearchOperator *": return _pywrapcp.Solver_ConcatenateOperators(self, *args) def RandomConcatenateOperators(self, *args) -> "operations_research::LocalSearchOperator *": r""" *Overload 1:* Randomized version of local search concatenator; calls a random operator at each call to MakeNextNeighbor(). | *Overload 2:* Randomized version of local search concatenator; calls a random operator at each call to MakeNextNeighbor(). The provided seed is used to initialize the random number generator. """ return _pywrapcp.Solver_RandomConcatenateOperators(self, *args) def NeighborhoodLimit(self, op: "LocalSearchOperator", limit: "int64_t") -> "operations_research::LocalSearchOperator *": r""" Creates a local search operator that wraps another local search operator and limits the number of neighbors explored (i.e., calls to MakeNextNeighbor from the current solution (between two calls to Start()). When this limit is reached, MakeNextNeighbor() returns false. The counter is cleared when Start() is called. """ return _pywrapcp.Solver_NeighborhoodLimit(self, op, limit) def LocalSearchPhase(self, *args) -> "operations_research::DecisionBuilder *": return _pywrapcp.Solver_LocalSearchPhase(self, *args) def LocalSearchPhaseParameters(self, *args) -> "operations_research::LocalSearchPhaseParameters *": return _pywrapcp.Solver_LocalSearchPhaseParameters(self, *args) def SearchDepth(self) -> "int": r""" Gets the search depth of the current active search. Returns -1 if there is no active search opened. """ return _pywrapcp.Solver_SearchDepth(self) def SearchLeftDepth(self) -> "int": r""" Gets the search left depth of the current active search. Returns -1 if there is no active search opened. """ return _pywrapcp.Solver_SearchLeftDepth(self) def SolveDepth(self) -> "int": r""" Gets the number of nested searches. It returns 0 outside search, 1 during the top level search, 2 or more in case of nested searches. """ return _pywrapcp.Solver_SolveDepth(self) def Rand64(self, size: "int64_t") -> "int64_t": r""" Returns a random value between 0 and 'size' - 1;""" return _pywrapcp.Solver_Rand64(self, size) def Rand32(self, size: "int32_t") -> "int32_t": r""" Returns a random value between 0 and 'size' - 1;""" return _pywrapcp.Solver_Rand32(self, size) def ReSeed(self, seed: "int32_t") -> "void": r""" Reseed the solver random generator.""" return _pywrapcp.Solver_ReSeed(self, seed) def LocalSearchProfile(self) -> "std::string": r""" Returns local search profiling information in a human readable format.""" return _pywrapcp.Solver_LocalSearchProfile(self) def Constraints(self) -> "int": r""" Counts the number of constraints that have been added to the solver before the search. """ return _pywrapcp.Solver_Constraints(self) def Accept(self, visitor: "operations_research::ModelVisitor *const") -> "void": r""" Accepts the given model visitor.""" return _pywrapcp.Solver_Accept(self, visitor) def FinishCurrentSearch(self) -> "void": r""" Tells the solver to kill or restart the current search.""" return _pywrapcp.Solver_FinishCurrentSearch(self) def RestartCurrentSearch(self) -> "void": return _pywrapcp.Solver_RestartCurrentSearch(self) def ShouldFail(self) -> "void": r""" These methods are only useful for the SWIG wrappers, which need a way to externally cause the Solver to fail. """ return _pywrapcp.Solver_ShouldFail(self) def __str__(self) -> "std::string": return _pywrapcp.Solver___str__(self) def Add(self, ct): if isinstance(ct, PyConstraint): self.__python_constraints.append(ct) self.AddConstraint(ct) def TreeNoCycle(self, nexts: "std::vector< operations_research::IntVar * > const &", active: "std::vector< operations_research::IntVar * > const &", callback: "operations_research::Solver::IndexFilter1"=0) -> "operations_research::Constraint *": return _pywrapcp.Solver_TreeNoCycle(self, nexts, active, callback) def SearchLogWithCallback(self, period: "int", callback: "std::function< std::string () >") -> "operations_research::SearchMonitor *": return _pywrapcp.Solver_SearchLogWithCallback(self, period, callback) def ElementFunction(self, values: "std::function< int64_t (int64_t) >", index: "IntVar") -> "operations_research::IntExpr *": return _pywrapcp.Solver_ElementFunction(self, values, index) def VarEvalValStrPhase(self, vars: "std::vector< operations_research::IntVar * > const &", var_evaluator: "std::function< int64_t (int64_t) >", val_str: "operations_research::Solver::IntValueStrategy") -> "operations_research::DecisionBuilder *": return _pywrapcp.Solver_VarEvalValStrPhase(self, vars, var_evaluator, val_str) def VarStrValEvalPhase(self, vars: "std::vector< operations_research::IntVar * > const &", var_str: "operations_research::Solver::IntVarStrategy", val_eval: "operations_research::Solver::IndexEvaluator2") -> "operations_research::DecisionBuilder *": return _pywrapcp.Solver_VarStrValEvalPhase(self, vars, var_str, val_eval) def VarEvalValEvalPhase(self, vars: "std::vector< operations_research::IntVar * > const &", var_eval: "std::function< int64_t (int64_t) >", val_eval: "operations_research::Solver::IndexEvaluator2") -> "operations_research::DecisionBuilder *": return _pywrapcp.Solver_VarEvalValEvalPhase(self, vars, var_eval, val_eval) def VarStrValEvalTieBreakPhase(self, vars: "std::vector< operations_research::IntVar * > const &", var_str: "operations_research::Solver::IntVarStrategy", val_eval: "operations_research::Solver::IndexEvaluator2", tie_breaker: "std::function< int64_t (int64_t) >") -> "operations_research::DecisionBuilder *": return _pywrapcp.Solver_VarStrValEvalTieBreakPhase(self, vars, var_str, val_eval, tie_breaker) def VarEvalValEvalTieBreakPhase(self, vars: "std::vector< operations_research::IntVar * > const &", var_eval: "std::function< int64_t (int64_t) >", val_eval: "operations_research::Solver::IndexEvaluator2", tie_breaker: "std::function< int64_t (int64_t) >") -> "operations_research::DecisionBuilder *": return _pywrapcp.Solver_VarEvalValEvalTieBreakPhase(self, vars, var_eval, val_eval, tie_breaker) def EvalEvalStrPhase(self, vars: "std::vector< operations_research::IntVar * > const &", evaluator: "operations_research::Solver::IndexEvaluator2", str: "operations_research::Solver::EvaluatorStrategy") -> "operations_research::DecisionBuilder *": return _pywrapcp.Solver_EvalEvalStrPhase(self, vars, evaluator, str) def EvalEvalStrTieBreakPhase(self, vars: "std::vector< operations_research::IntVar * > const &", evaluator: "operations_research::Solver::IndexEvaluator2", tie_breaker: "operations_research::Solver::IndexEvaluator1", str: "operations_research::Solver::EvaluatorStrategy") -> "operations_research::DecisionBuilder *": return _pywrapcp.Solver_EvalEvalStrTieBreakPhase(self, vars, evaluator, tie_breaker, str) def GuidedLocalSearch(self, *args) -> "operations_research::SearchMonitor *": return _pywrapcp.Solver_GuidedLocalSearch(self, *args) def SumObjectiveFilter(self, vars: "std::vector< operations_research::IntVar * > const &", values: "operations_research::Solver::IndexEvaluator2", filter_enum: "operations_research::Solver::LocalSearchFilterBound") -> "operations_research::LocalSearchFilter *": return _pywrapcp.Solver_SumObjectiveFilter(self, vars, values, filter_enum)Class variables
var ASSIGN_CENTER_VALUE-
Selects the first possible value which is the closest to the center of the domain of the selected variable. The center is defined as (min + max) / 2.
var ASSIGN_MAX_VALUE-
Selects the max value of the selected variable.
var ASSIGN_MIN_VALUE-
Selects the min value of the selected variable.
var ASSIGN_RANDOM_VALUE-
Selects randomly one of the possible values of the selected variable.
var CHOOSE_FIRST_UNBOUND-
Select the first unbound variable. Variables are considered in the order of the vector of IntVars used to create the selector.
var CHOOSE_HIGHEST_MAX-
Among unbound variables, select the variable with the highest maximal value. In case of a tie, the first one is selected, first being defined by the order in the vector of IntVars used to create the selector.
var CHOOSE_LOWEST_MIN-
Among unbound variables, select the variable with the smallest minimal value. In case of a tie, the first one is selected, "first" defined by the order in the vector of IntVars used to create the selector.
var CHOOSE_MAX_REGRET_ON_MIN-
Among unbound variables, select the variable with the largest gap between the first and the second values of the domain.
var CHOOSE_MAX_SIZE-
Among unbound variables, select the variable with the highest size. In case of a tie, the first one is selected, first being defined by the order in the vector of IntVars used to create the selector.
var CHOOSE_MIN_SIZE-
Among unbound variables, select the variable with the smallest size. In case of a tie, the first one is selected, first being defined by the order in the vector of IntVars used to create the selector.
var CHOOSE_MIN_SIZE_HIGHEST_MAX-
Among unbound variables, select the variable with the smallest size, i.e., the smallest number of possible values. In case of a tie, the selected variable is the one with the highest max value. In case of a tie, the first one is selected, first being defined by the order in the vector of IntVars used to create the selector.
var CHOOSE_MIN_SIZE_HIGHEST_MIN-
Among unbound variables, select the variable with the smallest size, i.e., the smallest number of possible values. In case of a tie, the selected variable is the one with the highest min value. In case of a tie, the first one is selected, first being defined by the order in the vector of IntVars used to create the selector.
var CHOOSE_MIN_SIZE_LOWEST_MAX-
Among unbound variables, select the variable with the smallest size, i.e., the smallest number of possible values. In case of a tie, the selected variables is the one with the lowest max value. In case of a tie, the first one is selected, first being defined by the order in the vector of IntVars used to create the selector.
var CHOOSE_MIN_SIZE_LOWEST_MIN-
Among unbound variables, select the variable with the smallest size, i.e., the smallest number of possible values. In case of a tie, the selected variables is the one with the lowest min value. In case of a tie, the first one is selected, first being defined by the order in the vector of IntVars used to create the selector.
var CHOOSE_MIN_SLACK_RANK_FORWARDvar CHOOSE_PATH-
Selects the next unbound variable on a path, the path being defined by the variables: var[i] corresponds to the index of the next of i.
var CHOOSE_RANDOM-
Randomly select one of the remaining unbound variables.
var CHOOSE_RANDOM_RANK_FORWARDvar CROSS-
Operator which cross exchanges the starting chains of 2 paths, including exchanging the whole paths. First and last nodes are not moved. Possible neighbors for the paths 1 -> 2 -> 3 -> 4 -> 5 and 6 -> 7 -> 8 (where (1, 5) and (6, 8) are first and last nodes of the paths and can therefore not be moved): 1 -> [7] -> 3 -> 4 -> 5 6 -> [2] -> 8 1 -> [7] -> 4 -> 5 6 -> [2 -> 3] -> 8 1 -> [7] -> 5 6 -> [2 -> 3 -> 4] -> 8
var DECREMENT-
Operator which defines a neighborhood to decrement values. The behavior is the same as INCREMENT, except values are decremented instead of incremented.
var DELAYED_PRIORITY-
DELAYED_PRIORITY is the lowest priority: Demons will be processed after VAR_PRIORITY and NORMAL_PRIORITY demons.
var EQ-
Move is accepted when the current objective value is in the interval objective.Min .. objective.Max.
var EXCHANGE-
Operator which exchanges the positions of two nodes. Possible neighbors for the path 1 -> 2 -> 3 -> 4 -> 5 (where (1, 5) are first and last nodes of the path and can therefore not be moved): 1 -> [3] -> [2] -> 4 -> 5 1 -> [4] -> 3 -> [2] -> 5 1 -> 2 -> [4] -> [3] -> 5
var EXTENDEDSWAPACTIVE-
Operator which makes an inactive node active and an active one inactive. It is similar to SwapActiveOperator except that it tries to insert the inactive node in all possible positions instead of just the position of the node made inactive. Possible neighbors for the path 1 -> 2 -> 3 -> 4 with 5 inactive (where 1 and 4 are first and last nodes of the path) are: 1 -> [5] -> 3 -> 4 with 2 inactive 1 -> 3 -> [5] -> 4 with 2 inactive 1 -> [5] -> 2 -> 4 with 3 inactive 1 -> 2 -> [5] -> 4 with 3 inactive
var FULLPATHLNS-
Operator which relaxes one entire path and all inactive nodes, thus defining num_paths neighbors.
var GE-
Move is accepted when the current objective value >= objective.Min.
var INCREMENT-
Operator which defines one neighbor per variable. Each neighbor tries to increment by one the value of the corresponding variable. When a new solution is found the neighborhood is rebuilt from scratch, i.e., tries to increment values in the variable order. Consider for instance variables x and y. x is incremented one by one to its max, and when it is not possible to increment x anymore, y is incremented once. If this is a solution, then next neighbor tries to increment x.
var INTERVAL_DEFAULT-
The default is INTERVAL_SET_TIMES_FORWARD.
var INTERVAL_SET_TIMES_BACKWARD-
Selects the variable with the highest ending time of all variables, and fixes the ending time to this highest values.
var INTERVAL_SET_TIMES_FORWARD-
Selects the variable with the lowest starting time of all variables, and fixes its starting time to this lowest value.
var INTERVAL_SIMPLE-
The simple is INTERVAL_SET_TIMES_FORWARD.
var INT_VALUE_DEFAULT-
The default behavior is ASSIGN_MIN_VALUE.
var INT_VALUE_SIMPLE-
The simple selection is ASSIGN_MIN_VALUE.
var INT_VAR_DEFAULT-
The default behavior is CHOOSE_FIRST_UNBOUND.
var INT_VAR_SIMPLE-
The simple selection is CHOOSE_FIRST_UNBOUND.
var LE-
Move is accepted when the current objective value <= objective.Max.
var MAKEACTIVE-
Operator which inserts an inactive node into a path. Possible neighbors for the path 1 -> 2 -> 3 -> 4 with 5 inactive (where 1 and 4 are first and last nodes of the path) are: 1 -> [5] -> 2 -> 3 -> 4 1 -> 2 -> [5] -> 3 -> 4 1 -> 2 -> 3 -> [5] -> 4
var MAKECHAININACTIVE-
Operator which makes a "chain" of path nodes inactive. Possible neighbors for the path 1 -> 2 -> 3 -> 4 (where 1 and 4 are first and last nodes of the path) are: 1 -> 3 -> 4 with 2 inactive 1 -> 2 -> 4 with 3 inactive 1 -> 4 with 2 and 3 inactive
var MAKEINACTIVE-
Operator which makes path nodes inactive. Possible neighbors for the path 1 -> 2 -> 3 -> 4 (where 1 and 4 are first and last nodes of the path) are: 1 -> 3 -> 4 with 2 inactive 1 -> 2 -> 4 with 3 inactive
var NORMAL_PRIORITY-
NORMAL_PRIORITY is the highest priority: Demons will be processed first.
var OROPT-
Relocate: OROPT and RELOCATE. Operator which moves a sub-chain of a path to another position; the specified chain length is the fixed length of the chains being moved. When this length is 1, the operator simply moves a node to another position. Possible neighbors for the path 1 -> 2 -> 3 -> 4 -> 5, for a chain length of 2 (where (1, 5) are first and last nodes of the path and can therefore not be moved): 1 -> 4 -> [2 -> 3] -> 5 1 -> [3 -> 4] -> 2 -> 5
Using Relocate with chain lengths of 1, 2 and 3 together is equivalent to the OrOpt operator on a path. The OrOpt operator is a limited version of 3Opt (breaks 3 arcs on a path).
