add examples that captures code samples in the documentation
This commit is contained in:
Laurent Perron
220
examples/python/code_samples_sat.py
Normal file
220
examples/python/code_samples_sat.py
Normal file
@@ -0,0 +1,220 @@
|
||||
# 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
|
||||
#
|
||||
# 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.
|
||||
|
||||
from __future__ import print_function
|
||||
|
||||
import collections
|
||||
|
||||
from ortools.sat.python import cp_model
|
||||
|
||||
|
||||
def MinimalCpSat():
|
||||
# Creates the model.
|
||||
model = cp_model.CpModel()
|
||||
# Creates the variables.
|
||||
num_vals = 3
|
||||
x = model.NewIntVar(0, num_vals - 1, "x")
|
||||
y = model.NewIntVar(0, num_vals - 1, "y")
|
||||
z = model.NewIntVar(0, num_vals - 1, "z")
|
||||
# Create the constraints.
|
||||
model.Add(x != y)
|
||||
|
||||
# Create a solver and solve.
|
||||
solver = cp_model.CpSolver()
|
||||
status = solver.Solve(model)
|
||||
|
||||
if status == cp_model.MODEL_SAT:
|
||||
print("x = %i" % solver.Value(x))
|
||||
print("y = %i" % solver.Value(y))
|
||||
print("z = %i" % solver.Value(z))
|
||||
|
||||
|
||||
class VarArraySolutionPrinter(cp_model.CpSolverSolutionCallback):
|
||||
"""Print intermediate solutions."""
|
||||
|
||||
def __init__(self, variables):
|
||||
self.__variables = variables
|
||||
self.__solution_count = 0
|
||||
|
||||
def NewSolution(self):
|
||||
self.__solution_count += 1
|
||||
for v in self.__variables:
|
||||
print('%s=%i' % (v, self.Value(v)), end = ' ')
|
||||
print()
|
||||
|
||||
def SolutionCount(self):
|
||||
return self.__solution_count
|
||||
|
||||
|
||||
|
||||
|
||||
def MinimalCpSatAllSolutions():
|
||||
# Creates the model.
|
||||
model = cp_model.CpModel()
|
||||
# Creates the variables.
|
||||
num_vals = 3
|
||||
x = model.NewIntVar(0, num_vals - 1, "x")
|
||||
y = model.NewIntVar(0, num_vals - 1, "y")
|
||||
z = model.NewIntVar(0, num_vals - 1, "z")
|
||||
# Create the constraints.
|
||||
model.Add(x != y)
|
||||
|
||||
# Create a solver and solve.
|
||||
solver = cp_model.CpSolver()
|
||||
solution_printer = VarArraySolutionPrinter([x, y, z])
|
||||
status = solver.SearchForAllSolutions(model, solution_printer)
|
||||
|
||||
print('Number of solutions found: %i' % solution_printer.SolutionCount())
|
||||
|
||||
|
||||
def SolvingLinearProblem():
|
||||
# Create a model.
|
||||
model = cp_model.CpModel()
|
||||
|
||||
# x and y are integer non-negative variables.
|
||||
x = model.NewIntVar(0, 17, 'x')
|
||||
y = model.NewIntVar(0, 17, 'y')
|
||||
model.Add(2*x + 14*y <= 35)
|
||||
model.Add(2*x <= 7)
|
||||
obj_var = model.NewIntVar(0, 1000, "obj_var")
|
||||
model.Add(obj_var == x + 10*y)
|
||||
objective = model.Maximize(obj_var)
|
||||
|
||||
# Create a solver and solve.
|
||||
solver = cp_model.CpSolver()
|
||||
status = solver.Solve(model)
|
||||
if status == cp_model.OPTIMAL:
|
||||
print("Objective value: %i" % solver.ObjectiveValue())
|
||||
print()
|
||||
print('x= %i' % solver.Value(x))
|
||||
print('y= %i' % solver.Value(y))
|
||||
|
||||
|
||||
def MinimalJobShop():
|
||||
# Create the model.
|
||||
model = cp_model.CpModel()
|
||||
|
||||
machines_count = 3
|
||||
jobs_count = 3
|
||||
all_machines = range(0, machines_count)
|
||||
all_jobs = range(0, jobs_count)
|
||||
# Define data.
|
||||
machines = [[0, 1, 2],
|
||||
[0, 2, 1],
|
||||
[1, 2]]
|
||||
|
||||
processing_times = [[3, 2, 2],
|
||||
[2, 1, 4],
|
||||
[4, 3]]
