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dotnet: Remove reference to dotnet release command

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- Currently not implemented...

Add abseil patch

- Add patches/absl-config.cmake

Makefile: Add abseil-cpp on unix

- Force abseil-cpp SHA1 to 45221cc
  note: Just before the PR #136 which break all CMake

Makefile: Add abseil-cpp on windows

- Force abseil-cpp SHA1 to 45221cc
  note: Just before the PR #136 which break all CMake

CMake: Add abseil-cpp

- Force abseil-cpp SHA1 to 45221cc
  note: Just before the PR #136 which break all CMake

port to absl: C++ Part

- Fix warning with the use of ABSL_MUST_USE_RESULT
  > The macro must appear as the very first part of a function
    declaration or definition:
    ...
    Note: past advice was to place the macro after the argument list.
  src: dependencies/sources/abseil-cpp-master/absl/base/attributes.h:418
- Rename enum after windows clash
- Remove non compact table constraints
- Change index type from int64 to int in routing library
- Fix file_nonport compilation on windows
- Fix another naming conflict with windows (NO_ERROR is a macro)
- Cleanup hash containers; work on sat internals
- Add optional_boolean sub-proto

Sync cpp examples with internal code
- reenable issue173 after reducing number of loops

port to absl: Python Part

- Add back cp_model.INT32_MIN|MAX for examples

Update Python examples

- Add random_tsp.py
- Run words_square example
- Run magic_square in python tests

port to absl: Java Part

- Fix compilation of the new routing parameters in java
- Protect some code from SWIG parsing

Update Java Examples

port to absl: .Net Part

Update .Net examples

work on sat internals; Add C++ CP-SAT CpModelBuilder API; update sample code and recipes to use the new API; sync with internal code

Remove VS 2015 in Appveyor-CI

- abseil-cpp does not support VS 2015...

improve tables

upgrade C++ sat examples to use the new API; work on sat internals

update license dates

rewrite jobshop_ft06_distance.py to use the CP-SAT solver

rename last example

revert last commit

more work on SAT internals

fix
This commit is contained in:
Corentin Le Molgat
2018-10-31 16:18:18 +01:00
parent 745906cb7c
commit b027e57e95
490 changed files with 92044 additions and 24779 deletions
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242
examples/python/cvrp.py
View File

@@ -24,7 +24,10 @@
"""
from __future__ import print_function
from functools import partial
from six.moves import xrange
from ortools.constraint_solver import pywrapcp
from ortools.constraint_solver import routing_enums_pb2
@@ -33,153 +36,160 @@ from ortools.constraint_solver import routing_enums_pb2
# Problem Data Definition #
###########################
def create_data_model():
"""Stores the data for the problem"""
data = {}
# Locations in block unit
_locations = \
[(4, 4), # depot
(2, 0), (8, 0), # locations to visit
(0, 1), (1, 1),
(5, 2), (7, 2),
(3, 3), (6, 3),
(5, 5), (8, 5),
(1, 6), (2, 6),
(3, 7), (6, 7),
(0, 8), (7, 8)]
# Compute locations in meters using the block dimension defined as follow
# Manhattan average block: 750ft x 264ft -> 228m x 80m
# here we use: 114m x 80m city block
# src: https://nyti.ms/2GDoRIe "NY Times: Know Your distance"
data["locations"] = [(l[0] * 114, l[1] * 80) for l in _locations]
data["num_locations"] = len(data["locations"])
data["demands"] = \
[0, # depot
1, 1, # 1, 2
2, 4, # 3, 4
2, 4, # 5, 6
8, 8, # 7, 8
1, 2, # 9,10
1, 2, # 11,12
4, 4, # 13, 14
8, 8] # 15, 16
data["num_vehicles"] = 4
data["vehicle_capacity"] = 15
data["depot"] = 0
return data
"""Stores the data for the problem"""
data = {}
# Locations in block unit
_locations = \
[(4, 4), # depot
(2, 0), (8, 0), # locations to visit
(0, 1), (1, 1),
(5, 2), (7, 2),
(3, 3), (6, 3),
(5, 5), (8, 5),
(1, 6), (2, 6),
(3, 7), (6, 7),
(0, 8), (7, 8)]
# Compute locations in meters using the block dimension defined as follow
# Manhattan average block: 750ft x 264ft -> 228m x 80m
# here we use: 114m x 80m city block
# src: https://nyti.ms/2GDoRIe 'NY Times: Know Your distance'
data['locations'] = [(l[0] * 114, l[1] * 80) for l in _locations]
data['num_locations'] = len(data['locations'])
data['demands'] = \
[0, # depot
1, 1, # 1, 2
2, 4, # 3, 4
2, 4, # 5, 6
8, 8, # 7, 8
1, 2, # 9,10
1, 2, # 11,12
4, 4, # 13, 14
8, 8] # 15, 16
data['num_vehicles'] = 4
data['vehicle_capacity'] = 15
data['depot'] = 0
return data
#######################
# Problem Constraints #
#######################
def manhattan_distance(position_1, position_2):
"""Computes the Manhattan distance between two points"""
return (abs(position_1[0] - position_2[0]) +
abs(position_1[1] - position_2[1]))
"""Computes the Manhattan distance between two points"""
return (
abs(position_1[0] - position_2[0]) + abs(position_1[1] - position_2[1]))
def create_distance_evaluator(data):
"""Creates callback to return distance between points."""
_distances = {}
# precompute distance between location to have distance callback in O(1)
for from_node in xrange(data["num_locations"]):
_distances[from_node] = {}
for to_node in xrange(data["num_locations"]):
if from_node == to_node:
_distances[from_node][to_node] = 0
else:
_distances[from_node][to_node] = (manhattan_distance(
data["locations"][from_node], data["locations"][to_node]))
"""Creates callback to return distance between points."""
_distances = {}
# precompute distance between location to have distance callback in O(1)
for from_node in xrange(data['num_locations']):
_distances[from_node] = {}
for to_node in xrange(data['num_locations']):
if from_node == to_node:
_distances[from_node][to_node] = 0
else:
_distances[from_node][to_node] = (
manhattan_distance(data['locations'][from_node],
data['locations'][to_node]))
def distance_evaluator(from_node, to_node):
"""Returns the manhattan distance between the two nodes"""
return _distances[from_node][to_node]
def distance_evaluator(manager, from_node, to_node):
"""Returns the manhattan distance between the two nodes"""
return _distances[manager.IndexToNode(from_node)][manager.IndexToNode(
to_node)]
return distance_evaluator
return distance_evaluator
def create_demand_evaluator(data):
"""Creates callback to get demands at each location."""
_demands = data["demands"]
"""Creates callback to get demands at each location."""
_demands = data['demands']
def demand_evaluator(from_node, to_node):
"""Returns the demand of the current node"""
del to_node
return _demands[from_node]
def demand_evaluator(manager, node):
"""Returns the demand of the current node"""
return _demands[manager.IndexToNode(node)]
return demand_evaluator
return demand_evaluator
def add_capacity_constraints(routing, data, demand_evaluator):
"""Adds capacity constraint"""
capacity = 'Capacity'
routing.AddDimension(
demand_evaluator,
0, # null capacity slack
data["vehicle_capacity"],
True, # start cumul to zero
capacity)
def add_capacity_constraints(routing, data, demand_evaluator_index):
"""Adds capacity constraint"""
capacity = 'Capacity'
routing.AddDimension(
demand_evaluator_index,
0, # null capacity slack
data['vehicle_capacity'],
True, # start cumul to zero
capacity)
###########
# Printer #
###########
def print_solution(data, routing, assignment):
"""Prints assignment on console"""
print('Objective: {}'.format(assignment.ObjectiveValue()))
total_distance = 0
total_load = 0
capacity_dimension = routing.GetDimensionOrDie('Capacity')
for vehicle_id in xrange(data["num_vehicles"]):
index = routing.Start(vehicle_id)
plan_output = 'Route for vehicle {}:\n'.format(vehicle_id)
distance = 0
while not routing.IsEnd(index):
load_var = capacity_dimension.CumulVar(index)
plan_output += ' {} Load({}) -> '.format(
routing.IndexToNode(index), assignment.Value(load_var))
previous_index = index
index = assignment.Value(routing.NextVar(index))
distance += routing.GetArcCostForVehicle(previous_index, index,
vehicle_id)
load_var = capacity_dimension.CumulVar(index)
plan_output += ' {0} Load({1})\n'.format(
routing.IndexToNode(index), assignment.Value(load_var))
plan_output += 'Distance of the route: {}m\n'.format(distance)
plan_output += 'Load of the route: {}\n'.format(
assignment.Value(load_var))
print(plan_output)
total_distance += distance
total_load += assignment.Value(load_var)
print('Total Distance of all routes: {}m'.format(total_distance))
print('Total Load of all routes: {}'.format(total_load))
def print_solution(data, routing, manager, assignment): # pylint:disable=too-many-locals
"""Prints assignment on console"""
print('Objective: {}'.format(assignment.ObjectiveValue()))
total_distance = 0
total_load = 0
capacity_dimension = routing.GetDimensionOrDie('Capacity')
for vehicle_id in xrange(data['num_vehicles']):
index = routing.Start(vehicle_id)
plan_output = 'Route for vehicle {}:\n'.format(vehicle_id)
distance = 0
while not routing.IsEnd(index):
load_var = capacity_dimension.CumulVar(index)
plan_output += ' {} Load({}) -> '.format(
manager.IndexToNode(index), assignment.Value(load_var))
previous_index = index
index = assignment.Value(routing.NextVar(index))
distance += routing.GetArcCostForVehicle(previous_index, index,
vehicle_id)
load_var = capacity_dimension.CumulVar(index)
plan_output += ' {0} Load({1})\n'.format(
manager.IndexToNode(index), assignment.Value(load_var))
plan_output += 'Distance of the route: {}m\n'.format(distance)
plan_output += 'Load of the route: {}\n'.format(assignment.Value(load_var))
print(plan_output)
total_distance += distance
total_load += assignment.Value(load_var)
print('Total Distance of all routes: {}m'.format(total_distance))
print('Total Load of all routes: {}'.format(total_load))
########
# Main #
########
def main():
"""Entry point of the program"""
# Instantiate the data problem.
data = create_data_model()
"""Entry point of the program"""
# Instantiate the data problem.
data = create_data_model()
# Create Routing Model
routing = pywrapcp.RoutingModel(data["num_locations"],
data["num_vehicles"], data["depot"])
# Define weight of each edge
distance_evaluator = create_distance_evaluator(data)
routing.SetArcCostEvaluatorOfAllVehicles(distance_evaluator)
# Add Capacity constraint
demand_evaluator = create_demand_evaluator(data)
add_capacity_constraints(routing, data, demand_evaluator)
# Create the routing index manager
manager = pywrapcp.RoutingIndexManager(data['num_locations'],
data['num_vehicles'], data['depot'])
# Setting first solution heuristic (cheapest addition).
search_parameters = pywrapcp.RoutingModel.DefaultSearchParameters()
search_parameters.first_solution_strategy = (
routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC) # pylint: disable=no-member
# Solve the problem.
assignment = routing.SolveWithParameters(search_parameters)
print_solution(data, routing, assignment)
# Create Routing Model
routing = pywrapcp.RoutingModel(manager)
# Define weight of each edge
distance_evaluator = routing.RegisterTransitCallback(
partial(create_distance_evaluator(data), manager))
routing.SetArcCostEvaluatorOfAllVehicles(distance_evaluator)
# Add Capacity constraint
demand_evaluator_index = routing.RegisterUnaryTransitCallback(
partial(create_demand_evaluator(data), manager))
add_capacity_constraints(routing, data, demand_evaluator_index)
# Setting first solution heuristic (cheapest addition).
search_parameters = pywrapcp.DefaultRoutingSearchParameters()
search_parameters.first_solution_strategy = (
routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC) # pylint: disable=no-member
# Solve the problem.
assignment = routing.SolveWithParameters(search_parameters)
print_solution(data, routing, manager, assignment)
if __name__ == '__main__':
main()
main()