var PATHLNS-
Operator which relaxes two sub-chains of three consecutive arcs each. Each sub-chain is defined by a start node and the next three arcs. Those six arcs are relaxed to build a new neighbor. PATHLNS explores all possible pairs of starting nodes and so defines n^2 neighbors, n being the number of nodes. Note that the two sub-chains can be part of the same path; they even may overlap.
var RELOCATE-
Relocate neighborhood with length of 1 (see OROPT comment).
var SEQUENCE_DEFAULTvar SEQUENCE_SIMPLEvar SIMPLELNS-
Operator which defines one neighbor per variable. Each neighbor relaxes one variable. When a new solution is found the neighborhood is rebuilt from scratch. Consider for instance variables x and y. First x is relaxed and the solver is looking for the best possible solution (with only x relaxed). Then y is relaxed, and the solver is looking for a new solution. If a new solution is found, then the next variable to be relaxed is x.
var SPLIT_LOWER_HALF-
Split the domain in two around the center, and choose the lower part first.
var SPLIT_UPPER_HALF-
Split the domain in two around the center, and choose the lower part first.
var SWAPACTIVE-
Operator which replaces an active node by an inactive one. Possible neighbors for the path 1 -> 2 -> 3 -> 4 with 5 inactive (where 1 and 4 are first and last nodes of the path) are: 1 -> [5] -> 3 -> 4 with 2 inactive 1 -> 2 -> [5] -> 4 with 3 inactive
var TWOOPT-
Operator which reverses a sub-chain of a path. It is called TwoOpt because it breaks two arcs on the path; resulting paths are called two-optimal. Possible neighbors for the path 1 -> 2 -> 3 -> 4 -> 5 (where (1, 5) are first and last nodes of the path and can therefore not be moved): 1 -> [3 -> 2] -> 4 -> 5 1 -> [4 -> 3 -> 2] -> 5 1 -> 2 -> [4 -> 3] -> 5
var UNACTIVELNS-
Operator which relaxes all inactive nodes and one sub-chain of six consecutive arcs. That way the path can be improved by inserting inactive nodes or swapping arcs.
var VAR_PRIORITY-
VAR_PRIORITY is between DELAYED_PRIORITY and NORMAL_PRIORITY.
Static methods
def DefaultSolverParameters() ‑> operations_research::ConstraintSolverParameters-
Create a ConstraintSolverParameters proto with all the default values.
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@staticmethod def DefaultSolverParameters() -> "operations_research::ConstraintSolverParameters": r""" Create a ConstraintSolverParameters proto with all the default values.""" return _pywrapcp.Solver_DefaultSolverParameters() def MemoryUsage() ‑> int64_t-
Current memory usage in bytes
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@staticmethod def MemoryUsage() -> "int64_t": r""" Current memory usage in bytes""" return _pywrapcp.Solver_MemoryUsage()
Instance variables
var thisown-
The membership flag
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thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
Methods
def AbsEquality(self, var: IntVar, abs_var: IntVar) ‑> operations_research::Constraint *-
Creates the constraint abs(var) == abs_var.
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def AbsEquality(self, var: "IntVar", abs_var: "IntVar") -> "operations_research::Constraint *": r""" Creates the constraint abs(var) == abs_var.""" return _pywrapcp.Solver_AbsEquality(self, var, abs_var) def Accept(self, visitor: operations_research::ModelVisitor *const) ‑> void-
Accepts the given model visitor.
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def Accept(self, visitor: "operations_research::ModelVisitor *const") -> "void": r""" Accepts the given model visitor.""" return _pywrapcp.Solver_Accept(self, visitor) def AcceptedNeighbors(self) ‑> int64_t-
The number of accepted neighbors.
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def AcceptedNeighbors(self) -> "int64_t": r""" The number of accepted neighbors.""" return _pywrapcp.Solver_AcceptedNeighbors(self) def Add(self, ct)-
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def Add(self, ct): if isinstance(ct, PyConstraint): self.__python_constraints.append(ct) self.AddConstraint(ct) def AddConstraint(self, c: Constraint) ‑> void-
Adds the constraint 'c' to the model.
After calling this method, and until there is a backtrack that undoes the addition, any assignment of variables to values must satisfy the given constraint in order to be considered feasible. There are two fairly different use cases:
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the most common use case is modeling: the given constraint is really part of the problem that the user is trying to solve. In this use case, AddConstraint is called outside of search (i.e., with state() == OUTSIDE_SEARCH). Most users should only use AddConstraint in this way. In this case, the constraint will belong to the model forever: it cannot not be removed by backtracking.
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a rarer use case is that 'c' is not a real constraint of the model. It may be a constraint generated by a branching decision (a constraint whose goal is to restrict the search space), a symmetry breaking constraint (a constraint that does restrict the search space, but in a way that cannot have an impact on the quality of the solutions in the subtree), or an inferred constraint that, while having no semantic value to the model (it does not restrict the set of solutions), is worth having because we believe it may strengthen the propagation. In these cases, it happens that the constraint is added during the search (i.e., with state() == IN_SEARCH or state() == IN_ROOT_NODE). When a constraint is added during a search, it applies only to the subtree of the search tree rooted at the current node, and will be automatically removed by backtracking.
This method does not take ownership of the constraint. If the constraint has been created by any factory method (Solver::MakeXXX), it will automatically be deleted. However, power users who implement their own constraints should do: solver.AddConstraint(solver.RevAlloc(new MyConstraint(…));
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def AddConstraint(self, c: "Constraint") -> "void": r""" Adds the constraint 'c' to the model. After calling this method, and until there is a backtrack that undoes the addition, any assignment of variables to values must satisfy the given constraint in order to be considered feasible. There are two fairly different use cases: - the most common use case is modeling: the given constraint is really part of the problem that the user is trying to solve. In this use case, AddConstraint is called outside of search (i.e., with state() == OUTSIDE_SEARCH). Most users should only use AddConstraint in this way. In this case, the constraint will belong to the model forever: it cannot not be removed by backtracking. - a rarer use case is that 'c' is not a real constraint of the model. It may be a constraint generated by a branching decision (a constraint whose goal is to restrict the search space), a symmetry breaking constraint (a constraint that does restrict the search space, but in a way that cannot have an impact on the quality of the solutions in the subtree), or an inferred constraint that, while having no semantic value to the model (it does not restrict the set of solutions), is worth having because we believe it may strengthen the propagation. In these cases, it happens that the constraint is added during the search (i.e., with state() == IN_SEARCH or state() == IN_ROOT_NODE). When a constraint is added during a search, it applies only to the subtree of the search tree rooted at the current node, and will be automatically removed by backtracking. This method does not take ownership of the constraint. If the constraint has been created by any factory method (Solver::MakeXXX), it will automatically be deleted. However, power users who implement their own constraints should do: solver.AddConstraint(solver.RevAlloc(new MyConstraint(...)); """ return _pywrapcp.Solver_AddConstraint(self, c) -
def AllDifferent(self, *args) ‑> operations_research::Constraint *-
Overload 1: All variables are pairwise different. This corresponds to the stronger version of the propagation algorithm.
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Overload 2: All variables are pairwise different. If 'stronger_propagation' is true, stronger, and potentially slower propagation will occur. This API will be deprecated in the future.
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def AllDifferent(self, *args) -> "operations_research::Constraint *": r""" *Overload 1:* All variables are pairwise different. This corresponds to the stronger version of the propagation algorithm. | *Overload 2:* All variables are pairwise different. If 'stronger_propagation' is true, stronger, and potentially slower propagation will occur. This API will be deprecated in the future. """ return _pywrapcp.Solver_AllDifferent(self, *args) def AllDifferentExcept(self, vars: std::vector< operations_research::IntVar * > const &, escape_value: int64_t) ‑> operations_research::Constraint *-
All variables are pairwise different, unless they are assigned to the escape value.
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def AllDifferentExcept(self, vars: "std::vector< operations_research::IntVar * > const &", escape_value: "int64_t") -> "operations_research::Constraint *": r""" All variables are pairwise different, unless they are assigned to the escape value. """ return _pywrapcp.Solver_AllDifferentExcept(self, vars, escape_value) def AllSolutionCollector(self, *args) ‑> operations_research::SolutionCollector *-
Overload 1: Collect all solutions of the search.
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Overload 2: Collect all solutions of the search. The variables will need to be added later.
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def AllSolutionCollector(self, *args) -> "operations_research::SolutionCollector *": r""" *Overload 1:* Collect all solutions of the search. | *Overload 2:* Collect all solutions of the search. The variables will need to be added later. """ return _pywrapcp.Solver_AllSolutionCollector(self, *args) def AllowedAssignments(self, *args) ‑> operations_research::Constraint *-
Overload 1: This method creates a constraint where the graph of the relation between the variables is given in extension. There are 'arity' variables involved in the relation and the graph is given by a integer tuple set.
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Overload 2: Compatibility layer for Python API.
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def AllowedAssignments(self, *args) -> "operations_research::Constraint *": r""" *Overload 1:* This method creates a constraint where the graph of the relation between the variables is given in extension. There are 'arity' variables involved in the relation and the graph is given by a integer tuple set. | *Overload 2:* Compatibility layer for Python API. """ return _pywrapcp.Solver_AllowedAssignments(self, *args) def AssignVariableValue(self, var: IntVar, val: int64_t) ‑> operations_research::Decision *-
Decisions.
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def AssignVariableValue(self, var: "IntVar", val: "int64_t") -> "operations_research::Decision *": r""" Decisions.""" return _pywrapcp.Solver_AssignVariableValue(self, var, val) def AssignVariableValueOrFail(self, var: IntVar, value: int64_t) ‑> operations_research::Decision *-
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def AssignVariableValueOrFail(self, var: "IntVar", value: "int64_t") -> "operations_research::Decision *": return _pywrapcp.Solver_AssignVariableValueOrFail(self, var, value) def AssignVariablesValues(self, vars: std::vector< operations_research::IntVar * > const &, values: std::vector< int64_t > const &) ‑> operations_research::Decision *-
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def AssignVariablesValues(self, vars: "std::vector< operations_research::IntVar * > const &", values: "std::vector< int64_t > const &") -> "operations_research::Decision *": return _pywrapcp.Solver_AssignVariablesValues(self, vars, values) def Assignment(self, *args) ‑> operations_research::Assignment *-
Overload 1: This method creates an empty assignment.
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Overload 2: This method creates an assignment which is a copy of 'a'.
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def Assignment(self, *args) -> "operations_research::Assignment *": r""" *Overload 1:* This method creates an empty assignment. | *Overload 2:* This method creates an assignment which is a copy of 'a'. """ return _pywrapcp.Solver_Assignment(self, *args) def BestValueSolutionCollector(self, *args) ‑> operations_research::SolutionCollector *-
Overload 1: Collect the solution corresponding to the optimal value of the objective of 'assignment'; if 'assignment' does not have an objective no solution is collected. This collector only collects one solution corresponding to the best objective value (the first one found).
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Overload 2: Collect the solution corresponding to the optimal value of the objective of 'assignment'; if 'assignment' does not have an objective no solution is collected. This collector only collects one solution corresponding to the best objective value (the first one found). The variables will need to be added later.
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def BestValueSolutionCollector(self, *args) -> "operations_research::SolutionCollector *": r""" *Overload 1:* Collect the solution corresponding to the optimal value of the objective of 'assignment'; if 'assignment' does not have an objective no solution is collected. This collector only collects one solution corresponding to the best objective value (the first one found). | *Overload 2:* Collect the solution corresponding to the optimal value of the objective of 'assignment'; if 'assignment' does not have an objective no solution is collected. This collector only collects one solution corresponding to the best objective value (the first one found). The variables will need to be added later. """ return _pywrapcp.Solver_BestValueSolutionCollector(self, *args) def BetweenCt(self, expr: IntExpr, l: int64_t, u: int64_t) ‑> operations_research::Constraint *-
(l <= expr <= u)
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def BetweenCt(self, expr: "IntExpr", l: "int64_t", u: "int64_t") -> "operations_research::Constraint *": r""" (l <= expr <= u)""" return _pywrapcp.Solver_BetweenCt(self, expr, l, u) def BoolVar(self, *args) ‑> operations_research::IntVar *-
Overload 1: MakeBoolVar will create a variable with a {0, 1} domain.
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Overload 2: MakeBoolVar will create a variable with a {0, 1} domain.
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def BoolVar(self, *args) -> "operations_research::IntVar *": r""" *Overload 1:* MakeBoolVar will create a variable with a {0, 1} domain. | *Overload 2:* MakeBoolVar will create a variable with a {0, 1} domain. """ return _pywrapcp.Solver_BoolVar(self, *args) def Branches(self) ‑> int64_t-
The number of branches explored since the creation of the solver.
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def Branches(self) -> "int64_t": r""" The number of branches explored since the creation of the solver.""" return _pywrapcp.Solver_Branches(self) def BranchesLimit(self, branches: int64_t) ‑> operations_research::RegularLimit *-
Creates a search limit that constrains the number of branches explored in the search tree.
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def BranchesLimit(self, branches: "int64_t") -> "operations_research::RegularLimit *": r""" Creates a search limit that constrains the number of branches explored in the search tree. """ return _pywrapcp.Solver_BranchesLimit(self, branches) def CheckAssignment(self, solution: Assignment) ‑> bool-
Checks whether the given assignment satisfies all relevant constraints.
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def CheckAssignment(self, solution: "Assignment") -> "bool": r""" Checks whether the given assignment satisfies all relevant constraints.""" return _pywrapcp.Solver_CheckAssignment(self, solution) def CheckConstraint(self, ct: Constraint) ‑> bool-
Checks whether adding this constraint will lead to an immediate failure. It will return false if the model is already inconsistent, or if adding the constraint makes it inconsistent.
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def CheckConstraint(self, ct: "Constraint") -> "bool": r""" Checks whether adding this constraint will lead to an immediate failure. It will return false if the model is already inconsistent, or if adding the constraint makes it inconsistent. """ return _pywrapcp.Solver_CheckConstraint(self, ct) def Circuit(self, nexts: std::vector< operations_research::IntVar * > const &) ‑> operations_research::Constraint *-
Force the "nexts" variable to create a complete Hamiltonian path.
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def Circuit(self, nexts: "std::vector< operations_research::IntVar * > const &") -> "operations_research::Constraint *": r""" Force the "nexts" variable to create a complete Hamiltonian path.""" return _pywrapcp.Solver_Circuit(self, nexts) def ClosureDemon(self, closure: operations_research::Solver::Closure) ‑> operations_research::Demon *-
Creates a demon from a closure.
