// Copyright 2010-2025 Google LLC // 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. // [START program] package com.google.ortools.linearsolver.samples; // [START import] import com.google.ortools.Loader; import com.google.ortools.linearsolver.MPConstraint; import com.google.ortools.linearsolver.MPObjective; import com.google.ortools.linearsolver.MPSolver; import com.google.ortools.linearsolver.MPVariable; import java.util.stream.IntStream; // [END import] /** MIP example that solves an assignment problem. */ public class AssignmentTeamsMip { public static void main(String[] args) { Loader.loadNativeLibraries(); // Data // [START data] double[][] costs = { {90, 76, 75, 70}, {35, 85, 55, 65}, {125, 95, 90, 105}, {45, 110, 95, 115}, {60, 105, 80, 75}, {45, 65, 110, 95}, }; int numWorkers = costs.length; int numTasks = costs[0].length; final int[] allWorkers = IntStream.range(0, numWorkers).toArray(); final int[] allTasks = IntStream.range(0, numTasks).toArray(); final int[] team1 = {0, 2, 4}; final int[] team2 = {1, 3, 5}; // Maximum total of tasks for any team final int teamMax = 2; // [END data] // Solver // [START solver] // Create the linear solver with the SCIP backend. MPSolver solver = MPSolver.createSolver("SCIP"); if (solver == null) { System.out.println("Could not create solver SCIP"); return; } // [END solver] // Variables // [START variables] // x[i][j] is an array of 0-1 variables, which will be 1 // if worker i is assigned to task j. MPVariable[][] x = new MPVariable[numWorkers][numTasks]; for (int worker : allWorkers) { for (int task : allTasks) { x[worker][task] = solver.makeBoolVar("x[" + worker + "," + task + "]"); } } // [END variables] // Constraints // [START constraints] // Each worker is assigned to at most one task. for (int worker : allWorkers) { MPConstraint constraint = solver.makeConstraint(0, 1, ""); for (int task : allTasks) { constraint.setCoefficient(x[worker][task], 1); } } // Each task is assigned to exactly one worker. for (int task : allTasks) { MPConstraint constraint = solver.makeConstraint(1, 1, ""); for (int worker : allWorkers) { constraint.setCoefficient(x[worker][task], 1); } } // Each team takes at most two tasks. MPConstraint team1Tasks = solver.makeConstraint(0, teamMax, ""); for (int worker : team1) { for (int task : allTasks) { team1Tasks.setCoefficient(x[worker][task], 1); } } MPConstraint team2Tasks = solver.makeConstraint(0, teamMax, ""); for (int worker : team2) { for (int task : allTasks) { team2Tasks.setCoefficient(x[worker][task], 1); } } // [END constraints] // Objective // [START objective] MPObjective objective = solver.objective(); for (int worker : allWorkers) { for (int task : allTasks) { objective.setCoefficient(x[worker][task], costs[worker][task]); } } objective.setMinimization(); // [END objective] // Solve // [START solve] MPSolver.ResultStatus resultStatus = solver.solve(); // [END solve] // Print solution. // [START print_solution] // Check that the problem has a feasible solution. if (resultStatus == MPSolver.ResultStatus.OPTIMAL || resultStatus == MPSolver.ResultStatus.FEASIBLE) { System.out.println("Total cost: " + objective.value() + "\n"); for (int worker : allWorkers) { for (int task : allTasks) { // Test if x[i][j] is 0 or 1 (with tolerance for floating point // arithmetic). if (x[worker][task].solutionValue() > 0.5) { System.out.println("Worker " + worker + " assigned to task " + task + ". Cost: " + costs[worker][task]); } } } } else { System.err.println("No solution found."); } // [END print_solution] } private AssignmentTeamsMip() {} } // [END program]