520
examples/python/cvrp_reload.py
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@@ -24,7 +24,10 @@
"""
from __future__ import print_function
from functools import partial
from six.moves import xrange
from ortools.constraint_solver import pywrapcp
from ortools.constraint_solver import routing_enums_pb2
@@ -33,304 +36,315 @@ from ortools.constraint_solver import routing_enums_pb2
# Problem Data Definition #
###########################
def create_data_model():
"""Stores the data for the problem"""
data = {}
_capacity = 15
# Locations in block unit
_locations = [
(4, 4), # depot
(4, 4), # unload depot_prime
(4, 4), # unload depot_second
(4, 4), # unload depot_fourth
(4, 4), # unload depot_fourth
(4, 4), # unload depot_fifth
(2, 0),
(8, 0), # locations to visit
(0, 1),
(1, 1),
(5, 2),
(7, 2),
(3, 3),
(6, 3),
(5, 5),
(8, 5),
(1, 6),
(2, 6),
(3, 7),
(6, 7),
(0, 8),
(7, 8)
]
# Compute locations in meters using the block dimension defined as follow
# Manhattan average block: 750ft x 264ft -> 228m x 80m
# here we use: 114m x 80m city block
# src: https://nyti.ms/2GDoRIe "NY Times: Know Your distance"
data["locations"] = [(l[0] * 114, l[1] * 80) for l in _locations]
data["num_locations"] = len(data["locations"])
data["demands"] = \
[0, # depot
-_capacity,
-_capacity,
-_capacity,
-_capacity,
-_capacity,
1, 1, # 1, 2
2, 4, # 3, 4
2, 4, # 5, 6
8, 8, # 7, 8
1, 2, # 9,10
1, 2, # 11,12
4, 4, # 13, 14
8, 8] # 15, 16
data["time_per_demand_unit"] = 5 # 5 minutes/unit
data["time_windows"] = \
[(0, 0), # depot
(0, 1000),
(0, 1000),
(0, 1000),
(0, 1000),
(0, 1000),
(75, 8500), (75, 8500), # 1, 2
(60, 7000), (45, 5500), # 3, 4
(0, 8000), (50, 6000), # 5, 6
(0, 1000), (10, 2000), # 7, 8
(0, 1000), (75, 8500), # 9, 10
(85, 9500), (5, 1500), # 11, 12
(15, 2500), (10, 2000), # 13, 14
(45, 5500), (30, 4000)] # 15, 16
data["num_vehicles"] = 3
data["vehicle_capacity"] = _capacity
data[
"vehicle_speed"] = 5 * 60 / 3.6 # Travel speed: 5km/h to convert in m/min
data["depot"] = 0
return data
"""Stores the data for the problem"""
data = {}
_capacity = 15
# Locations in block unit
_locations = [
(4, 4), # depot
(4, 4), # unload depot_prime
(4, 4), # unload depot_second
(4, 4), # unload depot_fourth
(4, 4), # unload depot_fourth
(4, 4), # unload depot_fifth
(2, 0),
(8, 0), # locations to visit
(0, 1),
(1, 1),
(5, 2),
(7, 2),
(3, 3),
(6, 3),
(5, 5),
(8, 5),
(1, 6),
(2, 6),
(3, 7),
(6, 7),
(0, 8),
(7, 8)
]
# Compute locations in meters using the block dimension defined as follow
# Manhattan average block: 750ft x 264ft -> 228m x 80m
# here we use: 114m x 80m city block
# src: https://nyti.ms/2GDoRIe 'NY Times: Know Your distance'
data['locations'] = [(l[0] * 114, l[1] * 80) for l in _locations]
data['num_locations'] = len(data['locations'])
data['demands'] = \
[0, # depot
-_capacity,
-_capacity,
-_capacity,
-_capacity,
-_capacity,
1, 1, # 1, 2
2, 4, # 3, 4
2, 4, # 5, 6
8, 8, # 7, 8
1, 2, # 9,10
1, 2, # 11,12
4, 4, # 13, 14
8, 8] # 15, 16
data['time_per_demand_unit'] = 5 # 5 minutes/unit
data['time_windows'] = \
[(0, 0), # depot
(0, 1000),
(0, 1000),
(0, 1000),
(0, 1000),
(0, 1000),
(75, 8500), (75, 8500), # 1, 2
(60, 7000), (45, 5500), # 3, 4
(0, 8000), (50, 6000), # 5, 6
(0, 1000), (10, 2000), # 7, 8
(0, 1000), (75, 8500), # 9, 10
(85, 9500), (5, 1500), # 11, 12
(15, 2500), (10, 2000), # 13, 14
(45, 5500), (30, 4000)] # 15, 16
data['num_vehicles'] = 3
data['vehicle_capacity'] = _capacity
data[
'vehicle_speed'] = 5 * 60 / 3.6 # Travel speed: 5km/h to convert in m/min
data['depot'] = 0
return data
#######################
# Problem Constraints #
#######################
def manhattan_distance(position_1, position_2):
"""Computes the Manhattan distance between two points"""
return (abs(position_1[0] - position_2[0]) +
abs(position_1[1] - position_2[1]))
"""Computes the Manhattan distance between two points"""
return (
abs(position_1[0] - position_2[0]) + abs(position_1[1] - position_2[1]))
def create_distance_evaluator(data):
"""Creates callback to return distance between points."""
_distances = {}
# precompute distance between location to have distance callback in O(1)
for from_node in xrange(data["num_locations"]):
_distances[from_node] = {}
for to_node in xrange(data["num_locations"]):
if from_node == to_node:
_distances[from_node][to_node] = 0
else:
_distances[from_node][to_node] = (manhattan_distance(
data["locations"][from_node], data["locations"][to_node]))
"""Creates callback to return distance between points."""
_distances = {}
# precompute distance between location to have distance callback in O(1)
for from_node in xrange(data['num_locations']):
_distances[from_node] = {}
for to_node in xrange(data['num_locations']):
if from_node == to_node:
_distances[from_node][to_node] = 0
else:
_distances[from_node][to_node] = (
manhattan_distance(data['locations'][from_node],
data['locations'][to_node]))
def distance_evaluator(from_node, to_node):
"""Returns the manhattan distance between the two nodes"""
return _distances[from_node][to_node]
def distance_evaluator(manager, from_node, to_node):
"""Returns the manhattan distance between the two nodes"""
return _distances[manager.IndexToNode(from_node)][manager.IndexToNode(
to_node)]
return distance_evaluator
return distance_evaluator
def add_distance_dimension(routing, distance_evaluator):
"""Add Global Span constraint"""
distance = 'Distance'
routing.AddDimension(
distance_evaluator,
0, # null slack
10000, # maximum distance per vehicle
True, # start cumul to zero
distance)
distance_dimension = routing.GetDimensionOrDie(distance)
# Try to minimize the max distance among vehicles.
# /!\ It doesn't mean the standard deviation is minimized
distance_dimension.SetGlobalSpanCostCoefficient(100)
def add_distance_dimension(routing, distance_evaluator_index):
"""Add Global Span constraint"""
distance = 'Distance'
routing.AddDimension(
distance_evaluator_index,
0, # null slack
10000, # maximum distance per vehicle
True, # start cumul to zero
distance)
distance_dimension = routing.GetDimensionOrDie(distance)
# Try to minimize the max distance among vehicles.
# /!\ It doesn't mean the standard deviation is minimized
distance_dimension.SetGlobalSpanCostCoefficient(100)
def create_demand_evaluator(data):
"""Creates callback to get demands at each location."""
_demands = data["demands"]
"""Creates callback to get demands at each location."""
_demands = data['demands']
def demand_evaluator(from_node, to_node):
"""Returns the demand of the current node"""
del to_node
return _demands[from_node]
def demand_evaluator(manager, from_node):
"""Returns the demand of the current node"""
return _demands[manager.IndexToNode(from_node)]
return demand_evaluator
return demand_evaluator
def add_capacity_constraints(routing, data, demand_evaluator):
"""Adds capacity constraint"""
vehicle_capacity = data["vehicle_capacity"]
capacity = 'Capacity'
routing.AddDimension(
demand_evaluator,
0, # Null slack
vehicle_capacity,
True, # start cumul to zero
capacity)
def add_capacity_constraints(routing, manager, data, demand_evaluator_index):
"""Adds capacity constraint"""
vehicle_capacity = data['vehicle_capacity']
capacity = 'Capacity'
routing.AddDimension(
demand_evaluator_index,
0, # Null slack
vehicle_capacity,
True, # start cumul to zero
capacity)
# Add Slack for reseting to zero unload depot nodes.
# e.g. vehicle with load 10/15 arrives at node 1 (depot unload)
# so we have CumulVar = 10(current load) + -15(unload) + 5(slack) = 0.
capacity_dimension = routing.GetDimensionOrDie(capacity)
for node_index in [1, 2, 3, 4, 5]:
index = routing.NodeToIndex(node_index)
capacity_dimension.SlackVar(index).SetRange(0, vehicle_capacity)
routing.AddDisjunction([node_index], 0)
# Add Slack for reseting to zero unload depot nodes.
# e.g. vehicle with load 10/15 arrives at node 1 (depot unload)
# so we have CumulVar = 10(current load) + -15(unload) + 5(slack) = 0.
capacity_dimension = routing.GetDimensionOrDie(capacity)
for node_index in [1, 2, 3, 4, 5]:
index = manager.NodeToIndex(node_index)
capacity_dimension.SlackVar(index).SetRange(0, vehicle_capacity)
routing.AddDisjunction([node_index], 0)
def create_time_evaluator(data):
"""Creates callback to get total times between locations."""
"""Creates callback to get total times between locations."""
def service_time(data, node):
"""Gets the service time for the specified location."""
return abs(data["demands"][node]) * data["time_per_demand_unit"]
def service_time(data, node):
"""Gets the service time for the specified location."""
return abs(data['demands'][node]) * data['time_per_demand_unit']
def travel_time(data, from_node, to_node):
"""Gets the travel times between two locations."""
if from_node == to_node:
travel_time = 0
else:
travel_time = manhattan_distance(
data["locations"][from_node],
data["locations"][to_node]) / data["vehicle_speed"]
return travel_time
def travel_time(data, from_node, to_node):
"""Gets the travel times between two locations."""
if from_node == to_node:
travel_time = 0
else:
travel_time = manhattan_distance(
data['locations'][from_node],
data['locations'][to_node]) / data['vehicle_speed']
return travel_time
_total_time = {}
# precompute total time to have time callback in O(1)
for from_node in xrange(data["num_locations"]):
_total_time[from_node] = {}
for to_node in xrange(data["num_locations"]):
if from_node == to_node:
_total_time[from_node][to_node] = 0
else:
_total_time[from_node][to_node] = int(
service_time(data, from_node) +
travel_time(data, from_node, to_node))
_total_time = {}
# precompute total time to have time callback in O(1)
for from_node in xrange(data['num_locations']):