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def ClosureDemon(self, closure: "operations_research::Solver::Closure") -> "operations_research::Demon *": r""" Creates a demon from a closure.""" return _pywrapcp.Solver_ClosureDemon(self, closure) def Compose(self, dbs: std::vector< operations_research::DecisionBuilder * > const &) ‑> operations_research::DecisionBuilder *-
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def Compose(self, dbs: "std::vector< operations_research::DecisionBuilder * > const &") -> "operations_research::DecisionBuilder *": return _pywrapcp.Solver_Compose(self, dbs) def ConcatenateOperators(self, *args) ‑> operations_research::LocalSearchOperator *-
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def ConcatenateOperators(self, *args) -> "operations_research::LocalSearchOperator *": return _pywrapcp.Solver_ConcatenateOperators(self, *args) def ConditionalExpression(self, condition: IntVar, expr: IntExpr, unperformed_value: int64_t) ‑> operations_research::IntExpr *-
Conditional Expr condition ? expr : unperformed_value
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def ConditionalExpression(self, condition: "IntVar", expr: "IntExpr", unperformed_value: "int64_t") -> "operations_research::IntExpr *": r""" Conditional Expr condition ? expr : unperformed_value""" return _pywrapcp.Solver_ConditionalExpression(self, condition, expr, unperformed_value) def ConstantRestart(self, frequency: int) ‑> operations_research::SearchMonitor *-
This search monitor will restart the search periodically after 'frequency' failures.
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def ConstantRestart(self, frequency: "int") -> "operations_research::SearchMonitor *": r""" This search monitor will restart the search periodically after 'frequency' failures. """ return _pywrapcp.Solver_ConstantRestart(self, frequency) def ConstraintAdder(self, ct: Constraint) ‑> operations_research::DecisionBuilder *-
Returns a decision builder that will add the given constraint to the model.
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def ConstraintAdder(self, ct: "Constraint") -> "operations_research::DecisionBuilder *": r""" Returns a decision builder that will add the given constraint to the model. """ return _pywrapcp.Solver_ConstraintAdder(self, ct) def ConstraintInitialPropagateCallback(self, ct: Constraint) ‑> operations_research::Demon *-
This method is a specialized case of the MakeConstraintDemon method to call the InitiatePropagate of the constraint 'ct'.
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def ConstraintInitialPropagateCallback(self, ct: "Constraint") -> "operations_research::Demon *": r""" This method is a specialized case of the MakeConstraintDemon method to call the InitiatePropagate of the constraint 'ct'. """ return _pywrapcp.Solver_ConstraintInitialPropagateCallback(self, ct) def Constraints(self) ‑> int-
Counts the number of constraints that have been added to the solver before the search.
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def Constraints(self) -> "int": r""" Counts the number of constraints that have been added to the solver before the search. """ return _pywrapcp.Solver_Constraints(self) def ConvexPiecewiseExpr(self, expr: IntExpr, early_cost: int64_t, early_date: int64_t, late_date: int64_t, late_cost: int64_t) ‑> operations_research::IntExpr *-
Convex piecewise function.
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def ConvexPiecewiseExpr(self, expr: "IntExpr", early_cost: "int64_t", early_date: "int64_t", late_date: "int64_t", late_cost: "int64_t") -> "operations_research::IntExpr *": r""" Convex piecewise function.""" return _pywrapcp.Solver_ConvexPiecewiseExpr(self, expr, early_cost, early_date, late_date, late_cost) def Count(self, *args) ‑> operations_research::Constraint *-
Overload 1: |{i | vars[i] == value}| == max_count
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Overload 2: |{i | vars[i] == value}| == max_count
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def Count(self, *args) -> "operations_research::Constraint *": r""" *Overload 1:* |{i | vars[i] == value}| == max_count | *Overload 2:* |{i | vars[i] == value}| == max_count """ return _pywrapcp.Solver_Count(self, *args) def Cover(self, vars: std::vector< operations_research::IntervalVar * > const &, target_var: IntervalVar) ‑> operations_research::Constraint *-
This constraint states that the target_var is the convex hull of the intervals. If none of the interval variables is performed, then the target var is unperformed too. Also, if the target variable is unperformed, then all the intervals variables are unperformed too.
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def Cover(self, vars: "std::vector< operations_research::IntervalVar * > const &", target_var: "IntervalVar") -> "operations_research::Constraint *": r""" This constraint states that the target_var is the convex hull of the intervals. If none of the interval variables is performed, then the target var is unperformed too. Also, if the target variable is unperformed, then all the intervals variables are unperformed too. """ return _pywrapcp.Solver_Cover(self, vars, target_var) def Cumulative(self, *args) ‑> operations_research::Constraint *-
Overload 1: This constraint forces that, for any integer t, the sum of the demands corresponding to an interval containing t does not exceed the given capacity.
Intervals and demands should be vectors of equal size.
Demands should only contain non-negative values. Zero values are supported, and the corresponding intervals are filtered out, as they neither impact nor are impacted by this constraint.
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Overload 2: This constraint forces that, for any integer t, the sum of the demands corresponding to an interval containing t does not exceed the given capacity.
Intervals and demands should be vectors of equal size.
Demands should only contain non-negative values. Zero values are supported, and the corresponding intervals are filtered out, as they neither impact nor are impacted by this constraint.
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Overload 3: This constraint forces that, for any integer t, the sum of the demands corresponding to an interval containing t does not exceed the given capacity.
Intervals and demands should be vectors of equal size.
Demands should only contain non-negative values. Zero values are supported, and the corresponding intervals are filtered out, as they neither impact nor are impacted by this constraint.
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Overload 4: This constraint enforces that, for any integer t, the sum of the demands corresponding to an interval containing t does not exceed the given capacity.
Intervals and demands should be vectors of equal size.
Demands should only contain non-negative values. Zero values are supported, and the corresponding intervals are filtered out, as they neither impact nor are impacted by this constraint.
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Overload 5: This constraint enforces that, for any integer t, the sum of demands corresponding to an interval containing t does not exceed the given capacity.
Intervals and demands should be vectors of equal size.
Demands should be positive.
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Overload 6: This constraint enforces that, for any integer t, the sum of demands corresponding to an interval containing t does not exceed the given capacity.
Intervals and demands should be vectors of equal size.
Demands should be positive.
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def Cumulative(self, *args) -> "operations_research::Constraint *": r""" *Overload 1:* This constraint forces that, for any integer t, the sum of the demands corresponding to an interval containing t does not exceed the given capacity. Intervals and demands should be vectors of equal size. Demands should only contain non-negative values. Zero values are supported, and the corresponding intervals are filtered out, as they neither impact nor are impacted by this constraint. | *Overload 2:* This constraint forces that, for any integer t, the sum of the demands corresponding to an interval containing t does not exceed the given capacity. Intervals and demands should be vectors of equal size. Demands should only contain non-negative values. Zero values are supported, and the corresponding intervals are filtered out, as they neither impact nor are impacted by this constraint. | *Overload 3:* This constraint forces that, for any integer t, the sum of the demands corresponding to an interval containing t does not exceed the given capacity. Intervals and demands should be vectors of equal size. Demands should only contain non-negative values. Zero values are supported, and the corresponding intervals are filtered out, as they neither impact nor are impacted by this constraint. | *Overload 4:* This constraint enforces that, for any integer t, the sum of the demands corresponding to an interval containing t does not exceed the given capacity. Intervals and demands should be vectors of equal size. Demands should only contain non-negative values. Zero values are supported, and the corresponding intervals are filtered out, as they neither impact nor are impacted by this constraint. | *Overload 5:* This constraint enforces that, for any integer t, the sum of demands corresponding to an interval containing t does not exceed the given capacity. Intervals and demands should be vectors of equal size. Demands should be positive. | *Overload 6:* This constraint enforces that, for any integer t, the sum of demands corresponding to an interval containing t does not exceed the given capacity. Intervals and demands should be vectors of equal size. Demands should be positive. """ return _pywrapcp.Solver_Cumulative(self, *args) def CustomLimit(self, limiter: std::function< bool () >) ‑> operations_research::SearchLimit *-
Callback-based search limit. Search stops when limiter returns true; if this happens at a leaf the corresponding solution will be rejected.
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def CustomLimit(self, limiter: "std::function< bool () >") -> "operations_research::SearchLimit *": r""" Callback-based search limit. Search stops when limiter returns true; if this happens at a leaf the corresponding solution will be rejected. """ return _pywrapcp.Solver_CustomLimit(self, limiter) def Decision(self, apply: operations_research::Solver::Action, refute: operations_research::Solver::Action) ‑> operations_research::Decision *-
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def Decision(self, apply: "operations_research::Solver::Action", refute: "operations_research::Solver::Action") -> "operations_research::Decision *": return _pywrapcp.Solver_Decision(self, apply, refute) def DecisionBuilderFromAssignment(self, assignment: Assignment, db: DecisionBuilder, vars: std::vector< operations_research::IntVar * > const &) ‑> operations_research::DecisionBuilder *-
Returns a decision builder for which the left-most leaf corresponds to assignment, the rest of the tree being explored using 'db'.
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def DecisionBuilderFromAssignment(self, assignment: "Assignment", db: "DecisionBuilder", vars: "std::vector< operations_research::IntVar * > const &") -> "operations_research::DecisionBuilder *": r""" Returns a decision builder for which the left-most leaf corresponds to assignment, the rest of the tree being explored using 'db'. """ return _pywrapcp.Solver_DecisionBuilderFromAssignment(self, assignment, db, vars) def DefaultPhase(self, *args) ‑> operations_research::DecisionBuilder *-
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def DefaultPhase(self, *args) -> "operations_research::DecisionBuilder *": return _pywrapcp.Solver_DefaultPhase(self, *args) def DelayedConstraintInitialPropagateCallback(self, ct: Constraint) ‑> operations_research::Demon *-
This method is a specialized case of the MakeConstraintDemon method to call the InitiatePropagate of the constraint 'ct' with low priority.
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def DelayedConstraintInitialPropagateCallback(self, ct: "Constraint") -> "operations_research::Demon *": r""" This method is a specialized case of the MakeConstraintDemon method to call the InitiatePropagate of the constraint 'ct' with low priority. """ return _pywrapcp.Solver_DelayedConstraintInitialPropagateCallback(self, ct) def DelayedPathCumul(self, nexts: std::vector< operations_research::IntVar * > const &, active: std::vector< operations_research::IntVar * > const &, cumuls: std::vector< operations_research::IntVar * > const &, transits: std::vector< operations_research::IntVar * > const &) ‑> operations_research::Constraint *-
Delayed version of the same constraint: propagation on the nexts variables is delayed until all constraints have propagated.
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def DelayedPathCumul(self, nexts: "std::vector< operations_research::IntVar * > const &", active: "std::vector< operations_research::IntVar * > const &", cumuls: "std::vector< operations_research::IntVar * > const &", transits: "std::vector< operations_research::IntVar * > const &") -> "operations_research::Constraint *": r""" Delayed version of the same constraint: propagation on the nexts variables is delayed until all constraints have propagated. """ return _pywrapcp.Solver_DelayedPathCumul(self, nexts, active, cumuls, transits) def Deviation(self, vars: std::vector< operations_research::IntVar * > const &, deviation_var: IntVar, total_sum: int64_t) ‑> operations_research::Constraint *-
Deviation constraint: sum_i |n * vars[i] - total_sum| <= deviation_var and sum_i vars[i] == total_sum n = #vars
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def Deviation(self, vars: "std::vector< operations_research::IntVar * > const &", deviation_var: "IntVar", total_sum: "int64_t") -> "operations_research::Constraint *": r""" Deviation constraint: sum_i |n * vars[i] - total_sum| <= deviation_var and sum_i vars[i] == total_sum n = #vars """ return _pywrapcp.Solver_Deviation(self, vars, deviation_var, total_sum) def DisjunctiveConstraint(self, intervals: std::vector< operations_research::IntervalVar * > const &, name: std::string const &) ‑> operations_research::DisjunctiveConstraint *-
This constraint forces all interval vars into an non-overlapping sequence. Intervals with zero duration can be scheduled anywhere.
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def DisjunctiveConstraint(self, intervals: "std::vector< operations_research::IntervalVar * > const &", name: "std::string const &") -> "operations_research::DisjunctiveConstraint *": r""" This constraint forces all interval vars into an non-overlapping sequence. Intervals with zero duration can be scheduled anywhere. """ return _pywrapcp.Solver_DisjunctiveConstraint(self, intervals, name) def Distribute(self, *args) ‑> operations_research::Constraint *-
Overload 1: Aggregated version of count: |{i | v[i] == values[j]}| == cards[j]
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Overload 2: Aggregated version of count: |{i | v[i] == values[j]}| == cards[j]
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Overload 3: Aggregated version of count: |{i | v[i] == j}| == cards[j]
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Overload 4: Aggregated version of count with bounded cardinalities: forall j in 0 .. card_size - 1: card_min <= |{i | v[i] == j}| <= card_max
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Overload 5: Aggregated version of count with bounded cardinalities: forall j in 0 .. card_size - 1: card_min[j] <= |{i | v[i] == j}| <= card_max[j]
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Overload 6: Aggregated version of count with bounded cardinalities: forall j in 0 .. card_size - 1: card_min[j] <= |{i | v[i] == j}| <= card_max[j]
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Overload 7: Aggregated version of count with bounded cardinalities: forall j in 0 .. card_size - 1: card_min[j] <= |{i | v[i] == values[j]}| <= card_max[j]
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Overload 8: Aggregated version of count with bounded cardinalities: forall j in 0 .. card_size - 1: card_min[j] <= |{i | v[i] == values[j]}| <= card_max[j]
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def Distribute(self, *args) -> "operations_research::Constraint *": r""" *Overload 1:* Aggregated version of count: |{i | v[i] == values[j]}| == cards[j] | *Overload 2:* Aggregated version of count: |{i | v[i] == values[j]}| == cards[j] | *Overload 3:* Aggregated version of count: |{i | v[i] == j}| == cards[j] | *Overload 4:* Aggregated version of count with bounded cardinalities: forall j in 0 .. card_size - 1: card_min <= |{i | v[i] == j}| <= card_max | *Overload 5:* Aggregated version of count with bounded cardinalities: forall j in 0 .. card_size - 1: card_min[j] <= |{i | v[i] == j}| <= card_max[j] | *Overload 6:* Aggregated version of count with bounded cardinalities: forall j in 0 .. card_size - 1: card_min[j] <= |{i | v[i] == j}| <= card_max[j] | *Overload 7:* Aggregated version of count with bounded cardinalities: forall j in 0 .. card_size - 1: card_min[j] <= |{i | v[i] == values[j]}| <= card_max[j] | *Overload 8:* Aggregated version of count with bounded cardinalities: forall j in 0 .. card_size - 1: card_min[j] <= |{i | v[i] == values[j]}| <= card_max[j] """ return _pywrapcp.Solver_Distribute(self, *args) def Element(self, *args) ‑> operations_research::IntExpr *-
Overload 1: values[index]
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Overload 2: values[index]
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Overload 3: Function-based element. The constraint takes ownership of the callback. The callback must be able to cope with any possible value in the domain of 'index' (potentially negative ones too).
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Overload 4: 2D version of function-based element expression, values(expr1, expr2).
