124 lines
4.2 KiB
Java
124 lines
4.2 KiB
Java
// Copyright 2010-2018 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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// MIP example that solves a multiple knapsack problem.
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// [START program]
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package com.google.ortools.linearsolver.samples;
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// [START import]
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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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// [END import]
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/** Multiple knapsack problem. */
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public class MultipleKnapsackMip {
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static {
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System.loadLibrary("jniortools");
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}
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// [START program_part1]
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// [START data_model]
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static class DataModel {
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public final double[] weights = {48, 30, 42, 36, 36, 48, 42, 42, 36, 24, 30, 30, 42, 36, 36};
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public final double[] values = {10, 30, 25, 50, 35, 30, 15, 40, 30, 35, 45, 10, 20, 30, 25};
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public final int numItems = weights.length;
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public final int numBins = 5;
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public final double[] binCapacities = {100, 100, 100, 100, 100};
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}
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// [END data_model]
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public static void main(String[] args) throws Exception {
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// [START data]
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final DataModel data = new DataModel();
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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 linear solver with the CBC backend.
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MPSolver solver = new MPSolver(
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"MultipleKnapsackMip", MPSolver.OptimizationProblemType.CBC_MIXED_INTEGER_PROGRAMMING);
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// [END solver]
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// [START program_part2]
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// [START variables]
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MPVariable[][] x = new MPVariable[data.numItems][data.numBins];
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for (int i = 0; i < data.numItems; ++i) {
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for (int j = 0; j < data.numBins; ++j) {
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x[i][j] = solver.makeIntVar(0, 1, "");
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}
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}
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// [END variables]
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// [START constraints]
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for (int i = 0; i < data.numItems; ++i) {
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MPConstraint constraint = solver.makeConstraint(0, 1, "");
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for (int j = 0; j < data.numBins; ++j) {
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constraint.setCoefficient(x[i][j], 1);
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}
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}
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for (int j = 0; j < data.numBins; ++j) {
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MPConstraint constraint = solver.makeConstraint(0, data.binCapacities[j], "");
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for (int i = 0; i < data.numItems; ++i) {
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constraint.setCoefficient(x[i][j], data.weights[i]);
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}
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}
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// [END constraints]
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// [START objective]
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MPObjective objective = solver.objective();
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for (int i = 0; i < data.numItems; ++i) {
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for (int j = 0; j < data.numBins; ++j) {
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objective.setCoefficient(x[i][j], data.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 resultStatus = 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 (resultStatus == MPSolver.ResultStatus.OPTIMAL) {
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System.out.println("Total packed value: " + objective.value() + "\n");
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double totalWeight = 0;
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for (int j = 0; j < data.numBins; ++j) {
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double binWeight = 0;
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double binValue = 0;
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System.out.println("Bin " + j + "\n");
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for (int i = 0; i < data.numItems; ++i) {
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if (x[i][j].solutionValue() == 1) {
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System.out.println(
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"Item " + i + " - weight: " + data.weights[i] + " value: " + data.values[i]);
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binWeight += data.weights[i];
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binValue += data.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 + "\n");
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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_part2]
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// [END program]
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