a7f49a2585
* rename swig files .i in .swig * update constraint_solver and routing * backport math_opt changes * move dynamic loading to ortools/third_party_solvers
120 lines
3.5 KiB
Python
120 lines
3.5 KiB
Python
#!/usr/bin/env python3
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# Copyright 2010-2025 Google LLC
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# [START program]
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"""MIP example that solves an assignment problem."""
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# [START import]
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from ortools.linear_solver import pywraplp
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# [END import]
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def main():
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# Data
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# [START data]
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costs = [
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[90, 76, 75, 70],
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[35, 85, 55, 65],
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[125, 95, 90, 105],
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[45, 110, 95, 115],
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[60, 105, 80, 75],
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[45, 65, 110, 95],
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]
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num_workers = len(costs)
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num_tasks = len(costs[0])
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team1 = [0, 2, 4]
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team2 = [1, 3, 5]
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# Maximum total of tasks for any team
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team_max = 2
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# [END data]
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# Solver
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# [START solver]
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# Create the mip solver with the SCIP backend.
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solver = pywraplp.Solver.CreateSolver("SCIP")
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if not solver:
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return
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# [END solver]
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# Variables
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# [START variables]
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# x[i, j] is an array of 0-1 variables, which will be 1
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# if worker i is assigned to task j.
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x = {}
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for worker in range(num_workers):
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for task in range(num_tasks):
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x[worker, task] = solver.BoolVar(f"x[{worker},{task}]")
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# [END variables]
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# Constraints
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# [START constraints]
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# Each worker is assigned at most 1 task.
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for worker in range(num_workers):
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solver.Add(solver.Sum([x[worker, task] for task in range(num_tasks)]) <= 1)
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# Each task is assigned to exactly one worker.
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for task in range(num_tasks):
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solver.Add(solver.Sum([x[worker, task] for worker in range(num_workers)]) == 1)
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# Each team takes at most two tasks.
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team1_tasks = []
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for worker in team1:
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for task in range(num_tasks):
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team1_tasks.append(x[worker, task])
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solver.Add(solver.Sum(team1_tasks) <= team_max)
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team2_tasks = []
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for worker in team2:
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for task in range(num_tasks):
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team2_tasks.append(x[worker, task])
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solver.Add(solver.Sum(team2_tasks) <= team_max)
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# [END constraints]
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# Objective
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# [START objective]
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objective_terms = []
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for worker in range(num_workers):
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for task in range(num_tasks):
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objective_terms.append(costs[worker][task] * x[worker, task])
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solver.Minimize(solver.Sum(objective_terms))
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# [END objective]
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# Solve
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# [START solve]
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print(f"Solving with {solver.SolverVersion()}")
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status = solver.Solve()
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# [END solve]
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# Print solution.
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# [START print_solution]
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if status == pywraplp.Solver.OPTIMAL or status == pywraplp.Solver.FEASIBLE:
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print(f"Total cost = {solver.Objective().Value()}\n")
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for worker in range(num_workers):
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for task in range(num_tasks):
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if x[worker, task].solution_value() > 0.5:
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print(
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f"Worker {worker} assigned to task {task}."
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+ f" Cost = {costs[worker][task]}"
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)
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else:
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print("No solution found.")
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print(f"Time = {solver.WallTime()} ms")
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# [END print_solution]
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if __name__ == "__main__":
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main()
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# [END program]
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