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Overload 5: vars[expr]
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def Element(self, *args) -> "operations_research::IntExpr *": r""" *Overload 1:* values[index] | *Overload 2:* values[index] | *Overload 3:* Function-based element. The constraint takes ownership of the callback. The callback must be able to cope with any possible value in the domain of 'index' (potentially negative ones too). | *Overload 4:* 2D version of function-based element expression, values(expr1, expr2). | *Overload 5:* vars[expr] """ return _pywrapcp.Solver_Element(self, *args) def ElementEquality(self, *args) ‑> operations_research::Constraint *-
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def ElementEquality(self, *args) -> "operations_research::Constraint *": return _pywrapcp.Solver_ElementEquality(self, *args) def ElementFunction(self, values: std::function< int64_t (int64_t) >, index: IntVar) ‑> operations_research::IntExpr *-
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def ElementFunction(self, values: "std::function< int64_t (int64_t) >", index: "IntVar") -> "operations_research::IntExpr *": return _pywrapcp.Solver_ElementFunction(self, values, index) def EndSearch(self) ‑> void-
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def EndSearch(self) -> "void": return _pywrapcp.Solver_EndSearch(self) def EvalEvalStrPhase(self, vars: std::vector< operations_research::IntVar * > const &, evaluator: operations_research::Solver::IndexEvaluator2, str: operations_research::Solver::EvaluatorStrategy) ‑> operations_research::DecisionBuilder *-
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def EvalEvalStrPhase(self, vars: "std::vector< operations_research::IntVar * > const &", evaluator: "operations_research::Solver::IndexEvaluator2", str: "operations_research::Solver::EvaluatorStrategy") -> "operations_research::DecisionBuilder *": return _pywrapcp.Solver_EvalEvalStrPhase(self, vars, evaluator, str) def EvalEvalStrTieBreakPhase(self, vars: std::vector< operations_research::IntVar * > const &, evaluator: operations_research::Solver::IndexEvaluator2, tie_breaker: operations_research::Solver::IndexEvaluator1, str: operations_research::Solver::EvaluatorStrategy) ‑> operations_research::DecisionBuilder *-
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def EvalEvalStrTieBreakPhase(self, vars: "std::vector< operations_research::IntVar * > const &", evaluator: "operations_research::Solver::IndexEvaluator2", tie_breaker: "operations_research::Solver::IndexEvaluator1", str: "operations_research::Solver::EvaluatorStrategy") -> "operations_research::DecisionBuilder *": return _pywrapcp.Solver_EvalEvalStrTieBreakPhase(self, vars, evaluator, tie_breaker, str) def Fail(self) ‑> void-
Abandon the current branch in the search tree. A backtrack will follow.
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def Fail(self) -> "void": r""" Abandon the current branch in the search tree. A backtrack will follow.""" return _pywrapcp.Solver_Fail(self) def FailDecision(self) ‑> operations_research::Decision *-
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def FailDecision(self) -> "operations_research::Decision *": return _pywrapcp.Solver_FailDecision(self) def FailStamp(self) ‑> uint64_t-
The fail_stamp() is incremented after each backtrack.
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def FailStamp(self) -> "uint64_t": r""" The fail_stamp() is incremented after each backtrack.""" return _pywrapcp.Solver_FailStamp(self) def Failures(self) ‑> int64_t-
The number of failures encountered since the creation of the solver.
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def Failures(self) -> "int64_t": r""" The number of failures encountered since the creation of the solver.""" return _pywrapcp.Solver_Failures(self) def FailuresLimit(self, failures: int64_t) ‑> operations_research::RegularLimit *-
Creates a search limit that constrains the number of failures that can happen when exploring the search tree.
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def FailuresLimit(self, failures: "int64_t") -> "operations_research::RegularLimit *": r""" Creates a search limit that constrains the number of failures that can happen when exploring the search tree. """ return _pywrapcp.Solver_FailuresLimit(self, failures) def FalseConstraint(self, *args) ‑> operations_research::Constraint *-
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def FalseConstraint(self, *args) -> "operations_research::Constraint *": return _pywrapcp.Solver_FalseConstraint(self, *args) def FinishCurrentSearch(self) ‑> void-
Tells the solver to kill or restart the current search.
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def FinishCurrentSearch(self) -> "void": r""" Tells the solver to kill or restart the current search.""" return _pywrapcp.Solver_FinishCurrentSearch(self) def FirstSolutionCollector(self, *args) ‑> operations_research::SolutionCollector *-
Overload 1: Collect the first solution of the search.
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Overload 2: Collect the first solution of the search. The variables will need to be added later.
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def FirstSolutionCollector(self, *args) -> "operations_research::SolutionCollector *": r""" *Overload 1:* Collect the first solution of the search. | *Overload 2:* Collect the first solution of the search. The variables will need to be added later. """ return _pywrapcp.Solver_FirstSolutionCollector(self, *args) def FixedDurationEndSyncedOnEndIntervalVar(self, interval_var: IntervalVar, duration: int64_t, offset: int64_t) ‑> operations_research::IntervalVar *-
Creates an interval var with a fixed duration whose end is synchronized with the end of another interval, with a given offset. The performed status is also in sync with the performed status of the given interval variable.
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def FixedDurationEndSyncedOnEndIntervalVar(self, interval_var: "IntervalVar", duration: "int64_t", offset: "int64_t") -> "operations_research::IntervalVar *": r""" Creates an interval var with a fixed duration whose end is synchronized with the end of another interval, with a given offset. The performed status is also in sync with the performed status of the given interval variable. """ return _pywrapcp.Solver_FixedDurationEndSyncedOnEndIntervalVar(self, interval_var, duration, offset) def FixedDurationEndSyncedOnStartIntervalVar(self, interval_var: IntervalVar, duration: int64_t, offset: int64_t) ‑> operations_research::IntervalVar *-
Creates an interval var with a fixed duration whose end is synchronized with the start of another interval, with a given offset. The performed status is also in sync with the performed status of the given interval variable.
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def FixedDurationEndSyncedOnStartIntervalVar(self, interval_var: "IntervalVar", duration: "int64_t", offset: "int64_t") -> "operations_research::IntervalVar *": r""" Creates an interval var with a fixed duration whose end is synchronized with the start of another interval, with a given offset. The performed status is also in sync with the performed status of the given interval variable. """ return _pywrapcp.Solver_FixedDurationEndSyncedOnStartIntervalVar(self, interval_var, duration, offset) def FixedDurationIntervalVar(self, *args) ‑> operations_research::IntervalVar *-
Overload 1: Creates an interval var with a fixed duration. The duration must be greater than 0. If optional is true, then the interval can be performed or unperformed. If optional is false, then the interval is always performed.
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Overload 2: Creates a performed interval var with a fixed duration. The duration must be greater than 0.
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Overload 3: Creates an interval var with a fixed duration, and performed_variable. The duration must be greater than 0.
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def FixedDurationIntervalVar(self, *args) -> "operations_research::IntervalVar *": r""" *Overload 1:* Creates an interval var with a fixed duration. The duration must be greater than 0. If optional is true, then the interval can be performed or unperformed. If optional is false, then the interval is always performed. | *Overload 2:* Creates a performed interval var with a fixed duration. The duration must be greater than 0. | *Overload 3:* Creates an interval var with a fixed duration, and performed_variable. The duration must be greater than 0. """ return _pywrapcp.Solver_FixedDurationIntervalVar(self, *args) def FixedDurationStartSyncedOnEndIntervalVar(self, interval_var: IntervalVar, duration: int64_t, offset: int64_t) ‑> operations_research::IntervalVar *-
Creates an interval var with a fixed duration whose start is synchronized with the end of another interval, with a given offset. The performed status is also in sync with the performed status of the given interval variable.
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def FixedDurationStartSyncedOnEndIntervalVar(self, interval_var: "IntervalVar", duration: "int64_t", offset: "int64_t") -> "operations_research::IntervalVar *": r""" Creates an interval var with a fixed duration whose start is synchronized with the end of another interval, with a given offset. The performed status is also in sync with the performed status of the given interval variable. """ return _pywrapcp.Solver_FixedDurationStartSyncedOnEndIntervalVar(self, interval_var, duration, offset) def FixedDurationStartSyncedOnStartIntervalVar(self, interval_var: IntervalVar, duration: int64_t, offset: int64_t) ‑> operations_research::IntervalVar *-
Creates an interval var with a fixed duration whose start is synchronized with the start of another interval, with a given offset. The performed status is also in sync with the performed status of the given interval variable.
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def FixedDurationStartSyncedOnStartIntervalVar(self, interval_var: "IntervalVar", duration: "int64_t", offset: "int64_t") -> "operations_research::IntervalVar *": r""" Creates an interval var with a fixed duration whose start is synchronized with the start of another interval, with a given offset. The performed status is also in sync with the performed status of the given interval variable. """ return _pywrapcp.Solver_FixedDurationStartSyncedOnStartIntervalVar(self, interval_var, duration, offset) def FixedInterval(self, start: int64_t, duration: int64_t, name: std::string const &) ‑> operations_research::IntervalVar *-
Creates a fixed and performed interval.
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def FixedInterval(self, start: "int64_t", duration: "int64_t", name: "std::string const &") -> "operations_research::IntervalVar *": r""" Creates a fixed and performed interval.""" return _pywrapcp.Solver_FixedInterval(self, start, duration, name) def GuidedLocalSearch(self, *args) ‑> operations_research::SearchMonitor *-
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def GuidedLocalSearch(self, *args) -> "operations_research::SearchMonitor *": return _pywrapcp.Solver_GuidedLocalSearch(self, *args) def IndexExpression(self, vars: std::vector< operations_research::IntVar * > const &, value: int64_t) ‑> operations_research::IntExpr *-
Returns the expression expr such that vars[expr] == value. It assumes that vars are all different.
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def IndexExpression(self, vars: "std::vector< operations_research::IntVar * > const &", value: "int64_t") -> "operations_research::IntExpr *": r""" Returns the expression expr such that vars[expr] == value. It assumes that vars are all different. """ return _pywrapcp.Solver_IndexExpression(self, vars, value) def IndexOfConstraint(self, vars: std::vector< operations_research::IntVar * > const &, index: IntVar, target: int64_t) ‑> operations_research::Constraint *-
This constraint is a special case of the element constraint with an array of integer variables, where the variables are all different and the index variable is constrained such that vars[index] == target.
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def IndexOfConstraint(self, vars: "std::vector< operations_research::IntVar * > const &", index: "IntVar", target: "int64_t") -> "operations_research::Constraint *": r""" This constraint is a special case of the element constraint with an array of integer variables, where the variables are all different and the index variable is constrained such that vars[index] == target. """ return _pywrapcp.Solver_IndexOfConstraint(self, vars, index, target) def IntConst(self, *args) ‑> operations_research::IntVar *-
Overload 1: IntConst will create a constant expression.
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Overload 2: IntConst will create a constant expression.
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def IntConst(self, *args) -> "operations_research::IntVar *": r""" *Overload 1:* IntConst will create a constant expression. | *Overload 2:* IntConst will create a constant expression. """ return _pywrapcp.Solver_IntConst(self, *args) def IntVar(self, *args) ‑> operations_research::IntVar *-
Overload 1: MakeIntVar will create the best range based int var for the bounds given.
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Overload 2: MakeIntVar will create a variable with the given sparse domain.
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Overload 3: MakeIntVar will create a variable with the given sparse domain.
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Overload 4: MakeIntVar will create the best range based int var for the bounds given.
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Overload 5: MakeIntVar will create a variable with the given sparse domain.
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Overload 6: MakeIntVar will create a variable with the given sparse domain.
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def IntVar(self, *args) -> "operations_research::IntVar *": r""" *Overload 1:* MakeIntVar will create the best range based int var for the bounds given. | *Overload 2:* MakeIntVar will create a variable with the given sparse domain. | *Overload 3:* MakeIntVar will create a variable with the given sparse domain. | *Overload 4:* MakeIntVar will create the best range based int var for the bounds given. | *Overload 5:* MakeIntVar will create a variable with the given sparse domain. | *Overload 6:* MakeIntVar will create a variable with the given sparse domain. """ return _pywrapcp.Solver_IntVar(self, *args) def IntervalRelaxedMax(self, interval_var: IntervalVar) ‑> operations_research::IntervalVar *-
Creates and returns an interval variable that wraps around the given one, relaxing the max start and end. Relaxing means making unbounded when optional. If the variable is non optional, this method returns interval_var.
More precisely, such an interval variable behaves as follows: When the underlying must be performed, the returned interval variable behaves exactly as the underlying; When the underlying may or may not be performed, the returned interval variable behaves like the underlying, except that it is unbounded on the max side; When the underlying cannot be performed, the returned interval variable is of duration 0 and must be performed in an interval unbounded on both sides.
This is very useful for implementing propagators that may only modify the start min or end min.
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def IntervalRelaxedMax(self, interval_var: "IntervalVar") -> "operations_research::IntervalVar *": r""" Creates and returns an interval variable that wraps around the given one, relaxing the max start and end. Relaxing means making unbounded when optional. If the variable is non optional, this method returns interval_var. More precisely, such an interval variable behaves as follows: When the underlying must be performed, the returned interval variable behaves exactly as the underlying; When the underlying may or may not be performed, the returned interval variable behaves like the underlying, except that it is unbounded on the max side; When the underlying cannot be performed, the returned interval variable is of duration 0 and must be performed in an interval unbounded on both sides. This is very useful for implementing propagators that may only modify the start min or end min. """ return _pywrapcp.Solver_IntervalRelaxedMax(self, interval_var) def IntervalRelaxedMin(self, interval_var: IntervalVar) ‑> operations_research::IntervalVar *-
Creates and returns an interval variable that wraps around the given one, relaxing the min start and end. Relaxing means making unbounded when optional. If the variable is non-optional, this method returns interval_var.
More precisely, such an interval variable behaves as follows: When the underlying must be performed, the returned interval variable behaves exactly as the underlying; When the underlying may or may not be performed, the returned interval variable behaves like the underlying, except that it is unbounded on the min side; When the underlying cannot be performed, the returned interval variable is of duration 0 and must be performed in an interval unbounded on both sides.
This is very useful to implement propagators that may only modify the start max or end max.
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def IntervalRelaxedMin(self, interval_var: "IntervalVar") -> "operations_research::IntervalVar *": r""" Creates and returns an interval variable that wraps around the given one, relaxing the min start and end. Relaxing means making unbounded when optional. If the variable is non-optional, this method returns interval_var. More precisely, such an interval variable behaves as follows: When the underlying must be performed, the returned interval variable behaves exactly as the underlying; When the underlying may or may not be performed, the returned interval variable behaves like the underlying, except that it is unbounded on the min side; When the underlying cannot be performed, the returned interval variable is of duration 0 and must be performed in an interval unbounded on both sides. This is very useful to implement propagators that may only modify the start max or end max. """ return _pywrapcp.Solver_IntervalRelaxedMin(self, interval_var) def IntervalVar(self, start_min: int64_t, start_max: int64_t, duration_min: int64_t, duration_max: int64_t, end_min: int64_t, end_max: int64_t, optional: bool, name: std::string const &) ‑> operations_research::IntervalVar *-
Creates an interval var by specifying the bounds on start, duration, and end.
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def IntervalVar(self, start_min: "int64_t", start_max: "int64_t", duration_min: "int64_t", duration_max: "int64_t", end_min: "int64_t", end_max: "int64_t", optional: "bool", name: "std::string const &") -> "operations_research::IntervalVar *": r""" Creates an interval var by specifying the bounds on start, duration, and end. """ return _pywrapcp.Solver_IntervalVar(self, start_min, start_max, duration_min, duration_max, end_min, end_max, optional, name) def InversePermutationConstraint(self, left: std::vector< operations_research::IntVar * > const &, right: std::vector< operations_research::IntVar * > const &) ‑> operations_research::Constraint *-
Creates a constraint that enforces that 'left' and 'right' both represent permutations of [0..left.size()-1], and that 'right' is the inverse permutation of 'left', i.e. for all i in [0..left.size()-1], right[left[i]] = i.
