1  #region License Information


2  /* HeuristicLab


3  * Copyright (C) 20022010 Heuristic and Evolutionary Algorithms Laboratory (HEAL)


4  *


5  * This file is part of HeuristicLab.


6  *


7  * HeuristicLab is free software: you can redistribute it and/or modify


8  * it under the terms of the GNU General Public License as published by


9  * the Free Software Foundation, either version 3 of the License, or


10  * (at your option) any later version.


11  *


12  * HeuristicLab is distributed in the hope that it will be useful,


13  * but WITHOUT ANY WARRANTY; without even the implied warranty of


14  * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the


15  * GNU General Public License for more details.


16  *


17  * You should have received a copy of the GNU General Public License


18  * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.


19  */


20  #endregion


21 


22  using System;


23  using System.Collections.Generic;


24  using HeuristicLab.Core;


25  using HeuristicLab.Data;


26  using HeuristicLab.Encodings.PermutationEncoding;


27  using HeuristicLab.Optimization;


28  using HeuristicLab.Parameters;


29  using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;


30  using HeuristicLab.Problems.VehicleRouting.Variants;


31  using HeuristicLab.Common;


32  using HeuristicLab.Problems.VehicleRouting.Interfaces;


33 


34  namespace HeuristicLab.Problems.VehicleRouting.Encodings.Alba {


35  [Item("PushForwardCreator", "The push forward insertion heuristic. It is implemented as described in Sam, and Thangiah, R. (1999). A Hybrid Genetic Algorithms, Simulated Annealing and Tabu Search Heuristic for Vehicle Routing Problems with Time Windows. Practical Handbook of Genetic Algorithms, Volume III, pp 347–381.")]


36  [StorableClass]


37  public sealed class PushForwardCreator : DefaultRepresentationCreator, IStochasticOperator, ITimeWindowedOperator {


38  #region IStochasticOperator Members


39  public ILookupParameter<IRandom> RandomParameter {


40  get { return (LookupParameter<IRandom>)Parameters["Random"]; }


41  }


42  #endregion


43 


44  public IValueParameter<DoubleValue> Alpha {


45  get { return (IValueParameter<DoubleValue>)Parameters["Alpha"]; }


46  }


47  public IValueParameter<DoubleValue> AlphaVariance {


48  get { return (IValueParameter<DoubleValue>)Parameters["AlphaVariance"]; }


49  }


50  public IValueParameter<DoubleValue> Beta {


51  get { return (IValueParameter<DoubleValue>)Parameters["Beta"]; }


52  }


53  public IValueParameter<DoubleValue> BetaVariance {


54  get { return (IValueParameter<DoubleValue>)Parameters["BetaVariance"]; }


55  }


56  public IValueParameter<DoubleValue> Gamma {


57  get { return (IValueParameter<DoubleValue>)Parameters["Gamma"]; }


58  }


59  public IValueParameter<DoubleValue> GammaVariance {


60  get { return (IValueParameter<DoubleValue>)Parameters["GammaVariance"]; }


61  }


62 


63  [StorableConstructor]


64  private PushForwardCreator(bool deserializing) : base(deserializing) { }


65 


66  public PushForwardCreator()


67  : base() {


68  Parameters.Add(new LookupParameter<IRandom>("Random", "The pseudo random number generator."));


69  Parameters.Add(new ValueParameter<DoubleValue>("Alpha", "The alpha value.", new DoubleValue(0.7)));


70  Parameters.Add(new ValueParameter<DoubleValue>("AlphaVariance", "The alpha variance.", new DoubleValue(0.5)));


71  Parameters.Add(new ValueParameter<DoubleValue>("Beta", "The beta value.", new DoubleValue(0.1)));


72  Parameters.Add(new ValueParameter<DoubleValue>("BetaVariance", "The beta variance.", new DoubleValue(0.07)));


73  Parameters.Add(new ValueParameter<DoubleValue>("Gamma", "The gamma value.", new DoubleValue(0.2)));


74  Parameters.Add(new ValueParameter<DoubleValue>("GammaVariance", "The gamma variance.", new DoubleValue(0.14)));


75  }


76 


77  public override IDeepCloneable Clone(Cloner cloner) {


78  return new PushForwardCreator(this, cloner);


79  }


80 


81  private PushForwardCreator(PushForwardCreator original, Cloner cloner)


82  : base(original, cloner) {


83  }


84 


85  // use the BoxMueller transform in the polar form to generate a N(0,1) random variable out of two uniformly distributed random variables


86  private static double Gauss(IRandom random) {


87  double u = 0.0, v = 0.0, s = 0.0;


88  do {


89  u = (random.NextDouble() * 2)  1;


90  v = (random.NextDouble() * 2)  1;


91  s = Math.Sqrt(u * u + v * v);


92  } while (s < Double.Epsilon  s > 1);


93  return u * Math.Sqrt((2.0 * Math.Log(s)) / s);


94  }


95 


96  private static double N(double mu, double sigma, IRandom random) {


97  return mu + (sigma * Gauss(random)); // transform the random variable sampled from N(0,1) to N(mu,sigma)