_total_time[from_node] = {}
for to_node in xrange(data['num_locations']):
if from_node == to_node:
_total_time[from_node][to_node] = 0
else:
_total_time[from_node][to_node] = int(
service_time(data, from_node) +
travel_time(data, from_node, to_node))
def time_evaluator(from_node, to_node):
"""Returns the total time between the two nodes"""
return _total_time[from_node][to_node]
def time_evaluator(manager, from_node, to_node):
"""Returns the total time between the two nodes"""
return _total_time[manager.IndexToNode(from_node)][manager.IndexToNode(
to_node)]
return time_evaluator
return time_evaluator
def add_time_window_constraints(routing, data, time_evaluator):
"""Add Time windows constraint"""
time = 'Time'
horizon = 1500
routing.AddDimension(
time_evaluator,
horizon, # allow waiting time
horizon, # maximum time per vehicle
False, # don't force start cumul to zero since we are giving TW to start nodes
time)
time_dimension = routing.GetDimensionOrDie(time)
# Add time window constraints for each location except depot
# and "copy" the slack var in the solution object (aka Assignment) to print it
for location_idx, time_window in enumerate(data["time_windows"]):
if location_idx == 0:
continue
index = routing.NodeToIndex(location_idx)
time_dimension.CumulVar(index).SetRange(time_window[0], time_window[1])
routing.AddToAssignment(time_dimension.SlackVar(index))
# Add time window constraints for each vehicle start node
# and "copy" the slack var in the solution object (aka Assignment) to print it
for vehicle_id in xrange(data["num_vehicles"]):
index = routing.Start(vehicle_id)
time_dimension.CumulVar(index).SetRange(data["time_windows"][0][0],
data["time_windows"][0][1])
routing.AddToAssignment(time_dimension.SlackVar(index))
# Warning: Slack var is not defined for vehicle's end node
#routing.AddToAssignment(time_dimension.SlackVar(self.routing.End(vehicle_id)))
def add_time_window_constraints(routing, manager, data, time_evaluator):
"""Add Time windows constraint"""
time = 'Time'
horizon = 1500
routing.AddDimension(
time_evaluator,
horizon, # allow waiting time
horizon, # maximum time per vehicle
False, # don't force start cumul to zero since we are giving TW to start nodes
time)
time_dimension = routing.GetDimensionOrDie(time)
# Add time window constraints for each location except depot
# and 'copy' the slack var in the solution object (aka Assignment) to print it
for location_idx, time_window in enumerate(data['time_windows']):
if location_idx == 0:
continue
index = manager.NodeToIndex(location_idx)
time_dimension.CumulVar(index).SetRange(time_window[0], time_window[1])
routing.AddToAssignment(time_dimension.SlackVar(index))
# Add time window constraints for each vehicle start node
# and 'copy' the slack var in the solution object (aka Assignment) to print it
for vehicle_id in xrange(data['num_vehicles']):
index = routing.Start(vehicle_id)
time_dimension.CumulVar(index).SetRange(data['time_windows'][0][0],
data['time_windows'][0][1])
routing.AddToAssignment(time_dimension.SlackVar(index))
# Warning: Slack var is not defined for vehicle's end node
#routing.AddToAssignment(time_dimension.SlackVar(self.routing.End(vehicle_id)))
###########
# Printer #
###########
def print_solution(data, routing, assignment):
"""Prints assignment on console"""
print('Objective: {}'.format(assignment.ObjectiveValue()))
total_distance = 0
total_load = 0
total_time = 0
capacity_dimension = routing.GetDimensionOrDie('Capacity')
time_dimension = routing.GetDimensionOrDie('Time')
dropped = []
for order in xrange(0, routing.nodes()):
index = routing.NodeToIndex(order)
if assignment.Value(routing.NextVar(index)) == index:
dropped.append(order)
print('dropped orders: {}'.format(dropped))
def print_solution(data, manager, routing, assignment): # pylint:disable=too-many-locals
"""Prints assignment on console"""
print('Objective: {}'.format(assignment.ObjectiveValue()))
total_distance = 0
total_load = 0
total_time = 0
capacity_dimension = routing.GetDimensionOrDie('Capacity')
time_dimension = routing.GetDimensionOrDie('Time')
dropped = []
for order in xrange(0, routing.nodes()):
index = manager.NodeToIndex(order)
if assignment.Value(routing.NextVar(index)) == index:
dropped.append(order)
print('dropped orders: {}'.format(dropped))
for vehicle_id in xrange(data["num_vehicles"]):
index = routing.Start(vehicle_id)
plan_output = 'Route for vehicle {}:\n'.format(vehicle_id)
distance = 0
while not routing.IsEnd(index):
load_var = capacity_dimension.CumulVar(index)
time_var = time_dimension.CumulVar(index)
plan_output += ' {0} Load({1}) Time({2},{3}) ->'.format(
routing.IndexToNode(index), assignment.Value(load_var),
assignment.Min(time_var), assignment.Max(time_var))
previous_index = index
index = assignment.Value(routing.NextVar(index))
distance += routing.GetArcCostForVehicle(previous_index, index,
vehicle_id)
load_var = capacity_dimension.CumulVar(index)
time_var = time_dimension.CumulVar(index)
plan_output += ' {0} Load({1}) Time({2},{3})\n'.format(
routing.IndexToNode(index), assignment.Value(load_var),
assignment.Min(time_var), assignment.Max(time_var))
plan_output += 'Distance of the route: {}m\n'.format(distance)
plan_output += 'Load of the route: {}\n'.format(
assignment.Value(load_var))
plan_output += 'Time of the route: {}min\n'.format(
assignment.Value(time_var))
print(plan_output)
total_distance += distance
total_load += assignment.Value(load_var)
total_time += assignment.Value(time_var)
print('Total Distance of all routes: {}m'.format(total_distance))
print('Total Load of all routes: {}'.format(total_load))
print('Total Time of all routes: {}min'.format(total_time))
for vehicle_id in xrange(data['num_vehicles']):
index = routing.Start(vehicle_id)
plan_output = 'Route for vehicle {}:\n'.format(vehicle_id)
distance = 0
while not routing.IsEnd(index):
load_var = capacity_dimension.CumulVar(index)
time_var = time_dimension.CumulVar(index)
plan_output += ' {0} Load({1}) Time({2},{3}) ->'.format(
manager.IndexToNode(index), assignment.Value(load_var),
assignment.Min(time_var), assignment.Max(time_var))
previous_index = index
index = assignment.Value(routing.NextVar(index))
distance += routing.GetArcCostForVehicle(previous_index, index,
vehicle_id)
load_var = capacity_dimension.CumulVar(index)
time_var = time_dimension.CumulVar(index)
plan_output += ' {0} Load({1}) Time({2},{3})\n'.format(
manager.IndexToNode(index), assignment.Value(load_var),
assignment.Min(time_var), assignment.Max(time_var))
plan_output += 'Distance of the route: {}m\n'.format(distance)
plan_output += 'Load of the route: {}\n'.format(assignment.Value(load_var))
plan_output += 'Time of the route: {}min\n'.format(
assignment.Value(time_var))
print(plan_output)
total_distance += distance
total_load += assignment.Value(load_var)
total_time += assignment.Value(time_var)
print('Total Distance of all routes: {}m'.format(total_distance))
print('Total Load of all routes: {}'.format(total_load))
print('Total Time of all routes: {}min'.format(total_time))
########
# Main #
########
def main():
"""Entry point of the program"""
# Instantiate the data problem.
data = create_data_model()
"""Entry point of the program"""
# Instantiate the data problem.
data = create_data_model()
# Create Routing Model
routing = pywrapcp.RoutingModel(data["num_locations"],
data["num_vehicles"], data["depot"])
# Define weight of each edge
distance_evaluator = create_distance_evaluator(data)
routing.SetArcCostEvaluatorOfAllVehicles(distance_evaluator)
# Add Distance constraint to minimize the longuest route
add_distance_dimension(routing, distance_evaluator)
# Add Capacity constraint
demand_evaluator = create_demand_evaluator(data)
add_capacity_constraints(routing, data, demand_evaluator)
# Add Time Window constraint
time_evaluator = create_time_evaluator(data)
add_time_window_constraints(routing, data, time_evaluator)
# Create the routing index manager
manager = pywrapcp.RoutingIndexManager(data['num_locations'],
data['num_vehicles'], data['depot'])
# Setting first solution heuristic (cheapest addition).
search_parameters = pywrapcp.RoutingModel.DefaultSearchParameters()
search_parameters.first_solution_strategy = (
routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC) # pylint: disable=no-member
# Solve the problem.
assignment = routing.SolveWithParameters(search_parameters)
print_solution(data, routing, assignment)
# Create Routing Model
routing = pywrapcp.RoutingModel(manager)
# Define weight of each edge
distance_evaluator_index = routing.RegisterTransitCallback(
partial(create_distance_evaluator(data), manager))
routing.SetArcCostEvaluatorOfAllVehicles(distance_evaluator_index)
# Add Distance constraint to minimize the longuest route
add_distance_dimension(routing, distance_evaluator_index)
# Add Capacity constraint
demand_evaluator_index = routing.RegisterUnaryTransitCallback(
partial(create_demand_evaluator(data), manager))
add_capacity_constraints(routing, manager, data, demand_evaluator_index)
# Add Time Window constraint
time_evaluator_index = routing.RegisterTransitCallback(
partial(create_time_evaluator(data), manager))
add_time_window_constraints(routing, manager, data, time_evaluator_index)
# Setting first solution heuristic (cheapest addition).
search_parameters = pywrapcp.DefaultRoutingSearchParameters()
search_parameters.first_solution_strategy = (
routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC) # pylint: disable=no-member
# Solve the problem.
assignment = routing.SolveWithParameters(search_parameters)
print_solution(data, manager, routing, assignment)
if __name__ == '__main__':
main()
main()

