Index: /branches/1614_GeneralizedQAP/HeuristicLab.Problems.GeneralizedQuadraticAssignment.Algorithms/3.3/HeuristicLab.Problems.GeneralizedQuadraticAssignment.Algorithms-3.3.csproj
===================================================================
--- /branches/1614_GeneralizedQAP/HeuristicLab.Problems.GeneralizedQuadraticAssignment.Algorithms/3.3/HeuristicLab.Problems.GeneralizedQuadraticAssignment.Algorithms-3.3.csproj	(revision 15757)
+++ /branches/1614_GeneralizedQAP/HeuristicLab.Problems.GeneralizedQuadraticAssignment.Algorithms/3.3/HeuristicLab.Problems.GeneralizedQuadraticAssignment.Algorithms-3.3.csproj	(revision 15758)
@@ -130,4 +130,5 @@
     <Compile Include="LocalSearch\IteratedLS.cs" />
     <Compile Include="LocalSearch\MultistartLS.cs" />
+    <Compile Include="LocalSolverNet\GQAPListSolver.cs" />
     <Compile Include="LocalSolverNet\GQAPIntegerSolver.cs" />
     <Compile Include="LocalSolverNet\LocalSolverContext.cs" />
Index: /branches/1614_GeneralizedQAP/HeuristicLab.Problems.GeneralizedQuadraticAssignment.Algorithms/3.3/LocalSolverNet/GQAPListSolver.cs
===================================================================
--- /branches/1614_GeneralizedQAP/HeuristicLab.Problems.GeneralizedQuadraticAssignment.Algorithms/3.3/LocalSolverNet/GQAPListSolver.cs	(revision 15758)
+++ /branches/1614_GeneralizedQAP/HeuristicLab.Problems.GeneralizedQuadraticAssignment.Algorithms/3.3/LocalSolverNet/GQAPListSolver.cs	(revision 15758)
@@ -0,0 +1,225 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2017 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;
+using System.Threading;
+using HeuristicLab.Common;
+using HeuristicLab.Core;
+using HeuristicLab.Data;
+using HeuristicLab.Encodings.IntegerVectorEncoding;
+using HeuristicLab.Optimization;
+using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+using localsolver;
+
+namespace HeuristicLab.Problems.GeneralizedQuadraticAssignment.Algorithms.LocalSolverNet {
+  [Item("LocalSolver List (GQAP)", "LocalSolver algorithm solving the GQAP using list decision variables")]
+  [StorableClass]
+  [Creatable(CreatableAttribute.Categories.Algorithms)]
+  public sealed class GQAPListSolver : ContextAlgorithm<LocalSolverContext, IntegerVectorEncoding> {
+    public override bool SupportsPause {
+      get { return false; }
+    }
+
+    public override Type ProblemType {
+      get { return typeof(GQAP); }
+    }
+
+    public new GQAP Problem {
+      get { return (GQAP)base.Problem; }
+      set { base.Problem = value; }
+    }
+
+    // LS Program variables
+    private LSExpression[] x;
+    private LSExpression[] equipmentsOnLocations;
+    private LSExpression obj;
+    private LocalSolver localSolver;
+
+
+    [StorableConstructor]
+    private GQAPListSolver(bool deserializing) : base(deserializing) { }
+    private GQAPListSolver(GQAPListSolver original, Cloner cloner)
+    : base(original, cloner) {
+    }
+    public GQAPListSolver() {
+      Problem = new GQAP();
+      MaximumEvaluationsParameter.Hidden = true;
+      MaximumIterationsParameter.Hidden = true;
+    }
+
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new GQAPListSolver(this, cloner);
+    }
+
+    private double prevObj;
+    private DateTime lastUpdate;
+    private CancellationToken token;
+
+    private void LocalSolverOnIterationTicked(LocalSolver ls, LSCallbackType type) {
+      IResult result;
+      Context.Iterations++;
+      if ((DateTime.UtcNow - lastUpdate) > TimeSpan.FromSeconds(1)) {
+        if (Results.TryGetValue("Iterations", out result))
+          ((IntValue)result.Value).Value = Context.Iterations;
+        else Results.Add(new Result("Iterations", new IntValue(Context.Iterations)));
+        lastUpdate = DateTime.UtcNow;
+      }
+
+      if (token.IsCancellationRequested) localSolver.Stop();
+
+      var curObj = obj.GetDoubleValue();
+
+      if (curObj >= prevObj) return;
+      UpdateSolution(curObj);
+
+      Context.RunOperator(Analyzer, CancellationToken.None);
+
+      if (StoppingCriterion()) localSolver.Stop();
+    }
+
+    private void UpdateSolution(double obj) {
+      IResult result;
+      prevObj = obj;
+      Context.BestQuality = obj;
+
+      if (Results.TryGetValue("BestQuality", out result))
+        ((DoubleValue)result.Value).Value = Context.BestQuality;
+      else Results.Add(new Result("BestQuality", new DoubleValue(Context.BestQuality)));
+
+      var locations = Problem.ProblemInstance.Capacities.Length;
+      var best = new int[Problem.ProblemInstance.Demands.Length];
+      for (var j = 0; j < locations; j++) {
+        var xcol = x[j].GetCollectionValue();
