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Google's Operations Research tools:

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// Copyright 2010-2025 Google LLC
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// 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.
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package com.google.ortools.java;
import com.google.ortools.Loader;
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import com.google.ortools.linearsolver.MPConstraint;
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import com.google.ortools.linearsolver.MPObjective;
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import com.google.ortools.linearsolver.MPSolver;
import com.google.ortools.linearsolver.MPVariable;
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/**
* Integer programming example that shows how to use the API.
*
*/
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public class IntegerProgramming {
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private static void runIntegerProgrammingExample(String solverType) {
MPSolver solver = MPSolver.createSolver(solverType);
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if (solver == null) {
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System.out.println("Could not create solver " + solverType);
return;
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}
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double infinity = Double.POSITIVE_INFINITY;
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// x1 and x2 are integer non-negative variables.
MPVariable x1 = solver.makeIntVar(0.0, infinity, "x1");
MPVariable x2 = solver.makeIntVar(0.0, infinity, "x2");
// Minimize x1 + 2 * x2.
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MPObjective objective = solver.objective();
objective.setCoefficient(x1, 1);
objective.setCoefficient(x2, 2);
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// 2 * x2 + 3 * x1 >= 17.
MPConstraint ct = solver.makeConstraint(17, infinity);
ct.setCoefficient(x1, 3);
ct.setCoefficient(x2, 2);
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final MPSolver.ResultStatus resultStatus = solver.solve();
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// Check that the problem has an optimal solution.
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if (resultStatus != MPSolver.ResultStatus.OPTIMAL) {
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System.err.println("The problem does not have an optimal solution!");
return;
}
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// Verify that the solution satisfies all constraints (when using solvers
// others than GLOP_LINEAR_PROGRAMMING, this is highly recommended!).
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if (!solver.verifySolution(/*tolerance=*/1e-7, /* log_errors= */ true)) {
System.err.println("The solution returned by the solver violated the"
+ " problem constraints by at least 1e-7");
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return;
}
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System.out.println("Problem solved in " + solver.wallTime() + " milliseconds");
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// The objective value of the solution.
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System.out.println("Optimal objective value = " + solver.objective().value());
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// The value of each variable in the solution.
System.out.println("x1 = " + x1.solutionValue());
System.out.println("x2 = " + x2.solutionValue());
System.out.println("Advanced usage:");
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System.out.println("Problem solved in " + solver.nodes() + " branch-and-bound nodes");
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}
public static void main(String[] args) throws Exception {
Loader.loadNativeLibraries();
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System.out.println("---- Integer programming example with SCIP (recommended) ----");
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runIntegerProgrammingExample("SCIP");
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System.out.println("---- Integer programming example with CBC ----");
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runIntegerProgrammingExample("CBC");
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System.out.println("---- Integer programming example with GLPK ----");
runIntegerProgrammingExample("GLPK");
System.out.println("---- Integer programming example with CP-SAT ----");
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runIntegerProgrammingExample("SAT");
System.out.println("---- Integer programming example with XPRESS ----");
runIntegerProgrammingExample("XPRESS");
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}
}