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ortools-clone/examples/dotnet/vrp.cs
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  > The macro must appear as the very first part of a function
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- Rename enum after windows clash
- Remove non compact table constraints
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work on sat internals; Add C++ CP-SAT CpModelBuilder API; update sample code and recipes to use the new API; sync with internal code

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improve tables

upgrade C++ sat examples to use the new API; work on sat internals

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rewrite jobshop_ft06_distance.py to use the CP-SAT solver

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fix
2018-11-30 14:48:55 +01:00

179 lines
6.2 KiB
C#

// Copyright 2018 Google
// 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.
using System;
using System.Collections.Generic;
using Google.OrTools.ConstraintSolver;
/// <summary>
/// This is a sample using the routing library .Net wrapper to solve a VRP problem.
/// A description of the problem can be found here:
/// http://en.wikipedia.org/wiki/Vehicle_routing_problem.
/// </summary>
public class VRP {
class DataProblem {
private int[,] locations_;
// Constructor:
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_.GetLength(0); i++) {
locations_[i, 0] = locations_[i, 0] * cityBlock[0];
locations_[i, 1] = locations_[i, 1] * cityBlock[1];
}
}
public int GetVehicleNumber() { return 4;}
public ref readonly int[,] GetLocations() { return ref locations_;}
public int GetLocationNumber() { return locations_.GetLength(0);}
public int GetDepot() { return 0;}
};
/// <summary>
/// Manhattan distance implemented as a callback. It uses an array of
/// positions and computes the Manhattan distance between the two
/// positions of two different indices.
/// </summary>
class ManhattanDistance : IntIntToLong {
private int[,] distances_;
private RoutingIndexManager manager_;
public ManhattanDistance(in DataProblem data,
in RoutingIndexManager manager) {
// precompute distance between location to have distance callback in O(1)
distances_ = new int[data.GetLocationNumber(), data.GetLocationNumber()];
manager_ = manager;
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] =
Math.Abs(data.GetLocations()[toNode, 0] -
data.GetLocations()[fromNode, 0]) +
Math.Abs(data.GetLocations()[toNode, 1] -
data.GetLocations()[fromNode, 1]);
}
}
}
/// <summary>
/// Returns the manhattan distance between the two nodes
/// </summary>
public override long Run(int FromIndex, int ToIndex) {
int FromNode = manager_.IndexToNode(FromIndex);
int ToNode = manager_.IndexToNode(ToIndex);
return distances_[FromNode, ToNode];
}
};
/// <summary>
/// Add distance Dimension
/// </summary>
static void AddDistanceDimension(
in DataProblem data,
in RoutingModel routing,
in int distance_index) {
String distance = "Distance";
routing.AddDimension(
distance_index,
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);
}
/// <summary>
/// Print the solution
/// </summary>
static void PrintSolution(
in DataProblem data,
in RoutingModel routing,
in RoutingIndexManager manager,
in Assignment solution) {
Console.WriteLine("Objective: {0}", solution.ObjectiveValue());
// Inspect solution.
for (int i=0; i < data.GetVehicleNumber(); ++i) {
Console.WriteLine("Route for Vehicle " + i + ":");
long distance = 0;
var index = routing.Start(i);
while (routing.IsEnd(index) == false) {
Console.Write("{0} -> ", manager.IndexToNode((int)index));
var previousIndex = index;
index = solution.Value(routing.NextVar(index));
distance += routing.GetArcCostForVehicle(previousIndex, index, i);
}
Console.WriteLine("{0}", manager.IndexToNode((int)index));
Console.WriteLine("Distance of the route: {0}m", distance);
}
}
/// <summary>
/// Solves the current routing problem.
/// </summary>
static void Solve() {
// Instantiate the data problem.
DataProblem data = new DataProblem();
// Create Routing Model
RoutingIndexManager manager = new RoutingIndexManager(
data.GetLocationNumber(),
data.GetVehicleNumber(),
data.GetDepot());
RoutingModel routing = new RoutingModel(manager);
// Define weight of each edge
IntIntToLong distanceEvaluator = new ManhattanDistance(data, manager);
//protect callbacks from the GC
GC.KeepAlive(distanceEvaluator);
int distance_index = routing.RegisterTransitCallback(distanceEvaluator);
routing.SetArcCostEvaluatorOfAllVehicles(distance_index);
AddDistanceDimension(data, routing, distance_index);
// Setting first solution heuristic (cheapest addition).
RoutingSearchParameters searchParameters =
operations_research_constraint_solver.DefaultRoutingSearchParameters();
searchParameters.FirstSolutionStrategy =
FirstSolutionStrategy.Types.Value.PathCheapestArc;
Assignment solution = routing.SolveWithParameters(searchParameters);
PrintSolution(data, routing, manager, solution);
}
public static void Main(String[] args) {
Solve();
}
}