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def InversePermutationConstraint(self, left: "std::vector< operations_research::IntVar * > const &", right: "std::vector< operations_research::IntVar * > const &") -> "operations_research::Constraint *": r""" Creates a constraint that enforces that 'left' and 'right' both represent permutations of [0..left.size()-1], and that 'right' is the inverse permutation of 'left', i.e. for all i in [0..left.size()-1], right[left[i]] = i. """ return _pywrapcp.Solver_InversePermutationConstraint(self, left, right) def IsBetweenCt(self, expr: IntExpr, l: int64_t, u: int64_t, b: IntVar) ‑> operations_research::Constraint *-
b == (l <= expr <= u)
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def IsBetweenCt(self, expr: "IntExpr", l: "int64_t", u: "int64_t", b: "IntVar") -> "operations_research::Constraint *": r""" b == (l <= expr <= u)""" return _pywrapcp.Solver_IsBetweenCt(self, expr, l, u, b) def IsBetweenVar(self, v: IntExpr, l: int64_t, u: int64_t) ‑> operations_research::IntVar *-
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def IsBetweenVar(self, v: "IntExpr", l: "int64_t", u: "int64_t") -> "operations_research::IntVar *": return _pywrapcp.Solver_IsBetweenVar(self, v, l, u) def IsDifferentCstCt(self, var: IntExpr, value: int64_t, boolvar: IntVar) ‑> operations_research::Constraint *-
boolvar == (var != value)
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def IsDifferentCstCt(self, var: "IntExpr", value: "int64_t", boolvar: "IntVar") -> "operations_research::Constraint *": r""" boolvar == (var != value)""" return _pywrapcp.Solver_IsDifferentCstCt(self, var, value, boolvar) def IsDifferentCstVar(self, var: IntExpr, value: int64_t) ‑> operations_research::IntVar *-
status var of (var != value)
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def IsDifferentCstVar(self, var: "IntExpr", value: "int64_t") -> "operations_research::IntVar *": r""" status var of (var != value)""" return _pywrapcp.Solver_IsDifferentCstVar(self, var, value) def IsDifferentCt(self, v1: IntExpr, v2: IntExpr, b: IntVar) ‑> operations_research::Constraint *-
b == (v1 != v2)
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def IsDifferentCt(self, v1: "IntExpr", v2: "IntExpr", b: "IntVar") -> "operations_research::Constraint *": r""" b == (v1 != v2)""" return _pywrapcp.Solver_IsDifferentCt(self, v1, v2, b) def IsDifferentVar(self, v1: IntExpr, v2: IntExpr) ‑> operations_research::IntVar *-
status var of (v1 != v2)
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def IsDifferentVar(self, v1: "IntExpr", v2: "IntExpr") -> "operations_research::IntVar *": r""" status var of (v1 != v2)""" return _pywrapcp.Solver_IsDifferentVar(self, v1, v2) def IsEqualCstCt(self, var: IntExpr, value: int64_t, boolvar: IntVar) ‑> operations_research::Constraint *-
boolvar == (var == value)
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def IsEqualCstCt(self, var: "IntExpr", value: "int64_t", boolvar: "IntVar") -> "operations_research::Constraint *": r""" boolvar == (var == value)""" return _pywrapcp.Solver_IsEqualCstCt(self, var, value, boolvar) def IsEqualCstVar(self, var: IntExpr, value: int64_t) ‑> operations_research::IntVar *-
status var of (var == value)
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def IsEqualCstVar(self, var: "IntExpr", value: "int64_t") -> "operations_research::IntVar *": r""" status var of (var == value)""" return _pywrapcp.Solver_IsEqualCstVar(self, var, value) def IsEqualCt(self, v1: IntExpr, v2: IntExpr, b: IntVar) ‑> operations_research::Constraint *-
b == (v1 == v2)
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def IsEqualCt(self, v1: "IntExpr", v2: "IntExpr", b: "IntVar") -> "operations_research::Constraint *": r""" b == (v1 == v2)""" return _pywrapcp.Solver_IsEqualCt(self, v1, v2, b) def IsEqualVar(self, v1: IntExpr, v2: IntExpr) ‑> operations_research::IntVar *-
status var of (v1 == v2)
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def IsEqualVar(self, v1: "IntExpr", v2: "IntExpr") -> "operations_research::IntVar *": r""" status var of (v1 == v2)""" return _pywrapcp.Solver_IsEqualVar(self, v1, v2) def IsGreaterCstCt(self, v: IntExpr, c: int64_t, b: IntVar) ‑> operations_research::Constraint *-
b == (v > c)
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def IsGreaterCstCt(self, v: "IntExpr", c: "int64_t", b: "IntVar") -> "operations_research::Constraint *": r""" b == (v > c)""" return _pywrapcp.Solver_IsGreaterCstCt(self, v, c, b) def IsGreaterCstVar(self, var: IntExpr, value: int64_t) ‑> operations_research::IntVar *-
status var of (var > value)
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def IsGreaterCstVar(self, var: "IntExpr", value: "int64_t") -> "operations_research::IntVar *": r""" status var of (var > value)""" return _pywrapcp.Solver_IsGreaterCstVar(self, var, value) def IsGreaterCt(self, left: IntExpr, right: IntExpr, b: IntVar) ‑> operations_research::Constraint *-
b == (left > right)
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def IsGreaterCt(self, left: "IntExpr", right: "IntExpr", b: "IntVar") -> "operations_research::Constraint *": r""" b == (left > right)""" return _pywrapcp.Solver_IsGreaterCt(self, left, right, b) def IsGreaterOrEqualCstCt(self, var: IntExpr, value: int64_t, boolvar: IntVar) ‑> operations_research::Constraint *-
boolvar == (var >= value)
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def IsGreaterOrEqualCstCt(self, var: "IntExpr", value: "int64_t", boolvar: "IntVar") -> "operations_research::Constraint *": r""" boolvar == (var >= value)""" return _pywrapcp.Solver_IsGreaterOrEqualCstCt(self, var, value, boolvar) def IsGreaterOrEqualCstVar(self, var: IntExpr, value: int64_t) ‑> operations_research::IntVar *-
status var of (var >= value)
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def IsGreaterOrEqualCstVar(self, var: "IntExpr", value: "int64_t") -> "operations_research::IntVar *": r""" status var of (var >= value)""" return _pywrapcp.Solver_IsGreaterOrEqualCstVar(self, var, value) def IsGreaterOrEqualCt(self, left: IntExpr, right: IntExpr, b: IntVar) ‑> operations_research::Constraint *-
b == (left >= right)
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def IsGreaterOrEqualCt(self, left: "IntExpr", right: "IntExpr", b: "IntVar") -> "operations_research::Constraint *": r""" b == (left >= right)""" return _pywrapcp.Solver_IsGreaterOrEqualCt(self, left, right, b) def IsGreaterOrEqualVar(self, left: IntExpr, right: IntExpr) ‑> operations_research::IntVar *-
status var of (left >= right)
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def IsGreaterOrEqualVar(self, left: "IntExpr", right: "IntExpr") -> "operations_research::IntVar *": r""" status var of (left >= right)""" return _pywrapcp.Solver_IsGreaterOrEqualVar(self, left, right) def IsGreaterVar(self, left: IntExpr, right: IntExpr) ‑> operations_research::IntVar *-
status var of (left > right)
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def IsGreaterVar(self, left: "IntExpr", right: "IntExpr") -> "operations_research::IntVar *": r""" status var of (left > right)""" return _pywrapcp.Solver_IsGreaterVar(self, left, right) def IsLessCstCt(self, v: IntExpr, c: int64_t, b: IntVar) ‑> operations_research::Constraint *-
b == (v < c)
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def IsLessCstCt(self, v: "IntExpr", c: "int64_t", b: "IntVar") -> "operations_research::Constraint *": r""" b == (v < c)""" return _pywrapcp.Solver_IsLessCstCt(self, v, c, b) def IsLessCstVar(self, var: IntExpr, value: int64_t) ‑> operations_research::IntVar *-
status var of (var < value)
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def IsLessCstVar(self, var: "IntExpr", value: "int64_t") -> "operations_research::IntVar *": r""" status var of (var < value)""" return _pywrapcp.Solver_IsLessCstVar(self, var, value) def IsLessCt(self, left: IntExpr, right: IntExpr, b: IntVar) ‑> operations_research::Constraint *-
b == (left < right)
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def IsLessCt(self, left: "IntExpr", right: "IntExpr", b: "IntVar") -> "operations_research::Constraint *": r""" b == (left < right)""" return _pywrapcp.Solver_IsLessCt(self, left, right, b) def IsLessOrEqualCstCt(self, var: IntExpr, value: int64_t, boolvar: IntVar) ‑> operations_research::Constraint *-
boolvar == (var <= value)
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def IsLessOrEqualCstCt(self, var: "IntExpr", value: "int64_t", boolvar: "IntVar") -> "operations_research::Constraint *": r""" boolvar == (var <= value)""" return _pywrapcp.Solver_IsLessOrEqualCstCt(self, var, value, boolvar) def IsLessOrEqualCstVar(self, var: IntExpr, value: int64_t) ‑> operations_research::IntVar *-
status var of (var <= value)
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def IsLessOrEqualCstVar(self, var: "IntExpr", value: "int64_t") -> "operations_research::IntVar *": r""" status var of (var <= value)""" return _pywrapcp.Solver_IsLessOrEqualCstVar(self, var, value) def IsLessOrEqualCt(self, left: IntExpr, right: IntExpr, b: IntVar) ‑> operations_research::Constraint *-
b == (left <= right)
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def IsLessOrEqualCt(self, left: "IntExpr", right: "IntExpr", b: "IntVar") -> "operations_research::Constraint *": r""" b == (left <= right)""" return _pywrapcp.Solver_IsLessOrEqualCt(self, left, right, b) def IsLessOrEqualVar(self, left: IntExpr, right: IntExpr) ‑> operations_research::IntVar *-
status var of (left <= right)
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def IsLessOrEqualVar(self, left: "IntExpr", right: "IntExpr") -> "operations_research::IntVar *": r""" status var of (left <= right)""" return _pywrapcp.Solver_IsLessOrEqualVar(self, left, right) def IsLessVar(self, left: IntExpr, right: IntExpr) ‑> operations_research::IntVar *-
status var of (left < right)
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def IsLessVar(self, left: "IntExpr", right: "IntExpr") -> "operations_research::IntVar *": r""" status var of (left < right)""" return _pywrapcp.Solver_IsLessVar(self, left, right) def IsMemberCt(self, *args) ‑> operations_research::Constraint *-
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def IsMemberCt(self, *args) -> "operations_research::Constraint *": return _pywrapcp.Solver_IsMemberCt(self, *args) def IsMemberVar(self, *args) ‑> operations_research::IntVar *-
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def IsMemberVar(self, *args) -> "operations_research::IntVar *": return _pywrapcp.Solver_IsMemberVar(self, *args) def LastSolutionCollector(self, *args) ‑> operations_research::SolutionCollector *-
Overload 1: Collect the last solution of the search.
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Overload 2: Collect the last solution of the search. The variables will need to be added later.
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def LastSolutionCollector(self, *args) -> "operations_research::SolutionCollector *": r""" *Overload 1:* Collect the last solution of the search. | *Overload 2:* Collect the last solution of the search. The variables will need to be added later. """ return _pywrapcp.Solver_LastSolutionCollector(self, *args) def LexicalLess(self, left: std::vector< operations_research::IntVar * > const &, right: std::vector< operations_research::IntVar * > const &) ‑> operations_research::Constraint *-
Creates a constraint that enforces that left is lexicographically less than right.
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def LexicalLess(self, left: "std::vector< operations_research::IntVar * > const &", right: "std::vector< operations_research::IntVar * > const &") -> "operations_research::Constraint *": r""" Creates a constraint that enforces that left is lexicographically less than right. """ return _pywrapcp.Solver_LexicalLess(self, left, right) def LexicalLessOrEqual(self, left: std::vector< operations_research::IntVar * > const &, right: std::vector< operations_research::IntVar * > const &) ‑> operations_research::Constraint *-
Creates a constraint that enforces that left is lexicographically less than or equal to right.
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def LexicalLessOrEqual(self, left: "std::vector< operations_research::IntVar * > const &", right: "std::vector< operations_research::IntVar * > const &") -> "operations_research::Constraint *": r""" Creates a constraint that enforces that left is lexicographically less than or equal to right. """ return _pywrapcp.Solver_LexicalLessOrEqual(self, left, right) def Limit(self, *args) ‑> operations_research::SearchLimit *-
Overload 1: Limits the search with the 'time', 'branches', 'failures' and 'solutions' limits. 'smart_time_check' reduces the calls to the wall
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Overload 2: Creates a search limit from its protobuf description
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Overload 3: Creates a search limit that is reached when either of the underlying limit is reached. That is, the returned limit is more stringent than both argument limits.
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def Limit(self, *args) -> "operations_research::SearchLimit *": r""" *Overload 1:* Limits the search with the 'time', 'branches', 'failures' and 'solutions' limits. 'smart_time_check' reduces the calls to the wall | *Overload 2:* Creates a search limit from its protobuf description | *Overload 3:* Creates a search limit that is reached when either of the underlying limit is reached. That is, the returned limit is more stringent than both argument limits. """ return _pywrapcp.Solver_Limit(self, *args) def LocalSearchPhase(self, *args) ‑> operations_research::DecisionBuilder *-
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def LocalSearchPhase(self, *args) -> "operations_research::DecisionBuilder *": return _pywrapcp.Solver_LocalSearchPhase(self, *args) def LocalSearchPhaseParameters(self, *args) ‑> operations_research::LocalSearchPhaseParameters *-
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def LocalSearchPhaseParameters(self, *args) -> "operations_research::LocalSearchPhaseParameters *": return _pywrapcp.Solver_LocalSearchPhaseParameters(self, *args) def LocalSearchProfile(self) ‑> std::string-
Returns local search profiling information in a human readable format.
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def LocalSearchProfile(self) -> "std::string": r""" Returns local search profiling information in a human readable format.""" return _pywrapcp.Solver_LocalSearchProfile(self) def LubyRestart(self, scale_factor: int) ‑> operations_research::SearchMonitor *-
This search monitor will restart the search periodically. At the iteration n, it will restart after scale_factor * Luby(n) failures where Luby is the Luby Strategy (i.e. 1 1 2 1 1 2 4 1 1 2 1 1 2 4 8…).
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def LubyRestart(self, scale_factor: "int") -> "operations_research::SearchMonitor *": r""" This search monitor will restart the search periodically. At the iteration n, it will restart after scale_factor * Luby(n) failures where Luby is the Luby Strategy (i.e. 1 1 2 1 1 2 4 1 1 2 1 1 2 4 8...). """ return _pywrapcp.Solver_LubyRestart(self, scale_factor) def Max(self, *args) ‑> operations_research::IntExpr *-
Overload 1: std::max(vars)
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Overload 2: std::max(left, right)
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Overload 3: std::max(expr, value)
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Overload 4: std::max(expr, value)
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def Max(self, *args) -> "operations_research::IntExpr *": r""" *Overload 1:* std::max(vars) | *Overload 2:* std::max(left, right) | *Overload 3:* std::max(expr, value) | *Overload 4:* std::max(expr, value) """ return _pywrapcp.Solver_Max(self, *args) def MaxEquality(self, vars: std::vector< operations_research::IntVar * > const &, max_var: IntVar) ‑> operations_research::Constraint *-
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def MaxEquality(self, vars: "std::vector< operations_research::IntVar * > const &", max_var: "IntVar") -> "operations_research::Constraint *": return _pywrapcp.Solver_MaxEquality(self, vars, max_var) def Maximize(self, v: IntVar, step: int64_t) ‑> operations_research::OptimizeVar *-
Creates a maximization objective.
