1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2013 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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4 | *
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5 | * This file is part of HeuristicLab.
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6 | *
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7 | * HeuristicLab is free software: you can redistribute it and/or modify
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8 | * it under the terms of the GNU General Public License as published by
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9 | * the Free Software Foundation, either version 3 of the License, or
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10 | * (at your option) any later version.
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11 | *
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12 | * HeuristicLab is distributed in the hope that it will be useful,
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13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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15 | * GNU General Public License for more details.
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16 | *
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17 | * You should have received a copy of the GNU General Public License
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18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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19 | */
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20 | #endregion
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21 |
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22 | using HeuristicLab.Common;
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23 | using HeuristicLab.Core;
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24 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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25 | using HeuristicLab.Problems.VehicleRouting.ProblemInstances;
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26 | using HeuristicLab.Problems.VehicleRouting.Variants;
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27 | using HeuristicLab.Problems.VehicleRouting.Interfaces;
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28 |
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29 | namespace HeuristicLab.Problems.VehicleRouting.Encodings.Potvin {
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30 | [Item("PotvinPairwiseOneLevelExchangeMainpulator", "The 1M operator which manipulates a PDP representation. It has been adapted to pickup and delivery from Potvin, J.-Y. and Bengio, S. (1996). The Vehicle Routing Problem with Time Windows - Part II: Genetic Search. INFORMS Journal of Computing, 8:165–172. It was adapted to the PDP formulation.")]
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31 | [StorableClass]
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32 | public sealed class PotvinPairwiseOneLevelExchangeMainpulator : PotvinManipulator {
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33 | [StorableConstructor]
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34 | private PotvinPairwiseOneLevelExchangeMainpulator(bool deserializing) : base(deserializing) { }
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35 |
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36 | public PotvinPairwiseOneLevelExchangeMainpulator() : base() { }
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37 |
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38 | public override IDeepCloneable Clone(Cloner cloner) {
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39 | return new PotvinPairwiseOneLevelExchangeMainpulator(this, cloner);
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40 | }
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41 |
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42 | private PotvinPairwiseOneLevelExchangeMainpulator(PotvinPairwiseOneLevelExchangeMainpulator original, Cloner cloner)
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43 | : base(original, cloner) {
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44 | }
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45 |
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46 | public static bool PairwiseMove(PotvinEncoding individual, IVRPProblemInstance instance, int city, bool allowInfeasible) {
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47 | bool success;
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48 |
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49 | IPickupAndDeliveryProblemInstance pdp = instance as IPickupAndDeliveryProblemInstance;
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50 |
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51 | if (pdp != null) {
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52 | Tour route1 = individual.Tours.Find(t => t.Stops.Contains(city));
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53 | int i = route1.Stops.IndexOf(city);
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54 |
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55 | int dest = pdp.GetPickupDeliveryLocation(city);
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56 | Tour destRoute = individual.Tours.Find(t => t.Stops.Contains(dest));
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57 | int j = destRoute.Stops.IndexOf(dest);
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58 |
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59 | route1.Stops.Remove(city);
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60 | destRoute.Stops.Remove(dest);
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61 |
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62 | int routeToAvoid = -1;
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63 | if (route1 == destRoute)
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64 | routeToAvoid = individual.Tours.IndexOf(route1);
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65 |
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66 | int source, target;
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67 | if (instance.GetDemand(city) >= 0) {
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68 | source = city;
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69 | target = dest;
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70 | } else {
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71 | source = dest;
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72 | target = city;
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73 | }
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74 |
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75 | double bestQuality = double.MaxValue;
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76 | int bestTour = -1;
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77 | int bestPositionSource = -1;
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78 | int bestPositionTarget = -1;
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79 |
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80 | for (int tourIdx = 0; tourIdx < individual.Tours.Count; tourIdx++) {
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81 | if (tourIdx != routeToAvoid) {
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82 | Tour tour = individual.Tours[tourIdx];
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83 | VRPEvaluation eval = instance.EvaluateTour(tour, individual);
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84 | individual.InsertPair(tour, source, target, instance);
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85 | VRPEvaluation evalNew = instance.EvaluateTour(tour, individual);
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86 |
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87 | double delta = evalNew.Quality - eval.Quality;
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88 |
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89 | if (delta < bestQuality &&
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90 | (instance.Feasible(evalNew) || allowInfeasible)) {
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91 | bestQuality = delta;
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92 | bestTour = tourIdx;
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93 | bestPositionSource = tour.Stops.IndexOf(source);
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94 | bestPositionTarget = tour.Stops.IndexOf(target);
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95 | }
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96 |
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97 | tour.Stops.Remove(source);
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98 | tour.Stops.Remove(target);
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99 | }
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100 | }
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101 |
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102 | if (bestTour >= 0) {
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103 | if (bestPositionTarget < bestPositionSource) {
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104 | individual.Tours[bestTour].Stops.Insert(bestPositionTarget, target);
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105 | individual.Tours[bestTour].Stops.Insert(bestPositionSource, source);
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106 | } else {
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107 | individual.Tours[bestTour].Stops.Insert(bestPositionSource, source);
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108 | individual.Tours[bestTour].Stops.Insert(bestPositionTarget, target);
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109 | }
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110 |
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111 | success = true;
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112 | } else {
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113 | if (j < i) {
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114 | destRoute.Stops.Insert(j, dest);
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115 | route1.Stops.Insert(i, city);
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116 | } else {
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117 | route1.Stops.Insert(i, city);
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118 | destRoute.Stops.Insert(j, dest);
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119 | }
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120 |
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121 | success = false;
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122 | }
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123 | } else {
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124 | success = false;
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125 | }
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126 |
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127 | return success;
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128 | }
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129 |
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130 | public static void ApplyManipulation(IRandom random, PotvinEncoding individual, IPickupAndDeliveryProblemInstance pdp, bool allowInfeasible) {
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131 | int selectedIndex = SelectRandomTourBiasedByLength(random, individual, pdp);
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132 | if (selectedIndex >= 0) {
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133 | Tour route1 =
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134 | individual.Tours[selectedIndex];
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135 |
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136 | int count = route1.Stops.Count;
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137 |
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138 | if (count > 0) {
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139 | int i = random.Next(0, count);
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140 | int city = route1.Stops[i];
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141 |
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142 | if (!PairwiseMove(individual, pdp, city, allowInfeasible))
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143 | i++;
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144 |
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145 | count = route1.Stops.Count;
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146 | }
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147 | }
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148 | }
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149 |
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150 |
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151 | protected override void Manipulate(IRandom random, PotvinEncoding individual) {
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152 | bool allowInfeasible = AllowInfeasibleSolutions.Value.Value;
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153 |
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154 | IPickupAndDeliveryProblemInstance pdp = ProblemInstance as IPickupAndDeliveryProblemInstance;
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155 |
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156 | if (pdp != null) {
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157 | ApplyManipulation(random, individual, pdp, allowInfeasible);
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158 | }
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159 | }
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160 | }
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161 | }
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