2025-01-10 11:33:35 +01:00
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// Copyright 2010-2025 Google LLC
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2018-08-02 17:00:44 -07:00
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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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2025-09-15 10:24:48 +02:00
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// [START program]
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2020-05-26 09:30:42 +02:00
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package com.google.ortools.sat.samples;
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2020-09-11 03:07:18 +02:00
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import com.google.ortools.Loader;
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2018-08-03 16:42:45 -07:00
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import com.google.ortools.sat.CpModel;
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import com.google.ortools.sat.CpSolver;
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2018-11-15 11:38:24 -08:00
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import com.google.ortools.sat.CpSolverStatus;
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import com.google.ortools.sat.IntVar;
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import com.google.ortools.sat.IntervalVar;
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2021-09-09 06:41:35 +02:00
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import com.google.ortools.sat.LinearExpr;
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2018-08-02 17:00:44 -07:00
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2018-08-28 11:19:49 +02:00
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/**
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2018-11-15 11:38:24 -08:00
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* We want to schedule 3 tasks on 3 weeks excluding weekends, making the final day as early as
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* possible.
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2018-08-28 11:19:49 +02:00
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*/
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public class NoOverlapSampleSat {
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public static void main(String[] args) throws Exception {
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Loader.loadNativeLibraries();
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CpModel model = new CpModel();
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// Three weeks.
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int horizon = 21;
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// Task 0, duration 2.
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IntVar start0 = model.newIntVar(0, horizon, "start0");
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int duration0 = 2;
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IntervalVar task0 = model.newFixedSizeIntervalVar(start0, duration0, "task0");
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// Task 1, duration 4.
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IntVar start1 = model.newIntVar(0, horizon, "start1");
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int duration1 = 4;
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IntervalVar task1 = model.newFixedSizeIntervalVar(start1, duration1, "task1");
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// Task 2, duration 3.
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IntVar start2 = model.newIntVar(0, horizon, "start2");
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int duration2 = 3;
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IntervalVar task2 = model.newFixedSizeIntervalVar(start2, duration2, "task2");
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// Weekends.
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IntervalVar weekend0 = model.newFixedInterval(5, 2, "weekend0");
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IntervalVar weekend1 = model.newFixedInterval(12, 2, "weekend1");
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IntervalVar weekend2 = model.newFixedInterval(19, 2, "weekend2");
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// No Overlap constraint. This constraint enforces that no two intervals can overlap.
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// In this example, as we use 3 fixed intervals that span over weekends, this constraint makes
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// sure that all tasks are executed on weekdays.
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2018-11-10 23:56:52 +01:00
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model.addNoOverlap(new IntervalVar[] {task0, task1, task2, weekend0, weekend1, weekend2});
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// Makespan objective.
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IntVar obj = model.newIntVar(0, horizon, "makespan");
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model.addMaxEquality(obj,
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new LinearExpr[] {LinearExpr.newBuilder().add(start0).add(duration0).build(),
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LinearExpr.newBuilder().add(start1).add(duration1).build(),
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LinearExpr.newBuilder().add(start2).add(duration2).build()});
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model.minimize(obj);
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// Creates a solver and solves the model.
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CpSolver solver = new CpSolver();
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CpSolverStatus status = solver.solve(model);
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if (status == CpSolverStatus.OPTIMAL) {
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System.out.println("Optimal Schedule Length: " + solver.objectiveValue());
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System.out.println("Task 0 starts at " + solver.value(start0));
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System.out.println("Task 1 starts at " + solver.value(start1));
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System.out.println("Task 2 starts at " + solver.value(start2));
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}
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}
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}
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2025-09-15 10:24:48 +02:00
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// [END program]
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