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def Maximize(self, v: "IntVar", step: "int64_t") -> "operations_research::OptimizeVar *": r""" Creates a maximization objective.""" return _pywrapcp.Solver_Maximize(self, v, step) def MemberCt(self, *args) ‑> operations_research::Constraint *-
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def MemberCt(self, *args) -> "operations_research::Constraint *": return _pywrapcp.Solver_MemberCt(self, *args) def Min(self, *args) ‑> operations_research::IntExpr *-
Overload 1: std::min(vars)
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Overload 2: std::min (left, right)
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Overload 3: std::min(expr, value)
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Overload 4: std::min(expr, value)
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def Min(self, *args) -> "operations_research::IntExpr *": r""" *Overload 1:* std::min(vars) | *Overload 2:* std::min (left, right) | *Overload 3:* std::min(expr, value) | *Overload 4:* std::min(expr, value) """ return _pywrapcp.Solver_Min(self, *args) def MinEquality(self, vars: std::vector< operations_research::IntVar * > const &, min_var: IntVar) ‑> operations_research::Constraint *-
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def MinEquality(self, vars: "std::vector< operations_research::IntVar * > const &", min_var: "IntVar") -> "operations_research::Constraint *": return _pywrapcp.Solver_MinEquality(self, vars, min_var) def Minimize(self, v: IntVar, step: int64_t) ‑> operations_research::OptimizeVar *-
Creates a minimization objective.
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def Minimize(self, v: "IntVar", step: "int64_t") -> "operations_research::OptimizeVar *": r""" Creates a minimization objective.""" return _pywrapcp.Solver_Minimize(self, v, step) def MirrorInterval(self, interval_var: IntervalVar) ‑> operations_research::IntervalVar *-
Creates an interval var that is the mirror image of the given one, that is, the interval var obtained by reversing the axis.
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def MirrorInterval(self, interval_var: "IntervalVar") -> "operations_research::IntervalVar *": r""" Creates an interval var that is the mirror image of the given one, that is, the interval var obtained by reversing the axis. """ return _pywrapcp.Solver_MirrorInterval(self, interval_var) def MonotonicElement(self, values: operations_research::Solver::IndexEvaluator1, increasing: bool, index: IntVar) ‑> operations_research::IntExpr *-
Function based element. The constraint takes ownership of the callback. The callback must be monotonic. It must be able to cope with any possible value in the domain of 'index' (potentially negative ones too). Furtermore, monotonicity is not checked. Thus giving a non-monotonic function, or specifying an incorrect increasing parameter will result in undefined behavior.
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def MonotonicElement(self, values: "operations_research::Solver::IndexEvaluator1", increasing: "bool", index: "IntVar") -> "operations_research::IntExpr *": r""" Function based element. The constraint takes ownership of the callback. The callback must be monotonic. It must be able to cope with any possible value in the domain of 'index' (potentially negative ones too). Furtermore, monotonicity is not checked. Thus giving a non-monotonic function, or specifying an incorrect increasing parameter will result in undefined behavior. """ return _pywrapcp.Solver_MonotonicElement(self, values, increasing, index) def MoveTowardTargetOperator(self, *args) ‑> operations_research::LocalSearchOperator *-
Overload 1: Creates a local search operator that tries to move the assignment of some variables toward a target. The target is given as an Assignment. This operator generates neighbors in which the only difference compared to the current state is that one variable that belongs to the target assignment is set to its target value.
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Overload 2: Creates a local search operator that tries to move the assignment of some variables toward a target. The target is given either as two vectors: a vector of variables and a vector of associated target values. The two vectors should be of the same length. This operator generates neighbors in which the only difference compared to the current state is that one variable that belongs to the given vector is set to its target value.
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def MoveTowardTargetOperator(self, *args) -> "operations_research::LocalSearchOperator *": r""" *Overload 1:* Creates a local search operator that tries to move the assignment of some variables toward a target. The target is given as an Assignment. This operator generates neighbors in which the only difference compared to the current state is that one variable that belongs to the target assignment is set to its target value. | *Overload 2:* Creates a local search operator that tries to move the assignment of some variables toward a target. The target is given either as two vectors: a vector of variables and a vector of associated target values. The two vectors should be of the same length. This operator generates neighbors in which the only difference compared to the current state is that one variable that belongs to the given vector is set to its target value. """ return _pywrapcp.Solver_MoveTowardTargetOperator(self, *args) def NeighborhoodLimit(self, op: LocalSearchOperator, limit: int64_t) ‑> operations_research::LocalSearchOperator *-
Creates a local search operator that wraps another local search operator and limits the number of neighbors explored (i.e., calls to MakeNextNeighbor from the current solution (between two calls to Start()). When this limit is reached, MakeNextNeighbor() returns false. The counter is cleared when Start() is called.
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def NeighborhoodLimit(self, op: "LocalSearchOperator", limit: "int64_t") -> "operations_research::LocalSearchOperator *": r""" Creates a local search operator that wraps another local search operator and limits the number of neighbors explored (i.e., calls to MakeNextNeighbor from the current solution (between two calls to Start()). When this limit is reached, MakeNextNeighbor() returns false. The counter is cleared when Start() is called. """ return _pywrapcp.Solver_NeighborhoodLimit(self, op, limit) def NestedOptimize(self, *args) ‑> operations_research::DecisionBuilder *-
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def NestedOptimize(self, *args) -> "operations_research::DecisionBuilder *": return _pywrapcp.Solver_NestedOptimize(self, *args) def NewSearch(self, *args) ‑> void-
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def NewSearch(self, *args) -> "void": return _pywrapcp.Solver_NewSearch(self, *args) def NextSolution(self) ‑> bool-
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def NextSolution(self) -> "bool": return _pywrapcp.Solver_NextSolution(self) def NonOverlappingBoxesConstraint(self, *args) ‑> operations_research::Constraint *-
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def NonOverlappingBoxesConstraint(self, *args) -> "operations_research::Constraint *": return _pywrapcp.Solver_NonOverlappingBoxesConstraint(self, *args) def NotMemberCt(self, *args) ‑> operations_research::Constraint *-
Overload 1: expr not in set.
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Overload 2: expr should not be in the list of forbidden intervals [start[i]..end[i]].
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Overload 3: expr should not be in the list of forbidden intervals [start[i]..end[i]].
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def NotMemberCt(self, *args) -> "operations_research::Constraint *": r""" *Overload 1:* expr not in set. | *Overload 2:* expr should not be in the list of forbidden intervals [start[i]..end[i]]. | *Overload 3:* expr should not be in the list of forbidden intervals [start[i]..end[i]]. """ return _pywrapcp.Solver_NotMemberCt(self, *args) def NullIntersect(self, first_vars: std::vector< operations_research::IntVar * > const &, second_vars: std::vector< operations_research::IntVar * > const &) ‑> operations_research::Constraint *-
Creates a constraint that states that all variables in the first vector are different from all variables in the second group. Thus the set of values in the first vector does not intersect with the set of values in the second vector.
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def NullIntersect(self, first_vars: "std::vector< operations_research::IntVar * > const &", second_vars: "std::vector< operations_research::IntVar * > const &") -> "operations_research::Constraint *": r""" Creates a constraint that states that all variables in the first vector are different from all variables in the second group. Thus the set of values in the first vector does not intersect with the set of values in the second vector. """ return _pywrapcp.Solver_NullIntersect(self, first_vars, second_vars) def NullIntersectExcept(self, first_vars: std::vector< operations_research::IntVar * > const &, second_vars: std::vector< operations_research::IntVar * > const &, escape_value: int64_t) ‑> operations_research::Constraint *-
Creates a constraint that states that all variables in the first vector are different from all variables from the second group, unless they are assigned to the escape value. Thus the set of values in the first vector minus the escape value does not intersect with the set of values in the second vector.
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def NullIntersectExcept(self, first_vars: "std::vector< operations_research::IntVar * > const &", second_vars: "std::vector< operations_research::IntVar * > const &", escape_value: "int64_t") -> "operations_research::Constraint *": r""" Creates a constraint that states that all variables in the first vector are different from all variables from the second group, unless they are assigned to the escape value. Thus the set of values in the first vector minus the escape value does not intersect with the set of values in the second vector. """ return _pywrapcp.Solver_NullIntersectExcept(self, first_vars, second_vars, escape_value) def Operator(self, *args) ‑> operations_research::LocalSearchOperator *-
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def Operator(self, *args) -> "operations_research::LocalSearchOperator *": return _pywrapcp.Solver_Operator(self, *args) def Optimize(self, maximize: bool, v: IntVar, step: int64_t) ‑> operations_research::OptimizeVar *-
Creates a objective with a given sense (true = maximization).
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def Optimize(self, maximize: "bool", v: "IntVar", step: "int64_t") -> "operations_research::OptimizeVar *": r""" Creates a objective with a given sense (true = maximization).""" return _pywrapcp.Solver_Optimize(self, maximize, v, step) def Pack(self, vars: std::vector< operations_research::IntVar * > const &, number_of_bins: int) ‑> operations_research::Pack *-
This constraint packs all variables onto 'number_of_bins' variables. For any given variable, a value of 'number_of_bins' indicates that the variable is not assigned to any bin. Dimensions, i.e., cumulative constraints on this packing, can be added directly from the pack class.
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def Pack(self, vars: "std::vector< operations_research::IntVar * > const &", number_of_bins: "int") -> "operations_research::Pack *": r""" This constraint packs all variables onto 'number_of_bins' variables. For any given variable, a value of 'number_of_bins' indicates that the variable is not assigned to any bin. Dimensions, i.e., cumulative constraints on this packing, can be added directly from the pack class. """ return _pywrapcp.Solver_Pack(self, vars, number_of_bins) def Parameters(self) ‑> operations_research::ConstraintSolverParameters-
Stored Parameters.
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def Parameters(self) -> "operations_research::ConstraintSolverParameters": r""" Stored Parameters.""" return _pywrapcp.Solver_Parameters(self) def PathCumul(self, *args) ‑> operations_research::Constraint *-
Overload 1: Creates a constraint which accumulates values along a path such that: cumuls[next[i]] = cumuls[i] + transits[i]. Active variables indicate if the corresponding next variable is active; this could be useful to model unperformed nodes in a routing problem.
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Overload 2: Creates a constraint which accumulates values along a path such that: cumuls[next[i]] = cumuls[i] + transit_evaluator(i, next[i]). Active variables indicate if the corresponding next variable is active; this could be useful to model unperformed nodes in a routing problem. Ownership of transit_evaluator is taken and it must be a repeatable callback.
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Overload 3: Creates a constraint which accumulates values along a path such that: cumuls[next[i]] = cumuls[i] + transit_evaluator(i, next[i]) + slacks[i]. Active variables indicate if the corresponding next variable is active; this could be useful to model unperformed nodes in a routing problem. Ownership of transit_evaluator is taken and it must be a repeatable callback.
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def PathCumul(self, *args) -> "operations_research::Constraint *": r""" *Overload 1:* Creates a constraint which accumulates values along a path such that: cumuls[next[i]] = cumuls[i] + transits[i]. Active variables indicate if the corresponding next variable is active; this could be useful to model unperformed nodes in a routing problem. | *Overload 2:* Creates a constraint which accumulates values along a path such that: cumuls[next[i]] = cumuls[i] + transit_evaluator(i, next[i]). Active variables indicate if the corresponding next variable is active; this could be useful to model unperformed nodes in a routing problem. Ownership of transit_evaluator is taken and it must be a repeatable callback. | *Overload 3:* Creates a constraint which accumulates values along a path such that: cumuls[next[i]] = cumuls[i] + transit_evaluator(i, next[i]) + slacks[i]. Active variables indicate if the corresponding next variable is active; this could be useful to model unperformed nodes in a routing problem. Ownership of transit_evaluator is taken and it must be a repeatable callback. """ return _pywrapcp.Solver_PathCumul(self, *args) def Phase(self, *args) ‑> operations_research::DecisionBuilder *-
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def Phase(self, *args) -> "operations_research::DecisionBuilder *": return _pywrapcp.Solver_Phase(self, *args) def PrintModelVisitor(self) ‑> operations_research::ModelVisitor *-
Prints the model.
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def PrintModelVisitor(self) -> "operations_research::ModelVisitor *": r""" Prints the model.""" return _pywrapcp.Solver_PrintModelVisitor(self) def Rand32(self, size: int32_t) ‑> int32_t-
Returns a random value between 0 and 'size' - 1;
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def Rand32(self, size: "int32_t") -> "int32_t": r""" Returns a random value between 0 and 'size' - 1;""" return _pywrapcp.Solver_Rand32(self, size) def Rand64(self, size: int64_t) ‑> int64_t-
Returns a random value between 0 and 'size' - 1;
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def Rand64(self, size: "int64_t") -> "int64_t": r""" Returns a random value between 0 and 'size' - 1;""" return _pywrapcp.Solver_Rand64(self, size) def RandomConcatenateOperators(self, *args) ‑> operations_research::LocalSearchOperator *-
Overload 1: Randomized version of local search concatenator; calls a random operator at each call to MakeNextNeighbor().
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Overload 2: Randomized version of local search concatenator; calls a random operator at each call to MakeNextNeighbor(). The provided seed is used to initialize the random number generator.
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def RandomConcatenateOperators(self, *args) -> "operations_research::LocalSearchOperator *": r""" *Overload 1:* Randomized version of local search concatenator; calls a random operator at each call to MakeNextNeighbor(). | *Overload 2:* Randomized version of local search concatenator; calls a random operator at each call to MakeNextNeighbor(). The provided seed is used to initialize the random number generator. """ return _pywrapcp.Solver_RandomConcatenateOperators(self, *args) def RandomLnsOperator(self, *args) ‑> operations_research::LocalSearchOperator *-
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def RandomLnsOperator(self, *args) -> "operations_research::LocalSearchOperator *": return _pywrapcp.Solver_RandomLnsOperator(self, *args) def RankFirstInterval(self, sequence: SequenceVar, index: int) ‑> operations_research::Decision *-
Returns a decision that tries to rank first the ith interval var in the sequence variable.
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def RankFirstInterval(self, sequence: "SequenceVar", index: "int") -> "operations_research::Decision *": r""" Returns a decision that tries to rank first the ith interval var in the sequence variable. """ return _pywrapcp.Solver_RankFirstInterval(self, sequence, index) def RankLastInterval(self, sequence: SequenceVar, index: int) ‑> operations_research::Decision *-
Returns a decision that tries to rank last the ith interval var in the sequence variable.
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def RankLastInterval(self, sequence: "SequenceVar", index: "int") -> "operations_research::Decision *": r""" Returns a decision that tries to rank last the ith interval var in the sequence variable. """ return _pywrapcp.Solver_RankLastInterval(self, sequence, index) def ReSeed(self, seed: int32_t) ‑> void-
Reseed the solver random generator.
