c34026b101
note: done using ```sh git grep -l "2010-2024 Google" | xargs sed -i 's/2010-2024 Google/2010-2025 Google/' ```
97 lines
3.4 KiB
Java
97 lines
3.4 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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package com.google.ortools.sat.samples;
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import com.google.ortools.Loader;
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import com.google.ortools.sat.CpModel;
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import com.google.ortools.sat.CpSolver;
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import com.google.ortools.sat.CpSolverSolutionCallback;
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import com.google.ortools.sat.CpSolverStatus;
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import com.google.ortools.sat.DecisionStrategyProto;
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import com.google.ortools.sat.IntVar;
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import com.google.ortools.sat.LinearExpr;
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import com.google.ortools.sat.SatParameters;
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/** Encode the piecewise linear expression. */
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public class EarlinessTardinessCostSampleSat {
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public static void main(String[] args) throws Exception {
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Loader.loadNativeLibraries();
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long earlinessDate = 5;
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long earlinessCost = 8;
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long latenessDate = 15;
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long latenessCost = 12;
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// Create the CP-SAT model.
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CpModel model = new CpModel();
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// Declare our primary variable.
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IntVar x = model.newIntVar(0, 20, "x");
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// Create the expression variable and implement the piecewise linear function.
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//
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// \ /
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// \______/
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// ed ld
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//
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long largeConstant = 1000;
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IntVar expr = model.newIntVar(0, largeConstant, "expr");
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// Link together expr and the 3 segment.
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// First segment: y == earlinessCost * (earlinessDate - x).
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// Second segment: y = 0
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// Third segment: y == latenessCost * (x - latenessDate).
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model.addMaxEquality(expr,
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new LinearExpr[] {LinearExpr.newBuilder()
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.addTerm(x, -earlinessCost)
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.add(earlinessCost * earlinessDate)
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.build(),
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LinearExpr.constant(0),
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LinearExpr.newBuilder()
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.addTerm(x, latenessCost)
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.add(-latenessCost * latenessDate)
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.build()});
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// Search for x values in increasing order.
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model.addDecisionStrategy(new IntVar[] {x},
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DecisionStrategyProto.VariableSelectionStrategy.CHOOSE_FIRST,
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DecisionStrategyProto.DomainReductionStrategy.SELECT_MIN_VALUE);
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// Create the solver.
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CpSolver solver = new CpSolver();
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// Force the solver to follow the decision strategy exactly.
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solver.getParameters().setSearchBranching(SatParameters.SearchBranching.FIXED_SEARCH);
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// Tell the solver to enumerate all solutions.
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solver.getParameters().setEnumerateAllSolutions(true);
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// Solve the problem with the printer callback.
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CpSolverStatus unusedStatus = solver.solve(model, new CpSolverSolutionCallback() {
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public CpSolverSolutionCallback init(IntVar[] variables) {
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variableArray = variables;
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return this;
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}
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@Override
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public void onSolutionCallback() {
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for (IntVar v : variableArray) {
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System.out.printf("%s=%d ", v.getName(), value(v));
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}
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System.out.println();
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}
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private IntVar[] variableArray;
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}.init(new IntVar[] {x, expr}));
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}
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}
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