126 lines
4.2 KiB
Java
126 lines
4.2 KiB
Java
// 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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// Solve a multiple knapsack problem using a MIP solver.
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package com.google.ortools.linearsolver.samples;
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// [START import]
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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;
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import com.google.ortools.linearsolver.MPVariable;
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import java.util.stream.IntStream;
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// [END import]
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/** Multiple knapsack problem. */
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public class MultipleKnapsackMip {
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public static void main(String[] args) {
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Loader.loadNativeLibraries();
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// Instantiate the data problem.
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// [START data]
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final double[] weights = {48, 30, 42, 36, 36, 48, 42, 42, 36, 24, 30, 30, 42, 36, 36};
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final double[] values = {10, 30, 25, 50, 35, 30, 15, 40, 30, 35, 45, 10, 20, 30, 25};
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final int numItems = weights.length;
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final int[] allItems = IntStream.range(0, numItems).toArray();
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final double[] binCapacities = {100, 100, 100, 100, 100};
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final int numBins = binCapacities.length;
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final int[] allBins = IntStream.range(0, numBins).toArray();
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// [END data]
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// [START solver]
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// Create the linear solver with the SCIP backend.
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MPSolver solver = MPSolver.createSolver("SCIP");
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if (solver == null) {
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System.out.println("Could not create solver SCIP");
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return;
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}
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// [END solver]
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// Variables.
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// [START variables]
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MPVariable[][] x = new MPVariable[numItems][numBins];
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for (int i : allItems) {
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for (int b : allBins) {
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x[i][b] = solver.makeBoolVar("x_" + i + "_" + b);
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}
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}
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// [END variables]
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// Constraints.
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// [START constraints]
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// Each item is assigned to at most one bin.
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for (int i : allItems) {
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MPConstraint constraint = solver.makeConstraint(0, 1, "");
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for (int b : allBins) {
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constraint.setCoefficient(x[i][b], 1);
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}
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}
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// The amount packed in each bin cannot exceed its capacity.
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for (int b : allBins) {
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MPConstraint constraint = solver.makeConstraint(0, binCapacities[b], "");
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for (int i : allItems) {
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constraint.setCoefficient(x[i][b], weights[i]);
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}
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}
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// [END constraints]
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// Objective.
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// [START objective]
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// Maximize total value of packed items.
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MPObjective objective = solver.objective();
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for (int i : allItems) {
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for (int b : allBins) {
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objective.setCoefficient(x[i][b], values[i]);
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}
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}
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objective.setMaximization();
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// [END objective]
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// [START solve]
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final MPSolver.ResultStatus status = solver.solve();
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// [END solve]
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// [START print_solution]
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// Check that the problem has an optimal solution.
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if (status == MPSolver.ResultStatus.OPTIMAL) {
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System.out.println("Total packed value: " + objective.value());
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double totalWeight = 0;
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for (int b : allBins) {
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double binWeight = 0;
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double binValue = 0;
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System.out.println("Bin " + b);
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for (int i : allItems) {
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if (x[i][b].solutionValue() == 1) {
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System.out.println("Item " + i + " weight: " + weights[i] + " value: " + values[i]);
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binWeight += weights[i];
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binValue += values[i];
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}
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}
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System.out.println("Packed bin weight: " + binWeight);
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System.out.println("Packed bin value: " + binValue);
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totalWeight += binWeight;
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}
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System.out.println("Total packed weight: " + totalWeight);
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} else {
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System.err.println("The problem does not have an optimal solution.");
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}
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// [END print_solution]
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}
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private MultipleKnapsackMip() {}
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}
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// [END program]
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