a7f49a2585
* rename swig files .i in .swig * update constraint_solver and routing * backport math_opt changes * move dynamic loading to ortools/third_party_solvers
121 lines
3.4 KiB
Python
Executable File
121 lines
3.4 KiB
Python
Executable File
#!/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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"""Solve a simple bin packing problem using a MIP solver."""
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# [START program]
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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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# [START program_part1]
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# [START data_model]
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def create_data_model():
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"""Create the data for the example."""
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data = {}
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weights = [48, 30, 19, 36, 36, 27, 42, 42, 36, 24, 30]
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data["weights"] = weights
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data["items"] = list(range(len(weights)))
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data["bins"] = data["items"]
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data["bin_capacity"] = 100
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return data
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# [END data_model]
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def main():
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# [START data]
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data = create_data_model()
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# [END data]
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# [END program_part1]
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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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# [START program_part2]
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# [START variables]
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# Variables
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# x[i, j] = 1 if item i is packed in bin j.
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x = {}
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for i in data["items"]:
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for j in data["bins"]:
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x[(i, j)] = solver.IntVar(0, 1, "x_%i_%i" % (i, j))
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# y[j] = 1 if bin j is used.
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y = {}
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for j in data["bins"]:
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y[j] = solver.IntVar(0, 1, "y[%i]" % j)
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# [END variables]
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# [START constraints]
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# Constraints
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# Each item must be in exactly one bin.
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for i in data["items"]:
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solver.Add(sum(x[i, j] for j in data["bins"]) == 1)
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# The amount packed in each bin cannot exceed its capacity.
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for j in data["bins"]:
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solver.Add(
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sum(x[(i, j)] * data["weights"][i] for i in data["items"])
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<= y[j] * data["bin_capacity"]
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)
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# [END constraints]
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# [START objective]
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# Objective: minimize the number of bins used.
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solver.Minimize(solver.Sum([y[j] for j in data["bins"]]))
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# [END objective]
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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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# [START print_solution]
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if status == pywraplp.Solver.OPTIMAL:
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num_bins = 0
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for j in data["bins"]:
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if y[j].solution_value() == 1:
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bin_items = []
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bin_weight = 0
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for i in data["items"]:
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if x[i, j].solution_value() > 0:
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bin_items.append(i)
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bin_weight += data["weights"][i]
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if bin_items:
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num_bins += 1
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print("Bin number", j)
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print(" Items packed:", bin_items)
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print(" Total weight:", bin_weight)
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print()
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print()
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print("Number of bins used:", num_bins)
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print("Time = ", solver.WallTime(), " milliseconds")
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else:
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print("The problem does not have an optimal solution.")
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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_part2]
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# [END program]
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