#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2011 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
 *
 * This file is part of HeuristicLab.
 *
 * HeuristicLab is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * HeuristicLab is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
 */
#endregion

using System.Collections.Generic;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Data;
using HeuristicLab.Parameters;
using HeuristicLab.Problems.VehicleRouting.Encodings.Potvin;
using HeuristicLab.Problems.VehicleRouting.Encodings;
using HeuristicLab.Problems.VehicleRouting;
using HeuristicLab.Problems.VehicleRouting.Interfaces;

namespace HeuristicLab.Analysis.FitnessLandscape.VRP {
  [Item("RelocateManipulator", "Relocate manipulation")]
  [StorableClass]
  public sealed class RelocateManipulator : PotvinManipulator {
    [StorableConstructor]
    private RelocateManipulator(bool deserializing) : base(deserializing) { }
    private RelocateManipulator(RelocateManipulator original, Cloner cloner)
      : base(original, cloner) {
    }
    public override IDeepCloneable Clone(Cloner cloner) {
      return new RelocateManipulator(this, cloner);
    }
    public RelocateManipulator()
      : base() {
    }

    public static void Apply(IRandom random, PotvinEncoding individual, IVRPProblemInstance problemInstance, bool allowInfeasible) {
      bool feasible;
      int count = 0;

      do {
        feasible = true;

        int cities = individual.Cities;
        int city = 1 + random.Next(cities);
        Tour originalTour = individual.Tours.Find(t => t.Stops.Contains(city));
        //consider creating new route
        individual.Tours.Add(new Tour());

        int position = 1 + random.Next(cities + individual.Tours.Count - 1);
        if (position >= city) {
          position++;
        }

        var originalFeasible = problemInstance.TourFeasible(originalTour, individual);

        int originalPosition = originalTour.Stops.IndexOf(city);
        originalTour.Stops.RemoveAt(originalPosition);

        Tour insertionTour;
        int insertionPosition;
        if (position <= cities) {
          insertionTour = individual.Tours.Find(t => t.Stops.Contains(position));
          insertionPosition = insertionTour.Stops.IndexOf(position) + 1;
        } else { // additional insertion positions at beginning of tours
          insertionTour = individual.Tours[position - cities - 1];
          insertionPosition = 0;
        }
        originalFeasible &= problemInstance.TourFeasible(insertionTour, individual);
        insertionTour.Stops.Insert(insertionPosition, city);

        if (!allowInfeasible && originalFeasible) {
          feasible = problemInstance.TourFeasible(insertionTour, individual) &&
                     problemInstance.TourFeasible(originalTour, individual);
          if (!feasible) {
            insertionTour.Stops.RemoveAt(insertionPosition);
            originalTour.Stops.Insert(originalPosition, city);
          }
        }
        individual.Tours.RemoveAll(t => t.Stops.Count == 0);
      } while (!feasible && count++ < 100);
    }

    protected override void Manipulate(IRandom random, PotvinEncoding individual) {
      bool allowInfeasible = AllowInfeasibleSolutions.Value.Value;
      Apply(random, individual, ProblemInstance, allowInfeasible);
    }
  }
}