98  }


99 


100  private static double Distance(IVRPProblemInstance problemInstance, int start, int end) {


101  return problemInstance.GetDistance(start, end);


102  }


103 


104  private static double TravelDistance(IVRPProblemInstance problemInstance, List<int> route, int begin) {


105  double distance = 0;


106  for (int i = begin; i < route.Count  1 && (i == begin  route[i] != 0); i++) {


107  distance += Distance(problemInstance, route[i], route[i + 1]);


108  }


109  return distance;


110  }


111 


112  private static bool SubrouteConstraintsOK(IVRPProblemInstance problemInstance, List<int> route, int begin) {


113  AlbaEncoding solution = AlbaEncoding.ConvertFrom(route, problemInstance);


114 


115  return problemInstance.Feasible(solution);


116  }


117 


118  public static List<int> CreateSolution(IVRPProblemInstance problemInstance, IRandom random,


119  double alphaValue = 0.7, double betaValue = 0.1, double gammaValue = 0.2,


120  double alphaVariance = 0.5, double betaVariance = 0.07, double gammaVariance = 0.14) {


121  double alpha, beta, gamma;


122  alpha = N(alphaValue, Math.Sqrt(alphaVariance), random);


123  beta = N(betaValue, Math.Sqrt(betaVariance), random);


124  gamma = N(gammaValue, Math.Sqrt(gammaVariance), random);


125 


126  double x0 = problemInstance.Coordinates[0, 0];


127  double y0 = problemInstance.Coordinates[0, 1];


128  double distance = 0;


129  double cost = 0;


130  double minimumCost = double.MaxValue;


131  List<int> unroutedList = new List<int>();


132  List<double> costList = new List<double>();


133  int index;


134  int indexOfMinimumCost = 1;


135  int indexOfCustomer = 1;


136 


137  /*


138  * generate cost list


139  *


140  */


141  for (int i = 1; i <= problemInstance.Cities.Value; i++) {


142  distance = Distance(problemInstance, i, 0);


143  if (problemInstance.Coordinates[i, 0] < x0) distance = distance;


144 


145  cost = alpha * distance + // distance 0 <> City[i]


146  beta * (problemInstance as ITimeWindowedProblemInstance).DueTime[i] + // latest arrival time


147  gamma * (Math.Asin((problemInstance.Coordinates[i, 1]  y0) / distance) / 360 * distance); // polar angle


148 


149  index = 0;


150  while (index < costList.Count && costList[index] < cost) index++;


151  costList.Insert(index, cost);


152  unroutedList.Insert(index, i);


153  }


154 


155  /*


156  * route customers according to cost list


157  *


158  */


159  int routeIndex = 0;


160  int currentRoute = 0;


161  int c;


162  int customer = 1;


163  int subTourCount = 1;


164  List<int> route = new List<int>(problemInstance.Cities.Value + problemInstance.Vehicles.Value  1);


165  minimumCost = double.MaxValue;


166  indexOfMinimumCost = 1;


167  route.Add(0);


168  route.Add(0);


169  route.Insert(1, unroutedList[0]);


170  unroutedList.RemoveAt(0);


171  currentRoute = routeIndex;


172  routeIndex++;


173 


174  while (unroutedList.Count > 0) {


175  for (c = 0; c < unroutedList.Count; c++) {


176  for (int i = currentRoute + 1; i < route.Count; i++) {


177  route.Insert(i, (int)unroutedList[c]);


178  if (route[currentRoute] != 0) { throw new Exception("currentRoute not depot"); }


179  cost = TravelDistance(problemInstance, route, currentRoute);


180  if (cost < minimumCost && SubrouteConstraintsOK(problemInstance, route, currentRoute)) {


181  minimumCost = cost;


182  indexOfMinimumCost = i;


183  customer = (int)unroutedList[c];


184  indexOfCustomer = c;


185  }


186  route.RemoveAt(i);


187  }


188  }


189  // insert customer if found


190  if (indexOfMinimumCost != 1) {


191  route.Insert(indexOfMinimumCost, customer);


192  routeIndex++;


193  unroutedList.RemoveAt(indexOfCustomer);


194  costList.RemoveAt(indexOfCustomer);


195  } else { // no feasible customer found


196  routeIndex++;


197  route.Insert(routeIndex, 0);


198  currentRoute = routeIndex;


199  route.Insert(route.Count  1, (int)unroutedList[0]);


200  unroutedList.RemoveAt(0);


201  routeIndex++;


202  subTourCount++;


203  }


204  // reset minimum


205  minimumCost = double.MaxValue;


206  indexOfMinimumCost = 1;


207  indexOfCustomer = 1;


208  customer = 1;


209  }


210  while (route.Count < problemInstance.Cities.Value + problemInstance.Vehicles.Value)


211  route.Add(0);


212 


213  return route;


214  }


215 


216  protected override List<int> CreateSolution() {


217  return CreateSolution(ProblemInstance, RandomParameter.ActualValue,


218  Alpha.Value.Value, Beta.Value.Value, Gamma.Value.Value,


219  AlphaVariance.Value.Value, BetaVariance.Value.Value, GammaVariance.Value.Value);


220  }


221  }


222  }