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def ReSeed(self, seed: "int32_t") -> "void": r""" Reseed the solver random generator.""" return _pywrapcp.Solver_ReSeed(self, seed) def RestartCurrentSearch(self) ‑> void-
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def RestartCurrentSearch(self) -> "void": return _pywrapcp.Solver_RestartCurrentSearch(self) def RestartSearch(self) ‑> void-
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def RestartSearch(self) -> "void": return _pywrapcp.Solver_RestartSearch(self) def RestoreAssignment(self, assignment: Assignment) ‑> operations_research::DecisionBuilder *-
Returns a DecisionBuilder which restores an Assignment (calls void Assignment::Restore())
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def RestoreAssignment(self, assignment: "Assignment") -> "operations_research::DecisionBuilder *": r""" Returns a DecisionBuilder which restores an Assignment (calls void Assignment::Restore()) """ return _pywrapcp.Solver_RestoreAssignment(self, assignment) def ScalProd(self, *args) ‑> operations_research::IntExpr *-
Overload 1: scalar product
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Overload 2: scalar product
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def ScalProd(self, *args) -> "operations_research::IntExpr *": r""" *Overload 1:* scalar product | *Overload 2:* scalar product """ return _pywrapcp.Solver_ScalProd(self, *args) def ScalProdEquality(self, *args) ‑> operations_research::Constraint *-
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def ScalProdEquality(self, *args) -> "operations_research::Constraint *": return _pywrapcp.Solver_ScalProdEquality(self, *args) def ScalProdGreaterOrEqual(self, *args) ‑> operations_research::Constraint *-
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def ScalProdGreaterOrEqual(self, *args) -> "operations_research::Constraint *": return _pywrapcp.Solver_ScalProdGreaterOrEqual(self, *args) def ScalProdLessOrEqual(self, *args) ‑> operations_research::Constraint *-
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def ScalProdLessOrEqual(self, *args) -> "operations_research::Constraint *": return _pywrapcp.Solver_ScalProdLessOrEqual(self, *args) def ScheduleOrExpedite(self, var: IntervalVar, est: int64_t, marker: int64_t *const) ‑> operations_research::Decision *-
Returns a decision that tries to schedule a task at a given time. On the Apply branch, it will set that interval var as performed and set its end to 'est'. On the Refute branch, it will just update the 'marker' to 'est' - 1. This decision is used in the INTERVAL_SET_TIMES_BACKWARD strategy.
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def ScheduleOrExpedite(self, var: "IntervalVar", est: "int64_t", marker: "int64_t *const") -> "operations_research::Decision *": r""" Returns a decision that tries to schedule a task at a given time. On the Apply branch, it will set that interval var as performed and set its end to 'est'. On the Refute branch, it will just update the 'marker' to 'est' - 1. This decision is used in the INTERVAL_SET_TIMES_BACKWARD strategy. """ return _pywrapcp.Solver_ScheduleOrExpedite(self, var, est, marker) def ScheduleOrPostpone(self, var: IntervalVar, est: int64_t, marker: int64_t *const) ‑> operations_research::Decision *-
Returns a decision that tries to schedule a task at a given time. On the Apply branch, it will set that interval var as performed and set its start to 'est'. On the Refute branch, it will just update the 'marker' to 'est' + 1. This decision is used in the INTERVAL_SET_TIMES_FORWARD strategy.
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def ScheduleOrPostpone(self, var: "IntervalVar", est: "int64_t", marker: "int64_t *const") -> "operations_research::Decision *": r""" Returns a decision that tries to schedule a task at a given time. On the Apply branch, it will set that interval var as performed and set its start to 'est'. On the Refute branch, it will just update the 'marker' to 'est' + 1. This decision is used in the INTERVAL_SET_TIMES_FORWARD strategy. """ return _pywrapcp.Solver_ScheduleOrPostpone(self, var, est, marker) def SearchDepth(self) ‑> int-
Gets the search depth of the current active search. Returns -1 if there is no active search opened.
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def SearchDepth(self) -> "int": r""" Gets the search depth of the current active search. Returns -1 if there is no active search opened. """ return _pywrapcp.Solver_SearchDepth(self) def SearchLeftDepth(self) ‑> int-
Gets the search left depth of the current active search. Returns -1 if there is no active search opened.
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def SearchLeftDepth(self) -> "int": r""" Gets the search left depth of the current active search. Returns -1 if there is no active search opened. """ return _pywrapcp.Solver_SearchLeftDepth(self) def SearchLog(self, *args) ‑> operations_research::SearchMonitor *-
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def SearchLog(self, *args) -> "operations_research::SearchMonitor *": return _pywrapcp.Solver_SearchLog(self, *args) def SearchLogWithCallback(self, period: int, callback: std::function< std::string () >) ‑> operations_research::SearchMonitor *-
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def SearchLogWithCallback(self, period: "int", callback: "std::function< std::string () >") -> "operations_research::SearchMonitor *": return _pywrapcp.Solver_SearchLogWithCallback(self, period, callback) def SearchTrace(self, prefix: std::string const &) ‑> operations_research::SearchMonitor *-
Creates a search monitor that will trace precisely the behavior of the search. Use this only for low level debugging.
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def SearchTrace(self, prefix: "std::string const &") -> "operations_research::SearchMonitor *": r""" Creates a search monitor that will trace precisely the behavior of the search. Use this only for low level debugging. """ return _pywrapcp.Solver_SearchTrace(self, prefix) def SemiContinuousExpr(self, expr: IntExpr, fixed_charge: int64_t, step: int64_t) ‑> operations_research::IntExpr *-
Semi continuous Expression (x <= 0 -> f(x) = 0; x > 0 -> f(x) = ax + b) a >= 0 and b >= 0
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def SemiContinuousExpr(self, expr: "IntExpr", fixed_charge: "int64_t", step: "int64_t") -> "operations_research::IntExpr *": r""" Semi continuous Expression (x <= 0 -> f(x) = 0; x > 0 -> f(x) = ax + b) a >= 0 and b >= 0 """ return _pywrapcp.Solver_SemiContinuousExpr(self, expr, fixed_charge, step) def ShouldFail(self) ‑> void-
These methods are only useful for the SWIG wrappers, which need a way to externally cause the Solver to fail.
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def ShouldFail(self) -> "void": r""" These methods are only useful for the SWIG wrappers, which need a way to externally cause the Solver to fail. """ return _pywrapcp.Solver_ShouldFail(self) def SimulatedAnnealing(self, maximize: bool, v: IntVar, step: int64_t, initial_temperature: int64_t) ‑> operations_research::SearchMonitor *-
Creates a Simulated Annealing monitor.
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def SimulatedAnnealing(self, maximize: "bool", v: "IntVar", step: "int64_t", initial_temperature: "int64_t") -> "operations_research::SearchMonitor *": r""" Creates a Simulated Annealing monitor.""" return _pywrapcp.Solver_SimulatedAnnealing(self, maximize, v, step, initial_temperature) def Solutions(self) ‑> int64_t-
The number of solutions found since the start of the search.
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def Solutions(self) -> "int64_t": r""" The number of solutions found since the start of the search.""" return _pywrapcp.Solver_Solutions(self) def SolutionsLimit(self, solutions: int64_t) ‑> operations_research::RegularLimit *-
Creates a search limit that constrains the number of solutions found during the search.
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def SolutionsLimit(self, solutions: "int64_t") -> "operations_research::RegularLimit *": r""" Creates a search limit that constrains the number of solutions found during the search. """ return _pywrapcp.Solver_SolutionsLimit(self, solutions) def Solve(self, *args) ‑> bool-
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def Solve(self, *args) -> "bool": return _pywrapcp.Solver_Solve(self, *args) def SolveAndCommit(self, *args) ‑> bool-
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def SolveAndCommit(self, *args) -> "bool": return _pywrapcp.Solver_SolveAndCommit(self, *args) def SolveDepth(self) ‑> int-
Gets the number of nested searches. It returns 0 outside search, 1 during the top level search, 2 or more in case of nested searches.
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def SolveDepth(self) -> "int": r""" Gets the number of nested searches. It returns 0 outside search, 1 during the top level search, 2 or more in case of nested searches. """ return _pywrapcp.Solver_SolveDepth(self) def SolveOnce(self, db: DecisionBuilder, monitors: std::vector< operations_research::SearchMonitor * > const &) ‑> operations_research::DecisionBuilder *-
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def SolveOnce(self, db: "DecisionBuilder", monitors: "std::vector< operations_research::SearchMonitor * > const &") -> "operations_research::DecisionBuilder *": return _pywrapcp.Solver_SolveOnce(self, db, monitors) def SortingConstraint(self, vars: std::vector< operations_research::IntVar * > const &, sorted: std::vector< operations_research::IntVar * > const &) ‑> operations_research::Constraint *-
Creates a constraint binding the arrays of variables "vars" and "sorted_vars": sorted_vars[0] must be equal to the minimum of all variables in vars, and so on: the value of sorted_vars[i] must be equal to the i-th value of variables invars.
This constraint propagates in both directions: from "vars" to "sorted_vars" and vice-versa.
Behind the scenes, this constraint maintains that: - sorted is always increasing. - whatever the values of vars, there exists a permutation that injects its values into the sorted variables.
For more info, please have a look at: https://mpi-inf.mpg.de/~mehlhorn/ftp/Mehlhorn-Thiel.pdf
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def SortingConstraint(self, vars: "std::vector< operations_research::IntVar * > const &", sorted: "std::vector< operations_research::IntVar * > const &") -> "operations_research::Constraint *": r""" Creates a constraint binding the arrays of variables "vars" and "sorted_vars": sorted_vars[0] must be equal to the minimum of all variables in vars, and so on: the value of sorted_vars[i] must be equal to the i-th value of variables invars. This constraint propagates in both directions: from "vars" to "sorted_vars" and vice-versa. Behind the scenes, this constraint maintains that: - sorted is always increasing. - whatever the values of vars, there exists a permutation that injects its values into the sorted variables. For more info, please have a look at: https://mpi-inf.mpg.de/~mehlhorn/ftp/Mehlhorn-Thiel.pdf """ return _pywrapcp.Solver_SortingConstraint(self, vars, sorted) def SplitVariableDomain(self, var: IntVar, val: int64_t, start_with_lower_half: bool) ‑> operations_research::Decision *-
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def SplitVariableDomain(self, var: "IntVar", val: "int64_t", start_with_lower_half: "bool") -> "operations_research::Decision *": return _pywrapcp.Solver_SplitVariableDomain(self, var, val, start_with_lower_half) def Stamp(self) ‑> uint64_t-
The stamp indicates how many moves in the search tree we have performed. It is useful to detect if we need to update same lazy structures.
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def Stamp(self) -> "uint64_t": r""" The stamp indicates how many moves in the search tree we have performed. It is useful to detect if we need to update same lazy structures. """ return _pywrapcp.Solver_Stamp(self) def StatisticsModelVisitor(self) ‑> operations_research::ModelVisitor *-
Displays some nice statistics on the model.
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def StatisticsModelVisitor(self) -> "operations_research::ModelVisitor *": r""" Displays some nice statistics on the model.""" return _pywrapcp.Solver_StatisticsModelVisitor(self) def StoreAssignment(self, assignment: Assignment) ‑> operations_research::DecisionBuilder *-
Returns a DecisionBuilder which stores an Assignment (calls void Assignment::Store())
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def StoreAssignment(self, assignment: "Assignment") -> "operations_research::DecisionBuilder *": r""" Returns a DecisionBuilder which stores an Assignment (calls void Assignment::Store()) """ return _pywrapcp.Solver_StoreAssignment(self, assignment) def SubCircuit(self, nexts: std::vector< operations_research::IntVar * > const &) ‑> operations_research::Constraint *-
Force the "nexts" variable to create a complete Hamiltonian path for those that do not loop upon themselves.
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def SubCircuit(self, nexts: "std::vector< operations_research::IntVar * > const &") -> "operations_research::Constraint *": r""" Force the "nexts" variable to create a complete Hamiltonian path for those that do not loop upon themselves. """ return _pywrapcp.Solver_SubCircuit(self, nexts) def Sum(self, vars: std::vector< operations_research::IntVar * > const &) ‑> operations_research::IntExpr *-
sum of all vars.
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def Sum(self, vars: "std::vector< operations_research::IntVar * > const &") -> "operations_research::IntExpr *": r""" sum of all vars.""" return _pywrapcp.Solver_Sum(self, vars) def SumEquality(self, *args) ‑> operations_research::Constraint *-
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def SumEquality(self, *args) -> "operations_research::Constraint *": return _pywrapcp.Solver_SumEquality(self, *args) def SumGreaterOrEqual(self, vars: std::vector< operations_research::IntVar * > const &, cst: int64_t) ‑> operations_research::Constraint *-
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def SumGreaterOrEqual(self, vars: "std::vector< operations_research::IntVar * > const &", cst: "int64_t") -> "operations_research::Constraint *": return _pywrapcp.Solver_SumGreaterOrEqual(self, vars, cst) def SumLessOrEqual(self, vars: std::vector< operations_research::IntVar * > const &, cst: int64_t) ‑> operations_research::Constraint *-
Variation on arrays.
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def SumLessOrEqual(self, vars: "std::vector< operations_research::IntVar * > const &", cst: "int64_t") -> "operations_research::Constraint *": r""" Variation on arrays.""" return _pywrapcp.Solver_SumLessOrEqual(self, vars, cst) def SumObjectiveFilter(self, vars: std::vector< operations_research::IntVar * > const &, values: operations_research::Solver::IndexEvaluator2, filter_enum: operations_research::Solver::LocalSearchFilterBound) ‑> operations_research::LocalSearchFilter *-
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def SumObjectiveFilter(self, vars: "std::vector< operations_research::IntVar * > const &", values: "operations_research::Solver::IndexEvaluator2", filter_enum: "operations_research::Solver::LocalSearchFilterBound") -> "operations_research::LocalSearchFilter *": return _pywrapcp.Solver_SumObjectiveFilter(self, vars, values, filter_enum) def TabuSearch(self, maximize: bool, v: IntVar, step: int64_t, vars: std::vector< operations_research::IntVar * > const &, keep_tenure: int64_t, forbid_tenure: int64_t, tabu_factor: double) ‑> operations_research::SearchMonitor *-
MetaHeuristics which try to get the search out of local optima. Creates a Tabu Search monitor. In the context of local search the behavior is similar to MakeOptimize(), creating an objective in a given sense. The behavior differs once a local optimum is reached: thereafter solutions which degrade the value of the objective are allowed if they are not "tabu". A solution is "tabu" if it doesn't respect the following rules: - improving the best solution found so far - variables in the "keep" list must keep their value, variables in the "forbid" list must not take the value they have in the list. Variables with new values enter the tabu lists after each new solution found and leave the lists after a given number of iterations (called tenure). Only the variables passed to the method can enter the lists. The tabu criterion is softened by the tabu factor which gives the number of "tabu" violations which is tolerated; a factor of 1 means no violations allowed; a factor of 0 means all violations are allowed.
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def TabuSearch(self, maximize: "bool", v: "IntVar", step: "int64_t", vars: "std::vector< operations_research::IntVar * > const &", keep_tenure: "int64_t", forbid_tenure: "int64_t", tabu_factor: "double") -> "operations_research::SearchMonitor *": r""" MetaHeuristics which try to get the search out of local optima. Creates a Tabu Search monitor. In the context of local search the behavior is similar to MakeOptimize(), creating an objective in a given sense. The behavior differs once a local optimum is reached: thereafter solutions which degrade the value of the objective are allowed if they are not "tabu". A solution is "tabu" if it doesn't respect the following rules: - improving the best solution found so far - variables in the "keep" list must keep their value, variables in the "forbid" list must not take the value they have in the list. Variables with new values enter the tabu lists after each new solution found and leave the lists after a given number of iterations (called tenure). Only the variables passed to the method can enter the lists. The tabu criterion is softened by the tabu factor which gives the number of "tabu" violations which is tolerated; a factor of 1 means no violations allowed; a factor of 0 means all violations are allowed. """ return _pywrapcp.Solver_TabuSearch(self, maximize, v, step, vars, keep_tenure, forbid_tenure, tabu_factor) def TemporalDisjunction(self, *args) ‑> operations_research::Constraint *-
Overload 1: This constraint implements a temporal disjunction between two interval vars t1 and t2. 'alt' indicates which alternative was chosen (alt == 0 is equivalent to t1 before t2).
|
Overload 2: This constraint implements a temporal disjunction between two interval vars.