413
examples/python/cvrptw.py
View File

@@ -24,7 +24,10 @@
"""
from __future__ import print_function
from functools import partial
from six.moves import xrange
from ortools.constraint_solver import pywrapcp
from ortools.constraint_solver import routing_enums_pb2
@@ -33,248 +36,256 @@ from ortools.constraint_solver import routing_enums_pb2
# Problem Data Definition #
###########################
def create_data_model():
"""Stores the data for the problem"""
data = {}
# Locations in block unit
_locations = \
[(4, 4), # depot
(2, 0), (8, 0), # locations to visit
(0, 1), (1, 1),
(5, 2), (7, 2),
(3, 3), (6, 3),
(5, 5), (8, 5),
(1, 6), (2, 6),
(3, 7), (6, 7),
(0, 8), (7, 8)]
# Compute locations in meters using the block dimension defined as follow
# Manhattan average block: 750ft x 264ft -> 228m x 80m
# here we use: 114m x 80m city block
# src: https://nyti.ms/2GDoRIe "NY Times: Know Your distance"
data["locations"] = [(l[0] * 114, l[1] * 80) for l in _locations]
data["num_locations"] = len(data["locations"])
data["time_windows"] = \
[(0, 0),
(75, 85), (75, 85), # 1, 2
(60, 70), (45, 55), # 3, 4
(0, 8), (50, 60), # 5, 6
(0, 10), (10, 20), # 7, 8
(0, 10), (75, 85), # 9, 10
(85, 95), (5, 15), # 11, 12
(15, 25), (10, 20), # 13, 14
(45, 55), (30, 40)] # 15, 16
data["demands"] = \
[0, # depot
1, 1, # 1, 2
2, 4, # 3, 4
2, 4, # 5, 6
8, 8, # 7, 8
1, 2, # 9,10
1, 2, # 11,12
4, 4, # 13, 14
8, 8] # 15, 16
data["time_per_demand_unit"] = 5 # 5 minutes/unit
data["num_vehicles"] = 4
data["vehicle_capacity"] = 15
data[
"vehicle_speed"] = 5 * 60 / 3.6 # Travel speed: 5km/h to convert in m/min
data["depot"] = 0
return data
"""Stores the data for the problem"""
data = {}
# Locations in block unit
_locations = \
[(4, 4), # depot
(2, 0), (8, 0), # locations to visit
(0, 1), (1, 1),
(5, 2), (7, 2),
(3, 3), (6, 3),
(5, 5), (8, 5),
(1, 6), (2, 6),
(3, 7), (6, 7),
(0, 8), (7, 8)]
# Compute locations in meters using the block dimension defined as follow
# Manhattan average block: 750ft x 264ft -> 228m x 80m
# here we use: 114m x 80m city block
# src: https://nyti.ms/2GDoRIe "NY Times: Know Your distance"
data['locations'] = [(l[0] * 114, l[1] * 80) for l in _locations]
data['num_locations'] = len(data['locations'])
data['time_windows'] = \
[(0, 0),
(75, 85), (75, 85), # 1, 2
(60, 70), (45, 55), # 3, 4
(0, 8), (50, 60), # 5, 6
(0, 10), (10, 20), # 7, 8
(0, 10), (75, 85), # 9, 10
(85, 95), (5, 15), # 11, 12
(15, 25), (10, 20), # 13, 14
(45, 55), (30, 40)] # 15, 16
data['demands'] = \
[0, # depot
1, 1, # 1, 2
2, 4, # 3, 4
2, 4, # 5, 6
8, 8, # 7, 8
1, 2, # 9,10
1, 2, # 11,12
4, 4, # 13, 14
8, 8] # 15, 16
data['time_per_demand_unit'] = 5 # 5 minutes/unit
data['num_vehicles'] = 4
data['vehicle_capacity'] = 15
data['vehicle_speed'] = 83 # Travel speed: 5km/h converted in m/min
data['depot'] = 0
return data
#######################
# Problem Constraints #
#######################
def manhattan_distance(position_1, position_2):
"""Computes the Manhattan distance between two points"""
return (abs(position_1[0] - position_2[0]) +
abs(position_1[1] - position_2[1]))
"""Computes the Manhattan distance between two points"""
return (
abs(position_1[0] - position_2[0]) + abs(position_1[1] - position_2[1]))
def create_distance_evaluator(data):
"""Creates callback to return distance between points."""
_distances = {}
# precompute distance between location to have distance callback in O(1)
for from_node in xrange(data["num_locations"]):
_distances[from_node] = {}
for to_node in xrange(data["num_locations"]):
if from_node == to_node:
_distances[from_node][to_node] = 0
else:
_distances[from_node][to_node] = (manhattan_distance(
data["locations"][from_node], data["locations"][to_node]))
"""Creates callback to return distance between points."""
_distances = {}
# precompute distance between location to have distance callback in O(1)
for from_node in xrange(data['num_locations']):
_distances[from_node] = {}
for to_node in xrange(data['num_locations']):
if from_node == to_node:
_distances[from_node][to_node] = 0
else:
_distances[from_node][to_node] = (
manhattan_distance(data['locations'][from_node],
data['locations'][to_node]))
def distance_evaluator(from_node, to_node):
"""Returns the manhattan distance between the two nodes"""
return _distances[from_node][to_node]
def distance_evaluator(manager, from_node, to_node):
"""Returns the manhattan distance between the two nodes"""
return _distances[manager.IndexToNode(from_node)][manager.IndexToNode(
to_node)]
return distance_evaluator
return distance_evaluator
def create_demand_evaluator(data):
"""Creates callback to get demands at each location."""
_demands = data["demands"]
"""Creates callback to get demands at each location."""
_demands = data['demands']
def demand_evaluator(from_node, to_node):
"""Returns the demand of the current node"""
del to_node
return _demands[from_node]
def demand_evaluator(manager, node):
"""Returns the demand of the current node"""
return _demands[manager.IndexToNode(node)]
return demand_evaluator
return demand_evaluator
def add_capacity_constraints(routing, data, demand_evaluator):
"""Adds capacity constraint"""
capacity = 'Capacity'
routing.AddDimension(
demand_evaluator,
0, # null capacity slack
data["vehicle_capacity"],
True, # start cumul to zero
capacity)
def add_capacity_constraints(routing, data, demand_evaluator_index):
"""Adds capacity constraint"""
capacity = 'Capacity'
routing.AddDimension(
demand_evaluator_index,
0, # null capacity slack
data['vehicle_capacity'],
True, # start cumul to zero
capacity)
def create_time_evaluator(data):
"""Creates callback to get total times between locations."""
"""Creates callback to get total times between locations."""
def service_time(data, node):
"""Gets the service time for the specified location."""
return data["demands"][node] * data["time_per_demand_unit"]
def service_time(data, node):
"""Gets the service time for the specified location."""
return data['demands'][node] * data['time_per_demand_unit']
def travel_time(data, from_node, to_node):
"""Gets the travel times between two locations."""
if from_node == to_node:
travel_time = 0
else:
travel_time = manhattan_distance(
data["locations"][from_node],
data["locations"][to_node]) / data["vehicle_speed"]
return travel_time
def travel_time(data, from_node, to_node):
"""Gets the travel times between two locations."""
if from_node == to_node:
travel_time = 0
else:
travel_time = manhattan_distance(
data['locations'][from_node],
data['locations'][to_node]) / data['vehicle_speed']
return travel_time
_total_time = {}
# precompute total time to have time callback in O(1)
for from_node in xrange(data["num_locations"]):
_total_time[from_node] = {}
for to_node in xrange(data["num_locations"]):
if from_node == to_node:
_total_time[from_node][to_node] = 0
else:
_total_time[from_node][to_node] = int(
service_time(data, from_node) +
travel_time(data, from_node, to_node))
_total_time = {}
# precompute total time to have time callback in O(1)
for from_node in xrange(data['num_locations']):
_total_time[from_node] = {}
for to_node in xrange(data['num_locations']):
if from_node == to_node:
_total_time[from_node][to_node] = 0
else:
_total_time[from_node][to_node] = int(
service_time(data, from_node) +
travel_time(data, from_node, to_node))
def time_evaluator(from_node, to_node):
"""Returns the total time between the two nodes"""
return _total_time[from_node][to_node]
def time_evaluator(manager, from_node, to_node):
"""Returns the total time between the two nodes"""
return _total_time[manager.IndexToNode(from_node)][manager.IndexToNode(
to_node)]
return time_evaluator
return time_evaluator
def add_time_window_constraints(routing, data, time_evaluator):
"""Add Global Span constraint"""
time = 'Time'
horizon = 120
routing.AddDimension(
time_evaluator,
horizon, # allow waiting time
horizon, # maximum time per vehicle
False, # don't force start cumul to zero since we are giving TW to start nodes
time)
time_dimension = routing.GetDimensionOrDie(time)
# Add time window constraints for each location except depot
# and "copy" the slack var in the solution object (aka Assignment) to print it
for location_idx, time_window in enumerate(data["time_windows"]):