+        for (var i = 0; i < xcol.Count(); i++) {
+          var equip = xcol.Get(i);
+          best[equip] = j;
+        }
+      }
+      var bestVec = new IntegerVector(best);
+      var eval = Problem.ProblemInstance.Evaluate(bestVec);
+      Context.BestSolution = new GQAPSolution(bestVec, eval);
+
+      var scope = Context.ToScope(new GQAPSolution(new IntegerVector(best), (Evaluation)eval.Clone()), Problem.ProblemInstance.ToSingleObjective(eval));
+      Context.ReplaceIncumbent(scope);
+
+      if (Results.TryGetValue("BestSolution", out result))
+        result.Value = Context.BestSolution;
+      else Results.Add(new Result("BestSolution", Context.BestSolution));
+    }
+
+    protected override void Initialize(CancellationToken cancellationToken) {
+      base.Initialize(cancellationToken);
+
+      prevObj = double.MaxValue;
+    }
+
+    protected override void Run(CancellationToken cancellationToken) {
+      base.Run(cancellationToken);
+      token = cancellationToken;
+      lastUpdate = DateTime.UtcNow.AddSeconds(-1);
+      localSolver = new LocalSolver();
+
+      // Declares the optimization model
+      var model = localSolver.GetModel();
+
+      var data = Problem.ProblemInstance;
+
+      x = new LSExpression[data.Capacities.Length];
+      // x[f,l] = 1 if equipments f is on location l, 0 otherwise
+      for (int r = 0; r < data.Capacities.Length; r++) {
+        x[r] = model.List(data.Demands.Length);
+      }
+
+      // All equipments are installed in exactly 1 location
+      model.Constraint(model.Partition(x));
+
+      var demandsArray = model.Array(data.Demands);
+
+      // Create distances as an array to be accessed by an at operator
+      var weightsJagged = new double[data.Demands.Length][];
+      for (var i = 0; i < data.Demands.Length; i++) {
+        weightsJagged[i] = new double[data.Demands.Length];
+        for (var j = 0; j < data.Demands.Length; j++) {
+          weightsJagged[i][j] = data.Weights[i, j];
+        }
+      }
+      var distancesJagged = new double[data.Capacities.Length][];
+      for (var i = 0; i < data.Capacities.Length; i++) {
+        distancesJagged[i] = new double[data.Capacities.Length];
+        for (var j = 0; j < data.Capacities.Length; j++)
+          distancesJagged[i][j] = data.Distances[i, j];
+      }
+      var installJagged = new double[data.Demands.Length][];
+      for (var i = 0; i < data.Demands.Length; i++) {
+        installJagged[i] = new double[data.Capacities.Length];
+        for (var j = 0; j < data.Capacities.Length; j++)
+          installJagged[i][j] = data.InstallationCosts[i, j];
+      }
+      var weightsArray = model.Array(weightsJagged);
+      var distancesArray = model.Array(distancesJagged);
+      var installCostsArray = model.Array(installJagged);
+
+      obj = model.Sum();
+      // All locations contain not more equipments than there is capacity for
+      for (int l = 0; l < data.Capacities.Length; l++) {
+        var c = model.Count(x[l]);
+        var demandSelector = model.Function(i => demandsArray[x[l][i]]);
+        var assignedDemand = model.Sum(model.Range(0, c), demandSelector);
+        model.Constraint(assignedDemand <= data.Capacities[l]);
+
+        var installCostSelector = model.Function(i => installCostsArray[x[l][i], l]);
+        var installCosts = model.Sum(model.Range(0, c), installCostSelector);
+        obj.AddOperand(installCosts);
+        
+        // Flow to other equipments
+        for (int k = 0; k < data.Capacities.Length; k++) {
+          var kc = model.Count(x[k]);
+          var flowCostSelector = model.Function(j => model.Sum(model.Range(0, c), model.Function(i => weightsArray[x[l][i], x[k][j]] * distancesArray[l, k] * data.TransportationCosts)));
+          obj.AddOperand(model.Sum(model.Range(0, kc), flowCostSelector));
+        }
+      }
+
+      model.Minimize(this.obj);
+
+      try {
+        model.Close();
+
+        // Parameterizes the solver.
+        LSPhase phase = localSolver.CreatePhase();
+        phase.SetTimeLimit((int)Math.Ceiling(MaximumRuntime.TotalSeconds));
+
+        localSolver.AddCallback(LSCallbackType.IterationTicked, LocalSolverOnIterationTicked);
+
+        localSolver.Solve();
+
+        var curObj = this.obj.GetDoubleValue();
+        if (curObj < prevObj)
+          UpdateSolution(curObj);
+
+        localSolver.RemoveCallback(LSCallbackType.IterationTicked, LocalSolverOnIterationTicked);
+      } finally {
+        localSolver.Dispose();
+      }
+
+      Context.RunOperator(Analyzer, CancellationToken.None);
+    }
+  }
+}
