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ortools-clone/examples/python/assignment_with_constraints_sat.py
2023-11-16 19:46:56 +01:00

135 lines
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Python

#!/usr/bin/env python3
# Copyright 2010-2022 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.
"""solve an assignment problem with combination constraints on workers."""
from typing import Sequence
from absl import app
from ortools.sat.python import cp_model
def solve_assignment():
"""solve the assignment problem."""
# Data.
cost = [
[90, 76, 75, 70, 50, 74],
[35, 85, 55, 65, 48, 101],
[125, 95, 90, 105, 59, 120],
[45, 110, 95, 115, 104, 83],
[60, 105, 80, 75, 59, 62],
[45, 65, 110, 95, 47, 31],
[38, 51, 107, 41, 69, 99],
[47, 85, 57, 71, 92, 77],
[39, 63, 97, 49, 118, 56],
[47, 101, 71, 60, 88, 109],
[17, 39, 103, 64, 61, 92],
[101, 45, 83, 59, 92, 27],
]
group1 = [
[0, 0, 1, 1], # Workers 2, 3
[0, 1, 0, 1], # Workers 1, 3
[0, 1, 1, 0], # Workers 1, 2
[1, 1, 0, 0], # Workers 0, 1
[1, 0, 1, 0],
] # Workers 0, 2
group2 = [
[0, 0, 1, 1], # Workers 6, 7
[0, 1, 0, 1], # Workers 5, 7
[0, 1, 1, 0], # Workers 5, 6
[1, 1, 0, 0], # Workers 4, 5
[1, 0, 0, 1],
] # Workers 4, 7
group3 = [
[0, 0, 1, 1], # Workers 10, 11
[0, 1, 0, 1], # Workers 9, 11
[0, 1, 1, 0], # Workers 9, 10
[1, 0, 1, 0], # Workers 8, 10
[1, 0, 0, 1],
] # Workers 8, 11
sizes = [10, 7, 3, 12, 15, 4, 11, 5]
total_size_max = 15
num_workers = len(cost)
num_tasks = len(cost[1])
all_workers = range(num_workers)
all_tasks = range(num_tasks)
# Model.
model = cp_model.CpModel()
# Variables
selected = [
[model.new_bool_var("x[%i,%i]" % (i, j)) for j in all_tasks]
for i in all_workers
]
works = [model.new_bool_var("works[%i]" % i) for i in all_workers]
# Constraints
# Link selected and workers.
for i in range(num_workers):
model.add_max_equality(works[i], selected[i])
# Each task is assigned to at least one worker.
for j in all_tasks:
model.add(sum(selected[i][j] for i in all_workers) >= 1)
# Total task size for each worker is at most total_size_max
for i in all_workers:
model.add(sum(sizes[j] * selected[i][j] for j in all_tasks) <= total_size_max)
# Group constraints.
model.add_allowed_assignments([works[0], works[1], works[2], works[3]], group1)
model.add_allowed_assignments([works[4], works[5], works[6], works[7]], group2)
model.add_allowed_assignments([works[8], works[9], works[10], works[11]], group3)
# Objective
model.minimize(
sum(selected[i][j] * cost[i][j] for j in all_tasks for i in all_workers)
)
# Solve and output solution.
solver = cp_model.CpSolver()
status = solver.solve(model)
if status == cp_model.OPTIMAL:
print("Total cost = %i" % solver.objective_value)
print()
for i in all_workers:
for j in all_tasks:
if solver.boolean_value(selected[i][j]):
print(
"Worker ", i, " assigned to task ", j, " Cost = ", cost[i][j]
)
print()
print("Statistics")
print(" - conflicts : %i" % solver.num_conflicts)
print(" - branches : %i" % solver.num_branches)
print(" - wall time : %f s" % solver.wall_time)
def main(argv: Sequence[str]) -> None:
if len(argv) > 1:
raise app.UsageError("Too many command-line arguments.")
solve_assignment()
if __name__ == "__main__":
app.run(main)