if location_idx == 0:
continue
index = routing.NodeToIndex(location_idx)
time_dimension.CumulVar(index).SetRange(time_window[0], time_window[1])
routing.AddToAssignment(time_dimension.SlackVar(index))
# Add time window constraints for each vehicle start node
# and "copy" the slack var in the solution object (aka Assignment) to print it
for vehicle_id in xrange(data["num_vehicles"]):
index = routing.Start(vehicle_id)
time_dimension.CumulVar(index).SetRange(data["time_windows"][0][0],
data["time_windows"][0][1])
routing.AddToAssignment(time_dimension.SlackVar(index))
# Warning: Slack var is not defined for vehicle's end node
#routing.AddToAssignment(time_dimension.SlackVar(self.routing.End(vehicle_id)))
def add_time_window_constraints(routing, manager, data, time_evaluator_index):
"""Add Global Span constraint"""
time = 'Time'
horizon = 120
routing.AddDimension(
time_evaluator_index,
horizon, # allow waiting time
horizon, # maximum time per vehicle
False, # don't force start cumul to zero since we are giving TW to start nodes
time)
time_dimension = routing.GetDimensionOrDie(time)
# Add time window constraints for each location except depot
# and 'copy' the slack var in the solution object (aka Assignment) to print it
for location_idx, time_window in enumerate(data['time_windows']):
if location_idx == 0:
continue
index = manager.NodeToIndex(location_idx)
time_dimension.CumulVar(index).SetRange(time_window[0], time_window[1])
routing.AddToAssignment(time_dimension.SlackVar(index))
# Add time window constraints for each vehicle start node
# and 'copy' the slack var in the solution object (aka Assignment) to print it
for vehicle_id in xrange(data['num_vehicles']):
index = routing.Start(vehicle_id)
time_dimension.CumulVar(index).SetRange(data['time_windows'][0][0],
data['time_windows'][0][1])
routing.AddToAssignment(time_dimension.SlackVar(index))
# Warning: Slack var is not defined for vehicle's end node
#routing.AddToAssignment(time_dimension.SlackVar(self.routing.End(vehicle_id)))
###########
# Printer #
###########
def print_solution(data, routing, assignment): # pylint:disable=too-many-locals
"""Prints assignment on console"""
print('Objective: {}'.format(assignment.ObjectiveValue()))
total_distance = 0
total_load = 0
total_time = 0
capacity_dimension = routing.GetDimensionOrDie('Capacity')
time_dimension = routing.GetDimensionOrDie('Time')
for vehicle_id in xrange(data["num_vehicles"]):
index = routing.Start(vehicle_id)
plan_output = 'Route for vehicle {}:\n'.format(vehicle_id)
distance = 0
while not routing.IsEnd(index):
load_var = capacity_dimension.CumulVar(index)
time_var = time_dimension.CumulVar(index)
slack_var = time_dimension.SlackVar(index)
plan_output += ' {0} Load({1}) Time({2},{3}) Slack({4},{5}) ->'.format(
routing.IndexToNode(index), assignment.Value(load_var),
assignment.Min(time_var), assignment.Max(time_var),
assignment.Min(slack_var), assignment.Max(slack_var))
previous_index = index
index = assignment.Value(routing.NextVar(index))
distance += routing.GetArcCostForVehicle(previous_index, index,
vehicle_id)
load_var = capacity_dimension.CumulVar(index)
time_var = time_dimension.CumulVar(index)
slack_var = time_dimension.SlackVar(index)
plan_output += ' {0} Load({1}) Time({2},{3})\n'.format(
routing.IndexToNode(index), assignment.Value(load_var),
assignment.Min(time_var), assignment.Max(time_var))
plan_output += 'Distance of the route: {0}m\n'.format(distance)
plan_output += 'Load of the route: {}\n'.format(
assignment.Value(load_var))
plan_output += 'Time of the route: {}\n'.format(
assignment.Value(time_var))
print(plan_output)
total_distance += distance
total_load += assignment.Value(load_var)
total_time += assignment.Value(time_var)
print('Total Distance of all routes: {0}m'.format(total_distance))
print('Total Load of all routes: {}'.format(total_load))
print('Total Time of all routes: {0}min'.format(total_time))
def print_solution(data, manager, routing, assignment): # pylint:disable=too-many-locals
"""Prints assignment on console"""
print('Objective: {}'.format(assignment.ObjectiveValue()))
total_distance = 0
total_load = 0
total_time = 0
capacity_dimension = routing.GetDimensionOrDie('Capacity')
time_dimension = routing.GetDimensionOrDie('Time')
for vehicle_id in xrange(data['num_vehicles']):
index = routing.Start(vehicle_id)
plan_output = 'Route for vehicle {}:\n'.format(vehicle_id)
distance = 0
while not routing.IsEnd(index):
load_var = capacity_dimension.CumulVar(index)
time_var = time_dimension.CumulVar(index)
slack_var = time_dimension.SlackVar(index)
plan_output += ' {0} Load({1}) Time({2},{3}) Slack({4},{5}) ->'.format(
manager.IndexToNode(index), assignment.Value(load_var),
assignment.Min(time_var), assignment.Max(time_var),
assignment.Min(slack_var), assignment.Max(slack_var))
previous_index = index
index = assignment.Value(routing.NextVar(index))
distance += routing.GetArcCostForVehicle(previous_index, index,
vehicle_id)
load_var = capacity_dimension.CumulVar(index)
time_var = time_dimension.CumulVar(index)
slack_var = time_dimension.SlackVar(index)
plan_output += ' {0} Load({1}) Time({2},{3})\n'.format(
manager.IndexToNode(index), assignment.Value(load_var),
assignment.Min(time_var), assignment.Max(time_var))
plan_output += 'Distance of the route: {0}m\n'.format(distance)
plan_output += 'Load of the route: {}\n'.format(assignment.Value(load_var))
plan_output += 'Time of the route: {}\n'.format(assignment.Value(time_var))
print(plan_output)
total_distance += distance
total_load += assignment.Value(load_var)
total_time += assignment.Value(time_var)
print('Total Distance of all routes: {0}m'.format(total_distance))
print('Total Load of all routes: {}'.format(total_load))
print('Total Time of all routes: {0}min'.format(total_time))
########
# Main #
########
def main():
"""Entry point of the program"""
# Instantiate the data problem.
data = create_data_model()
"""Entry point of the program"""
# Instantiate the data problem.
data = create_data_model()
# Create Routing Model
routing = pywrapcp.RoutingModel(data["num_locations"],
data["num_vehicles"], data["depot"])
# Define weight of each edge
distance_evaluator = create_distance_evaluator(data)
routing.SetArcCostEvaluatorOfAllVehicles(distance_evaluator)
# Add Capacity constraint
demand_evaluator = create_demand_evaluator(data)
add_capacity_constraints(routing, data, demand_evaluator)
# Add Time Window constraint
time_evaluator = create_time_evaluator(data)
add_time_window_constraints(routing, data, time_evaluator)
# Create the routing index manager
manager = pywrapcp.RoutingIndexManager(data['num_locations'],
data['num_vehicles'], data['depot'])
# Setting first solution heuristic (cheapest addition).
search_parameters = pywrapcp.RoutingModel.DefaultSearchParameters()
search_parameters.first_solution_strategy = (
routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC) # pylint: disable=no-member
# Solve the problem.
assignment = routing.SolveWithParameters(search_parameters)
print_solution(data, routing, assignment)
# Create Routing Model
routing = pywrapcp.RoutingModel(manager)
# Define weight of each edge
distance_evaluator_index = routing.RegisterTransitCallback(
partial(create_distance_evaluator(data), manager))
routing.SetArcCostEvaluatorOfAllVehicles(distance_evaluator_index)
# Add Capacity constraint
demand_evaluator_index = routing.RegisterUnaryTransitCallback(
partial(create_demand_evaluator(data), manager))
add_capacity_constraints(routing, data, demand_evaluator_index)
# Add Time Window constraint
time_evaluator_index = routing.RegisterTransitCallback(
partial(create_time_evaluator(data), manager))
add_time_window_constraints(routing, manager, data, time_evaluator_index)
# Setting first solution heuristic (cheapest addition).
search_parameters = pywrapcp.DefaultRoutingSearchParameters()
search_parameters.first_solution_strategy = (
routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC) # pylint: disable=no-member
# Solve the problem.
assignment = routing.SolveWithParameters(search_parameters)
print_solution(data, manager, routing, assignment)
if __name__ == '__main__':
main()
main()

