ortools-clone/examples/java/Vrp.java

// Copyright 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 java.io.*;
import static java.lang.Math.abs;

import com.google.ortools.constraintsolver.RoutingModel;
import com.google.ortools.constraintsolver.NodeEvaluator2;
import com.google.ortools.constraintsolver.RoutingDimension;
import com.google.ortools.constraintsolver.RoutingSearchParameters;
import com.google.ortools.constraintsolver.FirstSolutionStrategy;
import com.google.ortools.constraintsolver.Assignment;

class DataProblem {
  private int[][] locations_;

  public DataProblem() {
    locations_ = new int[][] {
      {4, 4},
        {2, 0}, {8, 0},
        {0, 1}, {1, 1},
        {5, 2}, {7, 2},
        {3, 3}, {6, 3},
        {5, 5}, {8, 5},
        {1, 6}, {2, 6},
        {3, 7}, {6, 7},
        {0, 8}, {7, 8}
    };

    // Compute locations in meters using the block dimension defined as follow
    // Manhattan average block: 750ft x 264ft -> 228m x 80m
    // here we use: 114m x 80m city block
    // src: https://nyti.ms/2GDoRIe "NY Times: Know Your distance"
    int[] cityBlock = {228/2, 80};
    for (int i=0; i < locations_.length; i++) {
      locations_[i][0] = locations_[i][0] * cityBlock[0];
      locations_[i][1] = locations_[i][1] * cityBlock[1];
    }
  }

  /// @brief Gets the number of vehicles.
  public int getVehicleNumber() { return 4;}
  /// @brief Gets the locations.
  public int[][] getLocations() { return locations_;}
  /// @brief Gets the number of locations.
  public int getLocationNumber() { return locations_.length;}
  /// @brief Gets the depot NodeIndex.
  public int getDepot() { return 0;}
}

/// @brief Manhattan distance implemented as a callback.
/// @details It uses an array of positions and computes
/// the Manhattan distance between the two positions of
/// two different indices.
class ManhattanDistance extends NodeEvaluator2 {
  private int[][] distances_;

  public ManhattanDistance(DataProblem data) {
    // precompute distance between location to have distance callback in O(1)
    distances_ = new int[data.getLocationNumber()][data.getLocationNumber()];
    for (int fromNode = 0; fromNode < data.getLocationNumber(); ++fromNode) {
      for (int toNode = 0; toNode < data.getLocationNumber(); ++toNode) {
        if (fromNode == toNode)
          distances_[fromNode][toNode] = 0;
        else
          distances_[fromNode][toNode] =
            abs(data.getLocations()[toNode][0] - data.getLocations()[fromNode][0]) +
            abs(data.getLocations()[toNode][1] - data.getLocations()[fromNode][1]);
      }
    }
  }

  @Override
  /// @brief Returns the manhattan distance between the two nodes.
  public long run(int fromNode, int toNode) {
    return distances_[fromNode][toNode];
  }
}

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

  /// @brief Add Global Span constraint.
  static void addDistanceDimension(RoutingModel routing, DataProblem data) {
    String distance = "Distance";
    routing.addDimension(
        new ManhattanDistance(data),
        0,  // null slack
        3000, // maximum distance per vehicle
        true, // start cumul to zero
        distance);
    RoutingDimension distanceDimension = routing.getDimensionOrDie(distance);
    // Try to minimize the max distance among vehicles.
    // /!\ It doesn't mean the standard deviation is minimized
    distanceDimension.setGlobalSpanCostCoefficient(100);
  }

  /// @brief Print the solution
  static void printSolution(
      DataProblem data,
      RoutingModel routing,
      Assignment solution)
  {
    // Solution cost.
    System.out.println("Objective : " + solution.objectiveValue());
    // Inspect solution.
    for (int i=0; i < data.getVehicleNumber(); ++i) {
      System.out.println("Route for Vehicle " + i + ":");
      long distance = 0;
      for (long index = routing.start(i);
          !routing.isEnd(index);)
      {
        System.out.print(routing.indexToNode(index) + " -> ");

        long previousIndex = index;
        index = solution.value(routing.nextVar(index));
        distance += routing.getArcCostForVehicle(previousIndex, index, i);
      }
      System.out.println(routing.indexToNode(routing.end(i)));
      System.out.println("Distance of the route: " + distance + "m");
    }
  }

  /// @brief Solves the current routing problem.
  static void solve() {
    // Instantiate the data problem.
    DataProblem data = new DataProblem();

    // Create Routing Model
    RoutingModel routing = new RoutingModel(
        data.getLocationNumber(),
        data.getVehicleNumber(),
        data.getDepot());

    // Setting the cost function.
    // [todo]: protect callback from the GC
    NodeEvaluator2 distanceEvaluator = new ManhattanDistance(data);
    routing.setArcCostEvaluatorOfAllVehicles(distanceEvaluator);
    addDistanceDimension(routing, data);

    // Setting first solution heuristic (cheapest addition).
    RoutingSearchParameters search_parameters =
      RoutingSearchParameters.newBuilder()
      .mergeFrom(RoutingModel.defaultSearchParameters())
      .setFirstSolutionStrategy(FirstSolutionStrategy.Value.PATH_CHEAPEST_ARC)
      .build();

    Assignment solution = routing.solveWithParameters(search_parameters);
    printSolution(data, routing, solution);
  }

  /// @brief Entry point of the program.
  public static void main(String[] args) throws Exception {
    solve();
  }
}