RankingSampleSat.java

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// Copyright 2010-2018 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.

import com.google.ortools.sat.CpModel;
import com.google.ortools.sat.CpSolver;
import com.google.ortools.sat.CpSolverStatus;
import com.google.ortools.sat.IntVar;
import com.google.ortools.sat.IntervalVar;
import com.google.ortools.sat.LinearExpr;
import com.google.ortools.sat.Literal;
import java.util.ArrayList;
import java.util.List;

public class RankingSampleSat {
  static {
    System.loadLibrary("jniortools");
  }

  static void rankTasks(CpModel model, IntVar[] starts, Literal[] presences, IntVar[] ranks) {
    int numTasks = starts.length;

    // Creates precedence variables between pairs of intervals.
    Literal[][] precedences = new Literal[numTasks][numTasks];
    for (int i = 0; i < numTasks; ++i) {
      for (int j = 0; j < numTasks; ++j) {
        if (i == j) {
          precedences[i][i] = presences[i];
        } else {
          IntVar prec = model.newBoolVar(String.format("%d before %d", i, j));
          precedences[i][j] = prec;
          // Ensure that task i precedes task j if prec is true.
          model.addLessOrEqualWithOffset(starts[i], starts[j], 1).onlyEnforceIf(prec);
        }
      }
    }

    // Create optional intervals.
    for (int i = 0; i < numTasks - 1; ++i) {
      for (int j = i + 1; j < numTasks; ++j) {
        List<Literal> list = new ArrayList<>();
        list.add(precedences[i][j]);
        list.add(precedences[j][i]);
        list.add(presences[i].not());
        // Makes sure that if i is not performed, all precedences are false.
        model.addImplication(presences[i].not(), precedences[i][j].not());
        model.addImplication(presences[i].not(), precedences[j][i].not());
        list.add(presences[j].not());
        // Makes sure that if j is not performed, all precedences are false.
        model.addImplication(presences[j].not(), precedences[i][j].not());
        model.addImplication(presences[j].not(), precedences[j][i].not());
        // The following boolOr will enforce that for any two intervals:
        //    i precedes j or j precedes i or at least one interval is not
        //        performed.
        model.addBoolOr(list.toArray(new Literal[0]));
        // For efficiency, we add a redundant constraint declaring that only one of i precedes j and
        // j precedes i are true. This will speed up the solve because the reason of this
        // propagation is shorter that using interval bounds is true.
        model.addImplication(precedences[i][j], precedences[j][i].not());
        model.addImplication(precedences[j][i], precedences[i][j].not());
      }
    }

    // Links precedences and ranks.
    for (int i = 0; i < numTasks; ++i) {
      IntVar[] vars = new IntVar[numTasks + 1];
      int[] coefs = new int[numTasks + 1];
      for (int j = 0; j < numTasks; ++j) {
        vars[j] = (IntVar) precedences[j][i];
        coefs[j] = 1;
      }
      vars[numTasks] = ranks[i];
      coefs[numTasks] = -1;
      // ranks == sum(precedences) - 1;
      model.addEquality(LinearExpr.scalProd(vars, coefs), 1);
    }
  }

  public static void main(String[] args) throws Exception {
    CpModel model = new CpModel();
    int horizon = 100;
    int numTasks = 4;

    IntVar[] starts = new IntVar[numTasks];
    IntVar[] ends = new IntVar[numTasks];
    IntervalVar[] intervals = new IntervalVar[numTasks];
    Literal[] presences = new Literal[numTasks];
    IntVar[] ranks = new IntVar[numTasks];

    IntVar trueVar = model.newConstant(1);

    // Creates intervals, half of them are optional.
    for (int t = 0; t < numTasks; ++t) {
      starts[t] = model.newIntVar(0, horizon, "start_" + t);
      int duration = t + 1;
      ends[t] = model.newIntVar(0, horizon, "end_" + t);
      if (t < numTasks / 2) {
        intervals[t] = model.newIntervalVar(starts[t], duration, ends[t], "interval_" + t);
        presences[t] = trueVar;
      } else {
        presences[t] = model.newBoolVar("presence_" + t);
        intervals[t] = model.newOptionalIntervalVar(
            starts[t], duration, ends[t], presences[t], "o_interval_" + t);
      }

      // The rank will be -1 iff the task is not performed.
      ranks[t] = model.newIntVar(-1, numTasks - 1, "rank_" + t);
    }

    // Adds NoOverlap constraint.
    model.addNoOverlap(intervals);

    // Adds ranking constraint.
    rankTasks(model, starts, presences, ranks);

    // Adds a constraint on ranks (ranks[0] < ranks[1]).
    model.addLessOrEqualWithOffset(ranks[0], ranks[1], 1);

    // Creates makespan variable.
    IntVar makespan = model.newIntVar(0, horizon, "makespan");
    for (int t = 0; t < numTasks; ++t) {
      model.addLessOrEqual(ends[t], makespan).onlyEnforceIf(presences[t]);
    }
    // The objective function is a mix of a fixed gain per task performed, and a fixed cost for each
    // additional day of activity.
    // The solver will balance both cost and gain and minimize makespan * per-day-penalty - number
    // of tasks performed * per-task-gain.
    //
    // On this problem, as the fixed cost is less that the duration of the last interval, the solver
    // will not perform the last interval.
    IntVar[] objectiveVars = new IntVar[numTasks + 1];
    int[] objectiveCoefs = new int[numTasks + 1];
    for (int t = 0; t < numTasks; ++t) {
      objectiveVars[t] = (IntVar) presences[t];
      objectiveCoefs[t] = -7;
    }
    objectiveVars[numTasks] = makespan;
    objectiveCoefs[numTasks] = 2;
    model.minimize(LinearExpr.scalProd(objectiveVars, objectiveCoefs));

    // Creates a solver and solves the model.
    CpSolver solver = new CpSolver();
    CpSolverStatus status = solver.solve(model);

    if (status == CpSolverStatus.OPTIMAL) {
      System.out.println("Optimal cost: " + solver.objectiveValue());
      System.out.println("Makespan: " + solver.value(makespan));
      for (int t = 0; t < numTasks; ++t) {
        if (solver.booleanValue(presences[t])) {
          System.out.printf("Task %d starts at %d with rank %d%n", t, solver.value(starts[t]),
              solver.value(ranks[t]));
        } else {
          System.out.printf(
              "Task %d in not performed and ranked at %d%n", t, solver.value(ranks[t]));
        }
      }
    } else {
      System.out.println("Solver exited with nonoptimal status: " + status);
    }
  }
}