841
examples/python/cvrptw_plot.py
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2
examples/python/hidato_sat.py
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@@ -1,4 +1,4 @@
# Copyright 2010-2018 Google LLC
# Copyright 2010-2017 Google
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at

145
examples/python/random_tsp.py Executable file
View File

@@ -0,0 +1,145 @@
# Copyright 2010-2018 Google LLC
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Traveling Salesman Sample.
This is a sample using the routing library python wrapper to solve a
Traveling Salesman Problem.
The description of the problem can be found here:
http://en.wikipedia.org/wiki/Travelling_salesman_problem.
The optimization engine uses local search to improve solutions, first
solutions being generated using a cheapest addition heuristic.
Optionally one can randomly forbid a set of random connections between nodes
(forbidden arcs).
"""
import argparse
from functools import partial
import random
from ortools.constraint_solver import routing_enums_pb2
from ortools.constraint_solver import pywrapcp
parser = argparse.ArgumentParser()
parser.add_argument(
'--tsp_size',
default=10,
type=int,
help='Size of Traveling Salesman Problem instance.')
parser.add_argument(
'--tsp_use_random_matrix',
default=True,
type=bool,
help='Use random cost matrix.')
parser.add_argument(
'--tsp_random_forbidden_connections',
default=0,
type=int,
help='Number of random forbidden connections.')
parser.add_argument(
'--tsp_random_seed', default=0, type=int, help='Random seed.')
# Cost/distance functions.
def Distance(manager, i, j):
"""Sample function."""
# Put your distance code here.
node_i = manager.IndexToNode(i)
node_j = manager.IndexToNode(j)
return node_i + node_j
class RandomMatrix(object):
"""Random matrix."""
def __init__(self, size, seed):
"""Initialize random matrix."""
rand = random.Random()
rand.seed(seed)
distance_max = 100
self.matrix = {}
for from_node in range(size):
self.matrix[from_node] = {}
for to_node in range(size):
if from_node == to_node:
self.matrix[from_node][to_node] = 0
else:
self.matrix[from_node][to_node] = rand.randrange(distance_max)
def Distance(self, manager, from_index, to_index):
return self.matrix[manager.IndexToNode(from_index)][manager.IndexToNode(
to_index)]
def main(args):
# Create routing model
if args.tsp_size > 0:
# TSP of size args.tsp_size
# Second argument = 1 to build a single tour (it's a TSP).
# Nodes are indexed from 0 to args_tsp_size - 1, by default the start of
# the route is node 0.
manager = pywrapcp.RoutingIndexManager(args.tsp_size, 1, 0)
routing = pywrapcp.RoutingModel(manager)
search_parameters = pywrapcp.DefaultRoutingSearchParameters()
# Setting first solution heuristic (cheapest addition).
search_parameters.first_solution_strategy = (
routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC)
# Setting the cost function.
# Put a callback to the distance accessor here. The callback takes two
# arguments (the from and to node indices) and returns the distance between
# these indices.
cost = 0
if args.tsp_use_random_matrix:
matrix = RandomMatrix(args.tsp_size, args.tsp_random_seed)
cost = routing.RegisterTransitCallback(partial(matrix.Distance, manager))
else:
cost = routing.RegisterTransitCallback(partial(Distance, manager))
routing.SetArcCostEvaluatorOfAllVehicles(cost)
# Forbid node connections (randomly).
rand = random.Random()
rand.seed(args.tsp_random_seed)
forbidden_connections = 0
while forbidden_connections < args.tsp_random_forbidden_connections:
from_node = rand.randrange(args.tsp_size - 1)
to_node = rand.randrange(args.tsp_size - 1) + 1
if routing.NextVar(from_node).Contains(to_node):
print('Forbidding connection ' + str(from_node) + ' -> ' + str(to_node))
routing.NextVar(from_node).RemoveValue(to_node)
forbidden_connections += 1
# Solve, returns a solution if any.
assignment = routing.Solve()
if assignment:
# Solution cost.
print(assignment.ObjectiveValue())
# Inspect solution.
# Only one route here; otherwise iterate from 0 to routing.vehicles() - 1
route_number = 0
node = routing.Start(route_number)
route = ''
while not routing.IsEnd(node):
route += str(node) + ' -> '
node = assignment.Value(routing.NextVar(node))
route += '0'
print(route)
else:
print('No solution found.')
else:
print('Specify an instance greater than 0.')
if __name__ == '__main__':
main(parser.parse_args())

7
examples/python/steel_mill_slab_sat.py
View File

@@ -507,6 +507,13 @@ def collect_valid_slabs(capacities, colors, widths, loss_array, all_colors):
print('Collect Valid Slabs...DONE')
return collector.all_solutions()
num_orders = len(orders)
num_capacities = len(capacities)
all_slabs = range(num_slabs)
all_colors = range(num_colors)
all_orders = range(len(orders))
print('Solving steel mill with %i orders, %i slabs, and %i capacities' %
(num_orders, num_slabs, num_capacities - 1))
def steel_mill_slab_with_valid_slabs(problem, break_symmetries, output_proto):
"""Solves the Steel Mill Slab Problem."""

1
examples/python/task_allocation_sat.py
View File

@@ -10,6 +10,7 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""CP-SAT model for task allocation problem.
see
http://yetanothermathprogrammingconsultant.blogspot.com/2018/09/minizinc-cpsat-vs-mip.html

1
examples/python/transit_time.py
View File

@@ -14,6 +14,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
"""Display Transit Time
Distances are in meters and time in minutes.
Manhattan average block: 750ft x 264ft -> 228m x 80m