|
||||
# Computes horizon.
|
||||
horizon = 0
|
||||
for job in all_jobs:
|
||||
horizon += sum(processing_times[job])
|
||||
|
||||
Task = collections.namedtuple('Task', 'start end interval')
|
||||
AssignedTask = collections.namedtuple('AssignedTask', 'start job index')
|
||||
|
||||
# Creates jobs.
|
||||
all_tasks = {}
|
||||
for job in all_jobs:
|
||||
for index in range(0, len(machines[job])):
|
||||
start_var = model.NewIntVar(0, horizon, 'start_%i_%i' % (job, index))
|
||||
duration = processing_times[job][index]
|
||||
end_var = model.NewIntVar(0, horizon, 'end_%i_%i' % (job, index))
|
||||
interval_var = model.NewIntervalVar(start_var, duration, end_var,
|
||||
'interval_%i_%i' % (job, index))
|
||||
all_tasks[(job, index)] = Task(start=start_var,
|
||||
end=end_var,
|
||||
interval=interval_var)
|
||||
|
||||
# Creates sequence variables and add disjunctive constraints.
|
||||
for machine in all_machines:
|
||||
intervals = []
|
||||
for job in all_jobs:
|
||||
for index in range(0, len(machines[job])):
|
||||
if machines[job][index] == machine:
|
||||
intervals.append(all_tasks[(job, index)].interval)
|
||||
model.AddNoOverlap(intervals)
|
||||
|
||||
# Add precedence contraints.
|
||||
for job in all_jobs:
|
||||
for index in range(0, len(machines[job]) - 1):
|
||||
model.Add(all_tasks[(job, index + 1)].start >=
|
||||
all_tasks[(job, index)].end)
|
||||
|
||||
# Makespan objective.
|
||||
obj_var = model.NewIntVar(0, horizon, 'makespan')
|
||||
model.AddMaxEquality(
|
||||
obj_var, [all_tasks[(job, len(machines[job]) - 1)].end
|
||||
for job in all_jobs])
|
||||
model.Minimize(obj_var)
|
||||
|
||||
# Solve model.
|
||||
solver = cp_model.CpSolver()
|
||||
status = solver.Solve(model)
|
||||
|
||||
if status == cp_model.OPTIMAL:
|
||||
# Print out makespan.
|
||||
print('Optimal Schedule Length: %i' % solver.ObjectiveValue())
|
||||
print()
|
||||
|
||||
# Create one list of assigned tasks per machine.
|
||||
assigned_jobs = [[] for _ in range(machines_count)]
|
||||
for job in all_jobs:
|
||||
for index in range(len(machines[job])):
|
||||
machine = machines[job][index]
|
||||
assigned_jobs[machine].append(
|
||||
AssignedTask(start = solver.Value(all_tasks[(job, index)].start),
|
||||
job = job, index = index))
|
||||
|
||||
disp_col_width = 10
|
||||
sol_line = ""
|
||||
sol_line_tasks = ""
|
||||
|
||||
print("Optimal Schedule", "\n")
|
||||
|
||||
for machine in all_machines:
|
||||
# Sort by starting time.
|
||||
assigned_jobs[machine].sort()
|
||||
sol_line += "Machine " + str(machine) + ": "
|
||||
sol_line_tasks += "Machine " + str(machine) + ": "
|
||||
|
||||
for assigned_task in assigned_jobs[machine]:
|
||||
name = 'job_%i_%i' % (assigned_task.job, assigned_task.index)
|
||||
# Add spaces to output to align columns.
|
||||
sol_line_tasks += name + " " * (disp_col_width - len(name))
|
||||
start = assigned_task.start
|
||||
duration = processing_times[assigned_task.job][assigned_task.index]
|
||||
|
||||
sol_tmp = "[%i,%i]" % (start, start + duration)
|
||||
# Add spaces to output to align columns.
|
||||
sol_line += sol_tmp + " " * (disp_col_width - len(sol_tmp))
|
||||
|
||||
sol_line += "\n"
|
||||
sol_line_tasks += "\n"
|
||||
|
||||
print(sol_line_tasks)
|
||||
print("Time Intervals for Tasks\n")
|
||||
print(sol_line)
|
||||
|
||||
|
||||
|
||||
def main():
|
||||
MinimalCpSat()
|
||||
MinimalCpSatAllSolutions()
|
||||
SolvingLinearProblem()
|
||||
MinimalJobShop()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
Reference in New Issue
Block a user