// 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.sat.samples;

import com.google.ortools.Loader;
import com.google.ortools.sat.CpModel;
import com.google.ortools.sat.CpSolver;
import com.google.ortools.sat.CpSolverSolutionCallback;
import com.google.ortools.sat.CpSolverStatus;
import com.google.ortools.sat.DecisionStrategyProto;
import com.google.ortools.sat.IntVar;
import com.google.ortools.sat.LinearExpr;
import com.google.ortools.sat.SatParameters;

/** Encode the piecewise linear expression. */
public class EarlinessTardinessCostSampleSat {
  public static void main(String[] args) throws Exception {
    Loader.loadNativeLibraries();
    long earlinessDate = 5;
    long earlinessCost = 8;
    long latenessDate = 15;
    long latenessCost = 12;

    // Create the CP-SAT model.
    CpModel model = new CpModel();

    // Declare our primary variable.
    IntVar x = model.newIntVar(0, 20, "x");

    // Create the expression variable and implement the piecewise linear function.
    //
    //  \        /
    //   \______/
    //   ed    ld
    //
    long largeConstant = 1000;
    IntVar expr = model.newIntVar(0, largeConstant, "expr");

    // Link together expr and the 3 segment.
    // First segment: y == earlinessCost * (earlinessDate - x).
    // Second segment: y = 0
    // Third segment: y == latenessCost * (x - latenessDate).
    model.addMaxEquality(expr,
        new LinearExpr[] {LinearExpr.newBuilder()
                              .addTerm(x, -earlinessCost)
                              .add(earlinessCost * earlinessDate)
                              .build(),
            LinearExpr.constant(0),
            LinearExpr.newBuilder()
                .addTerm(x, latenessCost)
                .add(-latenessCost * latenessDate)
                .build()});

    // Search for x values in increasing order.
    model.addDecisionStrategy(new IntVar[] {x},
        DecisionStrategyProto.VariableSelectionStrategy.CHOOSE_FIRST,
        DecisionStrategyProto.DomainReductionStrategy.SELECT_MIN_VALUE);

    // Create the solver.
    CpSolver solver = new CpSolver();

    // Force the solver to follow the decision strategy exactly.
    solver.getParameters().setSearchBranching(SatParameters.SearchBranching.FIXED_SEARCH);
    // Tell the solver to enumerate all solutions.
    solver.getParameters().setEnumerateAllSolutions(true);

    // Solve the problem with the printer callback.
    CpSolverStatus unusedStatus = solver.solve(model, new CpSolverSolutionCallback() {
      public CpSolverSolutionCallback init(IntVar[] variables) {
        variableArray = variables;
        return this;
      }

      @Override
      public void onSolutionCallback() {
        for (IntVar v : variableArray) {
          System.out.printf("%s=%d ", v.getName(), value(v));
        }
        System.out.println();
      }

      private IntVar[] variableArray;
    }.init(new IntVar[] {x, expr}));
  }
}
// [END program]