152
examples/python/tsp.py
View File

@@ -24,7 +24,10 @@
"""
from __future__ import print_function
from functools import partial
from six.moves import xrange
from ortools.constraint_solver import pywrapcp
from ortools.constraint_solver import routing_enums_pb2
@@ -33,101 +36,108 @@ from ortools.constraint_solver import routing_enums_pb2
# Problem Data Definition #
###########################
def create_data_model():
"""Stores the data for the problem"""
data = {}
# Locations in block unit
_locations = \
[(4, 4), # depot
(2, 0), (8, 0), # locations to visit
(0, 1), (1, 1),
(5, 2), (7, 2),
(3, 3), (6, 3),
(5, 5), (8, 5),
(1, 6), (2, 6),
(3, 7), (6, 7),
(0, 8), (7, 8)]
# Compute locations in meters using the block dimension defined as follow
# Manhattan average block: 750ft x 264ft -> 228m x 80m
# here we use: 114m x 80m city block
# src: https://nyti.ms/2GDoRIe "NY Times: Know Your distance"
data["locations"] = [(l[0] * 114, l[1] * 80) for l in _locations]
data["num_locations"] = len(data["locations"])
data["num_vehicles"] = 1
data["depot"] = 0
return data
"""Stores the data for the problem"""
data = {}
# Locations in block unit
_locations = \
[(4, 4), # depot
(2, 0), (8, 0), # locations to visit
(0, 1), (1, 1),
(5, 2), (7, 2),
(3, 3), (6, 3),
(5, 5), (8, 5),
(1, 6), (2, 6),
(3, 7), (6, 7),
(0, 8), (7, 8)]
# Compute locations in meters using the block dimension defined as follow
# Manhattan average block: 750ft x 264ft -> 228m x 80m
# here we use: 114m x 80m city block
# src: https://nyti.ms/2GDoRIe 'NY Times: Know Your distance'
data['locations'] = [(l[0] * 114, l[1] * 80) for l in _locations]
data['num_locations'] = len(data['locations'])
data['num_vehicles'] = 1
data['depot'] = 0
return data
#######################
# Problem Constraints #
#######################
def manhattan_distance(position_1, position_2):
"""Computes the Manhattan distance between two points"""
return (abs(position_1[0] - position_2[0]) +
abs(position_1[1] - position_2[1]))
"""Computes the Manhattan distance between two points"""
return (
abs(position_1[0] - position_2[0]) + abs(position_1[1] - position_2[1]))
def create_distance_evaluator(data):
"""Creates callback to return distance between points."""
_distances = {}
# precompute distance between location to have distance callback in O(1)
for from_node in xrange(data["num_locations"]):
_distances[from_node] = {}
for to_node in xrange(data["num_locations"]):
if from_node == to_node:
_distances[from_node][to_node] = 0
else:
_distances[from_node][to_node] = (manhattan_distance(
data["locations"][from_node], data["locations"][to_node]))
"""Creates callback to return distance between points."""
_distances = {}
# precompute distance between location to have distance callback in O(1)
for from_node in xrange(data['num_locations']):
_distances[from_node] = {}
for to_node in xrange(data['num_locations']):
if from_node == to_node:
_distances[from_node][to_node] = 0
else:
_distances[from_node][to_node] = (
manhattan_distance(data['locations'][from_node],
data['locations'][to_node]))
def distance_evaluator(from_node, to_node):
"""Returns the manhattan distance between the two nodes"""
return _distances[from_node][to_node]
def distance_evaluator(manager, from_node, to_node):
"""Returns the manhattan distance between the two nodes"""
return _distances[manager.IndexToNode(from_node)][manager.IndexToNode(
to_node)]
return distance_evaluator
return distance_evaluator
###########
# Printer #
###########
def print_solution(routing, assignment):
"""Prints assignment on console"""
print('Objective: {}'.format(assignment.ObjectiveValue()))
index = routing.Start(0)
plan_output = 'Route:\n'
distance = 0
while not routing.IsEnd(index):
plan_output += ' {} ->'.format(routing.IndexToNode(index))
previous_index = index
index = assignment.Value(routing.NextVar(index))
distance += routing.GetArcCostForVehicle(previous_index, index, 0)
plan_output += ' {}\n'.format(routing.IndexToNode(index))
plan_output += 'Distance of the route: {}m\n'.format(distance)
print(plan_output)
def print_solution(routing, manager, assignment):
"""Prints assignment on console"""
print('Objective: {}'.format(assignment.ObjectiveValue()))
index = routing.Start(0)
plan_output = 'Route:\n'
distance = 0
while not routing.IsEnd(index):
plan_output += ' {} ->'.format(manager.IndexToNode(index))
previous_index = index
index = assignment.Value(routing.NextVar(index))
distance += routing.GetArcCostForVehicle(previous_index, index, 0)
plan_output += ' {}\n'.format(manager.IndexToNode(index))
plan_output += 'Distance of the route: {}m\n'.format(distance)
print(plan_output)
########
# Main #
########
def main():
"""Entry point of the program"""
# Instantiate the data problem.
data = create_data_model()
"""Entry point of the program"""
# Instantiate the data problem.
data = create_data_model()
# Create Routing Model
routing = pywrapcp.RoutingModel(data["num_locations"],
data["num_vehicles"], data["depot"])
# Define weight of each edge
distance_evaluator = create_distance_evaluator(data)
routing.SetArcCostEvaluatorOfAllVehicles(distance_evaluator)
# Create the routing index manager
manager = pywrapcp.RoutingIndexManager(data['num_locations'],
data['num_vehicles'], data['depot'])
# Setting first solution heuristic (cheapest addition).
search_parameters = pywrapcp.RoutingModel.DefaultSearchParameters()
search_parameters.first_solution_strategy = (
routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC)
# Solve the problem.
assignment = routing.SolveWithParameters(search_parameters)
print_solution(routing, assignment)
# Create Routing Model
routing = pywrapcp.RoutingModel(manager)
# Define weight of each edge
distance_evaluator_index = routing.RegisterTransitCallback(
partial(create_distance_evaluator(data), manager))
routing.SetArcCostEvaluatorOfAllVehicles(distance_evaluator_index)
# Setting first solution heuristic (cheapest addition).
search_parameters = pywrapcp.DefaultRoutingSearchParameters()
search_parameters.first_solution_strategy = (
routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC) # pylint: disable=no-member
# Solve the problem.
assignment = routing.SolveWithParameters(search_parameters)
print_solution(routing, manager, assignment)
if __name__ == '__main__':
main()
main()

162
examples/python/vrp.py
View File

@@ -24,7 +24,10 @@
"""
from __future__ import print_function
from functools import partial
from six.moves import xrange
from ortools.constraint_solver import pywrapcp
from ortools.constraint_solver import routing_enums_pb2
@@ -33,106 +36,113 @@ from ortools.constraint_solver import routing_enums_pb2
# Problem Data Definition #
###########################
def create_data_model():
"""Stores the data for the problem"""
data = {}
# Locations in block unit
_locations = \
[(4, 4), # depot
(2, 0), (8, 0), # locations to visit
(0, 1), (1, 1),
(5, 2), (7, 2),
(3, 3), (6, 3),
(5, 5), (8, 5),
(1, 6), (2, 6),
(3, 7), (6, 7),
(0, 8), (7, 8)]
# Compute locations in meters using the block dimension defined as follow
# Manhattan average block: 750ft x 264ft -> 228m x 80m
# here we use: 114m x 80m city block
# src: https://nyti.ms/2GDoRIe "NY Times: Know Your distance"
data["locations"] = [(l[0] * 114, l[1] * 80) for l in _locations]
data["num_locations"] = len(data["locations"])
data["num_vehicles"] = 4
data["depot"] = 0
return data
"""Stores the data for the problem"""
data = {}
# Locations in block unit
_locations = \
[(4, 4), # depot
(2, 0), (8, 0), # locations to visit
(0, 1), (1, 1),
(5, 2), (7, 2),
(3, 3), (6, 3),
(5, 5), (8, 5),
(1, 6), (2, 6),
(3, 7), (6, 7),
(0, 8), (7, 8)]
# Compute locations in meters using the block dimension defined as follow
# Manhattan average block: 750ft x 264ft -> 228m x 80m
# here we use: 114m x 80m city block
# src: https://nyti.ms/2GDoRIe 'NY Times: Know Your distance'
data['locations'] = [(l[0] * 114, l[1] * 80) for l in _locations]
data['num_locations'] = len(data['locations'])
data['num_vehicles'] = 4
data['depot'] = 0
return data
#######################
# Problem Constraints #
#######################
def manhattan_distance(position_1, position_2):
"""Computes the Manhattan distance between two points"""
return (abs(position_1[0] - position_2[0]) +
abs(position_1[1] - position_2[1]))
"""Computes the Manhattan distance between two points"""
return (
abs(position_1[0] - position_2[0]) + abs(position_1[1] - position_2[1]))
def create_distance_evaluator(data):
"""Creates callback to return distance between points."""
_distances = {}
# precompute distance between location to have distance callback in O(1)
for from_node in xrange(data["num_locations"]):
_distances[from_node] = {}
for to_node in xrange(data["num_locations"]):
if from_node == to_node:
_distances[from_node][to_node] = 0
else:
_distances[from_node][to_node] = (manhattan_distance(
data["locations"][from_node], data["locations"][to_node]))
"""Creates callback to return distance between points."""
_distances = {}
# precompute distance between location to have distance callback in O(1)
for from_node in xrange(data['num_locations']):
_distances[from_node] = {}
for to_node in xrange(data['num_locations']):
if from_node == to_node:
_distances[from_node][to_node] = 0
else:
_distances[from_node][to_node] = (
manhattan_distance(data['locations'][from_node],
data['locations'][to_node]))
def distance_evaluator(from_node, to_node):
"""Returns the manhattan distance between the two nodes"""
return _distances[from_node][to_node]
def distance_evaluator(manager, from_node, to_node):
"""Returns the manhattan distance between the two nodes"""
return _distances[manager.IndexToNode(from_node)][manager.IndexToNode(
to_node)]
return distance_evaluator
return distance_evaluator
###########
# Printer #
###########
def print_solution(data, routing, assignment):
"""Prints assignment on console"""
print('Objective: {}'.format(assignment.ObjectiveValue()))
total_distance = 0
for vehicle_id in xrange(data["num_vehicles"]):
index = routing.Start(vehicle_id)
plan_output = 'Route for vehicle {}:\n'.format(vehicle_id)
distance = 0
while not routing.IsEnd(index):
plan_output += ' {} ->'.format(routing.IndexToNode(index))
previous_index = index
index = assignment.Value(routing.NextVar(index))
distance += routing.GetArcCostForVehicle(previous_index, index,
vehicle_id)
plan_output += ' {}\n'.format(routing.IndexToNode(index))
plan_output += 'Distance of the route: {}m\n'.format(distance)
print(plan_output)
total_distance += distance
print('Total Distance of all routes: {}m'.format(total_distance))
def print_solution(data, routing, manager, assignment): # pylint:disable=too-many-locals
"""Prints assignment on console"""
print('Objective: {}'.format(assignment.ObjectiveValue()))
total_distance = 0
for vehicle_id in xrange(data['num_vehicles']):
index = routing.Start(vehicle_id)
plan_output = 'Route for vehicle {}:\n'.format(vehicle_id)
distance = 0
while not routing.IsEnd(index):
plan_output += ' {} ->'.format(manager.IndexToNode(index))
previous_index = index
index = assignment.Value(routing.NextVar(index))
distance += routing.GetArcCostForVehicle(previous_index, index,
vehicle_id)
plan_output += ' {}\n'.format(manager.IndexToNode(index))
plan_output += 'Distance of the route: {}m\n'.format(distance)
print(plan_output)
total_distance += distance
print('Total Distance of all routes: {}m'.format(total_distance))
########
# Main #
########
def main():
"""Entry point of the program"""
# Instantiate the data problem.
data = create_data_model()
"""Entry point of the program"""
# Instantiate the data problem.
data = create_data_model()
# Create Routing Model
routing = pywrapcp.RoutingModel(data["num_locations"],
data["num_vehicles"], data["depot"])
# Define weight of each edge
distance_evaluator = create_distance_evaluator(data)
routing.SetArcCostEvaluatorOfAllVehicles(distance_evaluator)
# Create the routing index manager
manager = pywrapcp.RoutingIndexManager(data['num_locations'],
data['num_vehicles'], data['depot'])
# Setting first solution heuristic (cheapest addition).
search_parameters = pywrapcp.RoutingModel.DefaultSearchParameters()
search_parameters.first_solution_strategy = (
routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC) # pylint: disable=no-member
# Solve the problem.
assignment = routing.SolveWithParameters(search_parameters)
print_solution(data, routing, assignment)
# Create Routing Model
routing = pywrapcp.RoutingModel(manager)
# Define weight of each edge
distance_evaluator_index = routing.RegisterTransitCallback(
partial(create_distance_evaluator(data), manager))
routing.SetArcCostEvaluatorOfAllVehicles(distance_evaluator_index)
# Setting first solution heuristic (cheapest addition).
search_parameters = pywrapcp.DefaultRoutingSearchParameters()
search_parameters.first_solution_strategy = (
routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC) # pylint: disable=no-member
# Solve the problem.
assignment = routing.SolveWithParameters(search_parameters)
print_solution(data, routing, manager, assignment)
if __name__ == '__main__':
main()
main()