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def TemporalDisjunction(self, *args) -> "operations_research::Constraint *": r""" *Overload 1:* This constraint implements a temporal disjunction between two interval vars t1 and t2. 'alt' indicates which alternative was chosen (alt == 0 is equivalent to t1 before t2). | *Overload 2:* This constraint implements a temporal disjunction between two interval vars. """ return _pywrapcp.Solver_TemporalDisjunction(self, *args) def TimeLimit(self, *args) ‑> operations_research::RegularLimit *-
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def TimeLimit(self, *args) -> "operations_research::RegularLimit *": return _pywrapcp.Solver_TimeLimit(self, *args) def TransitionConstraint(self, *args) ‑> operations_research::Constraint *-
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def TransitionConstraint(self, *args) -> "operations_research::Constraint *": return _pywrapcp.Solver_TransitionConstraint(self, *args) def TreeNoCycle(self, nexts: std::vector< operations_research::IntVar * > const &, active: std::vector< operations_research::IntVar * > const &, callback: operations_research::Solver::IndexFilter1 = 0) ‑> operations_research::Constraint *-
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def TreeNoCycle(self, nexts: "std::vector< operations_research::IntVar * > const &", active: "std::vector< operations_research::IntVar * > const &", callback: "operations_research::Solver::IndexFilter1"=0) -> "operations_research::Constraint *": return _pywrapcp.Solver_TreeNoCycle(self, nexts, active, callback) def TrueConstraint(self) ‑> operations_research::Constraint *-
This constraint always succeeds.
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def TrueConstraint(self) -> "operations_research::Constraint *": r""" This constraint always succeeds.""" return _pywrapcp.Solver_TrueConstraint(self) def Try(self, dbs: std::vector< operations_research::DecisionBuilder * > const &) ‑> operations_research::DecisionBuilder *-
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def Try(self, dbs: "std::vector< operations_research::DecisionBuilder * > const &") -> "operations_research::DecisionBuilder *": return _pywrapcp.Solver_Try(self, dbs) def VarEvalValEvalPhase(self, vars: std::vector< operations_research::IntVar * > const &, var_eval: std::function< int64_t (int64_t) >, val_eval: operations_research::Solver::IndexEvaluator2) ‑> operations_research::DecisionBuilder *-
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def VarEvalValEvalPhase(self, vars: "std::vector< operations_research::IntVar * > const &", var_eval: "std::function< int64_t (int64_t) >", val_eval: "operations_research::Solver::IndexEvaluator2") -> "operations_research::DecisionBuilder *": return _pywrapcp.Solver_VarEvalValEvalPhase(self, vars, var_eval, val_eval) def VarEvalValEvalTieBreakPhase(self, vars: std::vector< operations_research::IntVar * > const &, var_eval: std::function< int64_t (int64_t) >, val_eval: operations_research::Solver::IndexEvaluator2, tie_breaker: std::function< int64_t (int64_t) >) ‑> operations_research::DecisionBuilder *-
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def VarEvalValEvalTieBreakPhase(self, vars: "std::vector< operations_research::IntVar * > const &", var_eval: "std::function< int64_t (int64_t) >", val_eval: "operations_research::Solver::IndexEvaluator2", tie_breaker: "std::function< int64_t (int64_t) >") -> "operations_research::DecisionBuilder *": return _pywrapcp.Solver_VarEvalValEvalTieBreakPhase(self, vars, var_eval, val_eval, tie_breaker) def VarEvalValStrPhase(self, vars: std::vector< operations_research::IntVar * > const &, var_evaluator: std::function< int64_t (int64_t) >, val_str: operations_research::Solver::IntValueStrategy) ‑> operations_research::DecisionBuilder *-
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def VarEvalValStrPhase(self, vars: "std::vector< operations_research::IntVar * > const &", var_evaluator: "std::function< int64_t (int64_t) >", val_str: "operations_research::Solver::IntValueStrategy") -> "operations_research::DecisionBuilder *": return _pywrapcp.Solver_VarEvalValStrPhase(self, vars, var_evaluator, val_str) def VarStrValEvalPhase(self, vars: std::vector< operations_research::IntVar * > const &, var_str: operations_research::Solver::IntVarStrategy, val_eval: operations_research::Solver::IndexEvaluator2) ‑> operations_research::DecisionBuilder *-
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def VarStrValEvalPhase(self, vars: "std::vector< operations_research::IntVar * > const &", var_str: "operations_research::Solver::IntVarStrategy", val_eval: "operations_research::Solver::IndexEvaluator2") -> "operations_research::DecisionBuilder *": return _pywrapcp.Solver_VarStrValEvalPhase(self, vars, var_str, val_eval) def VarStrValEvalTieBreakPhase(self, vars: std::vector< operations_research::IntVar * > const &, var_str: operations_research::Solver::IntVarStrategy, val_eval: operations_research::Solver::IndexEvaluator2, tie_breaker: std::function< int64_t (int64_t) >) ‑> operations_research::DecisionBuilder *-
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def VarStrValEvalTieBreakPhase(self, vars: "std::vector< operations_research::IntVar * > const &", var_str: "operations_research::Solver::IntVarStrategy", val_eval: "operations_research::Solver::IndexEvaluator2", tie_breaker: "std::function< int64_t (int64_t) >") -> "operations_research::DecisionBuilder *": return _pywrapcp.Solver_VarStrValEvalTieBreakPhase(self, vars, var_str, val_eval, tie_breaker) def VariableGreaterOrEqualValue(self, var: IntVar, value: int64_t) ‑> operations_research::Decision *-
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def VariableGreaterOrEqualValue(self, var: "IntVar", value: "int64_t") -> "operations_research::Decision *": return _pywrapcp.Solver_VariableGreaterOrEqualValue(self, var, value) def VariableLessOrEqualValue(self, var: IntVar, value: int64_t) ‑> operations_research::Decision *-
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def VariableLessOrEqualValue(self, var: "IntVar", value: "int64_t") -> "operations_research::Decision *": return _pywrapcp.Solver_VariableLessOrEqualValue(self, var, value) def WallTime(self) ‑> int64_t-
DEPRECATED: Use Now() instead. Time elapsed, in ms since the creation of the solver.
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def WallTime(self) -> "int64_t": r""" DEPRECATED: Use Now() instead. Time elapsed, in ms since the creation of the solver. """ return _pywrapcp.Solver_WallTime(self) def WeightedMaximize(self, *args) ‑> operations_research::OptimizeVar *-
Overload 1: Creates a maximization weigthed objective.
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Overload 2: Creates a maximization weigthed objective.
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def WeightedMaximize(self, *args) -> "operations_research::OptimizeVar *": r""" *Overload 1:* Creates a maximization weigthed objective. | *Overload 2:* Creates a maximization weigthed objective. """ return _pywrapcp.Solver_WeightedMaximize(self, *args) def WeightedMinimize(self, *args) ‑> operations_research::OptimizeVar *-
Overload 1: Creates a minimization weighted objective. The actual objective is scalar_prod(sub_objectives, weights).
|
Overload 2: Creates a minimization weighted objective. The actual objective is scalar_prod(sub_objectives, weights).
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def WeightedMinimize(self, *args) -> "operations_research::OptimizeVar *": r""" *Overload 1:* Creates a minimization weighted objective. The actual objective is scalar_prod(sub_objectives, weights). | *Overload 2:* Creates a minimization weighted objective. The actual objective is scalar_prod(sub_objectives, weights). """ return _pywrapcp.Solver_WeightedMinimize(self, *args) def WeightedOptimize(self, *args) ‑> operations_research::OptimizeVar *-
Overload 1: Creates a weighted objective with a given sense (true = maximization).
|
Overload 2: Creates a weighted objective with a given sense (true = maximization).
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def WeightedOptimize(self, *args) -> "operations_research::OptimizeVar *": r""" *Overload 1:* Creates a weighted objective with a given sense (true = maximization). | *Overload 2:* Creates a weighted objective with a given sense (true = maximization). """ return _pywrapcp.Solver_WeightedOptimize(self, *args)
class TypeIncompatibilityChecker (model:Â RoutingModel, check_hard_incompatibilities:Â bool)-
Checker for type incompatibilities.
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class TypeIncompatibilityChecker(TypeRegulationsChecker): r""" Checker for type incompatibilities.""" thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") __repr__ = _swig_repr def __init__(self, model: "RoutingModel", check_hard_incompatibilities: "bool"): _pywrapcp.TypeIncompatibilityChecker_swiginit(self, _pywrapcp.new_TypeIncompatibilityChecker(model, check_hard_incompatibilities)) __swig_destroy__ = _pywrapcp.delete_TypeIncompatibilityCheckerAncestors
Inherited members
class TypeRegulationsChecker (*args, **kwargs)-
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class TypeRegulationsChecker(object): thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined - class is abstract") __repr__ = _swig_repr __swig_destroy__ = _pywrapcp.delete_TypeRegulationsChecker def CheckVehicle(self, vehicle: "int", next_accessor: "std::function< int64_t (int64_t) > const &") -> "bool": return _pywrapcp.TypeRegulationsChecker_CheckVehicle(self, vehicle, next_accessor)Subclasses
Instance variables
var thisown-
The membership flag
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thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
Methods
def CheckVehicle(self, vehicle: int, next_accessor: std::function< int64_t (int64_t) > const &) ‑> bool-
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def CheckVehicle(self, vehicle: "int", next_accessor: "std::function< int64_t (int64_t) > const &") -> "bool": return _pywrapcp.TypeRegulationsChecker_CheckVehicle(self, vehicle, next_accessor)
class TypeRegulationsConstraint (model:Â RoutingModel)-
The following constraint ensures that incompatibilities and requirements between types are respected.
It verifies both "hard" and "temporal" incompatibilities. Two nodes with hard incompatible types cannot be served by the same vehicle at all, while with a temporal incompatibility they can't be on the same route at the same time. The VisitTypePolicy of a node determines how visiting it impacts the type count on the route.
For example, for - three temporally incompatible types T1 T2 and T3 - 2 pairs of nodes a1/r1 and a2/r2 of type T1 and T2 respectively, with - a1 and a2 of VisitTypePolicy TYPE_ADDED_TO_VEHICLE - r1 and r2 of policy ADDED_TYPE_REMOVED_FROM_VEHICLE - 3 nodes A, UV and AR of type T3, respectively with type policies TYPE_ADDED_TO_VEHICLE, TYPE_ON_VEHICLE_UP_TO_VISIT and TYPE_SIMULTANEOUSLY_ADDED_AND_REMOVED the configurations UV –> a1 –> r1 –> a2 –> r2, a1 –> r1 –> a2 –> r2 –> A and a1 –> r1 –> AR –> a2 –> r2 are acceptable, whereas the configurations a1 –> a2 –> r1 –> …, or A –> a1 –> r1 –> …, or a1 –> r1 –> UV –> … are not feasible.
It also verifies same-vehicle and temporal type requirements. A node of type T_d with a same-vehicle requirement for type T_r needs to be served by the same vehicle as a node of type T_r. Temporal requirements, on the other hand, can take effect either when the dependent type is being added to the route or when it's removed from it, which is determined by the dependent node's VisitTypePolicy. In the above example: - If T3 is required on the same vehicle as T1, A, AR or UV must be on the same vehicle as a1. - If T2 is required when adding T1, a2 must be visited before a1, and if r2 is also visited on the route, it must be after a1, i.e. T2 must be on the vehicle when a1 is visited: … –> a2 –> … –> a1 –> … –> r2 –> … - If T3 is required when removing T1, T3 needs to be on the vehicle when r1 is visited: … –> A –> … –> r1 –> … OR … –> r1 –> … –> UV –> …
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class TypeRegulationsConstraint(Constraint): r""" The following constraint ensures that incompatibilities and requirements between types are respected. It verifies both "hard" and "temporal" incompatibilities. Two nodes with hard incompatible types cannot be served by the same vehicle at all, while with a temporal incompatibility they can't be on the same route at the same time. The VisitTypePolicy of a node determines how visiting it impacts the type count on the route. For example, for - three temporally incompatible types T1 T2 and T3 - 2 pairs of nodes a1/r1 and a2/r2 of type T1 and T2 respectively, with - a1 and a2 of VisitTypePolicy TYPE_ADDED_TO_VEHICLE - r1 and r2 of policy ADDED_TYPE_REMOVED_FROM_VEHICLE - 3 nodes A, UV and AR of type T3, respectively with type policies TYPE_ADDED_TO_VEHICLE, TYPE_ON_VEHICLE_UP_TO_VISIT and TYPE_SIMULTANEOUSLY_ADDED_AND_REMOVED the configurations UV --> a1 --> r1 --> a2 --> r2, a1 --> r1 --> a2 --> r2 --> A and a1 --> r1 --> AR --> a2 --> r2 are acceptable, whereas the configurations a1 --> a2 --> r1 --> ..., or A --> a1 --> r1 --> ..., or a1 --> r1 --> UV --> ... are not feasible. It also verifies same-vehicle and temporal type requirements. A node of type T_d with a same-vehicle requirement for type T_r needs to be served by the same vehicle as a node of type T_r. Temporal requirements, on the other hand, can take effect either when the dependent type is being added to the route or when it's removed from it, which is determined by the dependent node's VisitTypePolicy. In the above example: - If T3 is required on the same vehicle as T1, A, AR or UV must be on the same vehicle as a1. - If T2 is required when adding T1, a2 must be visited *before* a1, and if r2 is also visited on the route, it must be *after* a1, i.e. T2 must be on the vehicle when a1 is visited: ... --> a2 --> ... --> a1 --> ... --> r2 --> ... - If T3 is required when removing T1, T3 needs to be on the vehicle when r1 is visited: ... --> A --> ... --> r1 --> ... OR ... --> r1 --> ... --> UV --> ... """ thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") __repr__ = _swig_repr def __init__(self, model: "RoutingModel"): _pywrapcp.TypeRegulationsConstraint_swiginit(self, _pywrapcp.new_TypeRegulationsConstraint(model)) def Post(self) -> "void": return _pywrapcp.TypeRegulationsConstraint_Post(self) def InitialPropagateWrapper(self) -> "void": return _pywrapcp.TypeRegulationsConstraint_InitialPropagateWrapper(self) __swig_destroy__ = _pywrapcp.delete_TypeRegulationsConstraintAncestors
Inherited members
class TypeRequirementChecker (model:Â RoutingModel)-
Checker for type requirements.
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class TypeRequirementChecker(TypeRegulationsChecker): r""" Checker for type requirements.""" thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") __repr__ = _swig_repr def __init__(self, model: "RoutingModel"): _pywrapcp.TypeRequirementChecker_swiginit(self, _pywrapcp.new_TypeRequirementChecker(model)) __swig_destroy__ = _pywrapcp.delete_TypeRequirementCheckerAncestors
Inherited members