121 lines
3.7 KiB
Java
121 lines
3.7 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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// [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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/** MIP example that solves an assignment problem. */
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public class AssignmentMip {
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static {
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System.loadLibrary("jniortools");
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}
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public static void main(String[] args) {
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// Data
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// [START data_model]
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double[][] costs = {
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{90, 80, 75, 70},
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{35, 85, 55, 65},
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{125, 95, 90, 95},
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{45, 110, 95, 115},
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{50, 100, 90, 100},
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};
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int numWorkers = costs.length;
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int numTasks = costs[0].length;
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// [END data_model]
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// Solver
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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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"AssignmentMip", MPSolver.OptimizationProblemType.CBC_MIXED_INTEGER_PROGRAMMING);
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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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MPVariable[][] x = new MPVariable[numWorkers][numTasks];
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for (int i = 0; i < numWorkers; ++i) {
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for (int j = 0; j < numTasks; ++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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// Constraints
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// [START constraints]
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// Each worker is assigned to at most one task.
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for (int i = 0; i < numWorkers; ++i) {
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MPConstraint constraint = solver.makeConstraint(0, 1, "");
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for (int j = 0; j < numTasks; ++j) {
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constraint.setCoefficient(x[i][j], 1);
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}
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}
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// Each task is assigned to exactly one worker.
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for (int j = 0; j < numTasks; ++j) {
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MPConstraint constraint = solver.makeConstraint(1, 1, "");
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for (int i = 0; i < numWorkers; ++i) {
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constraint.setCoefficient(x[i][j], 1);
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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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MPObjective objective = solver.objective();
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for (int i = 0; i < numWorkers; ++i) {
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for (int j = 0; j < numTasks; ++j) {
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objective.setCoefficient(x[i][j], costs[i][j]);
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}
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}
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objective.setMinimization();
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// [END objective]
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// Solve
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// [START solve]
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MPSolver.ResultStatus resultStatus = 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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// Check that the problem has a feasible solution.
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if (resultStatus == MPSolver.ResultStatus.OPTIMAL
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|| resultStatus == MPSolver.ResultStatus.FEASIBLE) {
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System.out.println("Total cost: " + objective.value() + "\n");
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for (int i = 0; i < numWorkers; ++i) {
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for (int j = 0; j < numTasks; ++j) {
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// Test if x[i][j] is 0 or 1 (with tolerance for floating point
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// arithmetic).
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if (x[i][j].solutionValue() > 0.5) {
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System.out.println(
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"Worker " + i + " assigned to task " + j + ". Cost = " + costs[i][j]);
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}
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}
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}
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} else {
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System.err.println("No solution found.");
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}
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// [END print_solution]
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}
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private AssignmentMip() {}
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}
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// [END program]
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