190
examples/python/vrpgs.py
View File

@@ -24,7 +24,10 @@
"""
from __future__ import print_function
from functools import partial
from six.moves import xrange
from ortools.constraint_solver import pywrapcp
from ortools.constraint_solver import routing_enums_pb2
@@ -33,122 +36,129 @@ from ortools.constraint_solver import routing_enums_pb2
# Problem Data Definition #
###########################
def create_data_model():
"""Stores the data for the problem"""
data = {}
# Locations in block unit
_locations = \
[(4, 4), # depot
(2, 0), (8, 0), # locations to visit
(0, 1), (1, 1),
(5, 2), (7, 2),
(3, 3), (6, 3),
(5, 5), (8, 5),
(1, 6), (2, 6),
(3, 7), (6, 7),
(0, 8), (7, 8)]
# Compute locations in meters using the block dimension defined as follow
# Manhattan average block: 750ft x 264ft -> 228m x 80m
# here we use: 114m x 80m city block
# src: https://nyti.ms/2GDoRIe "NY Times: Know Your distance"
data["locations"] = [(l[0] * 114, l[1] * 80) for l in _locations]
data["num_locations"] = len(data["locations"])
data["num_vehicles"] = 4
data["depot"] = 0
return data
"""Stores the data for the problem"""
data = {}
# Locations in block unit
_locations = \
[(4, 4), # depot
(2, 0), (8, 0), # locations to visit
(0, 1), (1, 1),
(5, 2), (7, 2),
(3, 3), (6, 3),
(5, 5), (8, 5),
(1, 6), (2, 6),
(3, 7), (6, 7),
(0, 8), (7, 8)]
# Compute locations in meters using the block dimension defined as follow
# Manhattan average block: 750ft x 264ft -> 228m x 80m
# here we use: 114m x 80m city block
# src: https://nyti.ms/2GDoRIe 'NY Times: Know Your distance'
data['locations'] = [(l[0] * 114, l[1] * 80) for l in _locations]
data['num_locations'] = len(data['locations'])
data['num_vehicles'] = 4
data['depot'] = 0
return data
#######################
# Problem Constraints #
#######################
def manhattan_distance(position_1, position_2):
"""Computes the Manhattan distance between two points"""
return (abs(position_1[0] - position_2[0]) +
abs(position_1[1] - position_2[1]))
"""Computes the Manhattan distance between two points"""
return (
abs(position_1[0] - position_2[0]) + abs(position_1[1] - position_2[1]))
def create_distance_evaluator(data):
"""Creates callback to return distance between points."""
_distances = {}
# precompute distance between location to have distance callback in O(1)
for from_node in xrange(data["num_locations"]):
_distances[from_node] = {}
for to_node in xrange(data["num_locations"]):
if from_node == to_node:
_distances[from_node][to_node] = 0
else:
_distances[from_node][to_node] = (manhattan_distance(
data["locations"][from_node], data["locations"][to_node]))
"""Creates callback to return distance between points."""
_distances = {}
# precompute distance between location to have distance callback in O(1)
for from_node in xrange(data['num_locations']):
_distances[from_node] = {}
for to_node in xrange(data['num_locations']):
if from_node == to_node:
_distances[from_node][to_node] = 0
else:
_distances[from_node][to_node] = (
manhattan_distance(data['locations'][from_node],
data['locations'][to_node]))
def distance_evaluator(from_node, to_node):
"""Returns the manhattan distance between the two nodes"""
return _distances[from_node][to_node]
def distance_evaluator(manager, from_node, to_node):
"""Returns the manhattan distance between the two nodes"""
return _distances[manager.IndexToNode(from_node)][manager.IndexToNode(
to_node)]
return distance_evaluator
return distance_evaluator
def add_distance_dimension(routing, distance_evaluator):
"""Add Global Span constraint"""
distance = 'Distance'
routing.AddDimension(
distance_evaluator,
0, # null slack
3000, # maximum distance per vehicle
True, # start cumul to zero
distance)
distance_dimension = routing.GetDimensionOrDie(distance)
# Try to minimize the max distance among vehicles.
# /!\ It doesn't mean the standard deviation is minimized
distance_dimension.SetGlobalSpanCostCoefficient(100)
def add_distance_dimension(routing, distance_evaluator_index):
"""Add Global Span constraint"""
distance = 'Distance'
routing.AddDimension(
distance_evaluator_index,
0, # null slack
3000, # maximum distance per vehicle
True, # start cumul to zero
distance)
distance_dimension = routing.GetDimensionOrDie(distance)
# Try to minimize the max distance among vehicles.
# /!\ It doesn't mean the standard deviation is minimized
distance_dimension.SetGlobalSpanCostCoefficient(100)
###########
# Printer #
###########
def print_solution(data, routing, assignment):
"""Prints assignment on console"""
print('Objective: {}'.format(assignment.ObjectiveValue()))
total_distance = 0
for vehicle_id in xrange(data["num_vehicles"]):
index = routing.Start(vehicle_id)
plan_output = 'Route for vehicle {}:\n'.format(vehicle_id)
distance = 0
while not routing.IsEnd(index):
plan_output += ' {} ->'.format(routing.IndexToNode(index))
previous_index = index
index = assignment.Value(routing.NextVar(index))
distance += routing.GetArcCostForVehicle(previous_index, index,
vehicle_id)
plan_output += ' {}\n'.format(routing.IndexToNode(index))
plan_output += 'Distance of the route: {}m\n'.format(distance)
print(plan_output)
total_distance += distance
print('Total Distance of all routes: {}m'.format(total_distance))
def print_solution(data, routing, manager, assignment): # pylint:disable=too-many-locals
"""Prints assignment on console"""
print('Objective: {}'.format(assignment.ObjectiveValue()))
total_distance = 0
for vehicle_id in xrange(data['num_vehicles']):
index = routing.Start(vehicle_id)
plan_output = 'Route for vehicle {}:\n'.format(vehicle_id)
distance = 0
while not routing.IsEnd(index):
plan_output += ' {} ->'.format(manager.IndexToNode(index))
previous_index = index
index = assignment.Value(routing.NextVar(index))
distance += routing.GetArcCostForVehicle(previous_index, index,
vehicle_id)
plan_output += ' {}\n'.format(manager.IndexToNode(index))
plan_output += 'Distance of the route: {}m\n'.format(distance)
print(plan_output)
total_distance += distance
print('Total Distance of all routes: {}m'.format(total_distance))
########
# Main #
########
def main():
"""Entry point of the program"""
# Instantiate the data problem.
data = create_data_model()
"""Entry point of the program"""
# Instantiate the data problem.
data = create_data_model()
# Create Routing Model
routing = pywrapcp.RoutingModel(data["num_locations"],
data["num_vehicles"], data["depot"])
# Define weight of each edge
distance_evaluator = create_distance_evaluator(data)
routing.SetArcCostEvaluatorOfAllVehicles(distance_evaluator)
add_distance_dimension(routing, distance_evaluator)
# Create the routing index manager
manager = pywrapcp.RoutingIndexManager(data['num_locations'],
data['num_vehicles'], data['depot'])
# Setting first solution heuristic (cheapest addition).
search_parameters = pywrapcp.RoutingModel.DefaultSearchParameters()
search_parameters.first_solution_strategy = (
routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC) # pylint: disable=no-member
# Solve the problem.
assignment = routing.SolveWithParameters(search_parameters)
print_solution(data, routing, assignment)
# Create Routing Model
routing = pywrapcp.RoutingModel(manager)
# Define weight of each edge
distance_evaluator_index = routing.RegisterTransitCallback(
partial(create_distance_evaluator(data), manager))
routing.SetArcCostEvaluatorOfAllVehicles(distance_evaluator_index)
add_distance_dimension(routing, distance_evaluator_index)
# Setting first solution heuristic (cheapest addition).
search_parameters = pywrapcp.DefaultRoutingSearchParameters()
search_parameters.first_solution_strategy = (
routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC) # pylint: disable=no-member
# Solve the problem.
assignment = routing.SolveWithParameters(search_parameters)
print_solution(data, routing, manager, assignment)
if __name__ == '__main__':
main()
main()