#region License Information
/* HeuristicLab
* Copyright (C) 2002-2018 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 .
*/
#endregion
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
namespace HeuristicLab.Encodings.ScheduleEncoding.ScheduleEncoding {
[Item("DirectScheduleGTCrossover", "Represents a crossover using the GT-Algorithm to cross two direct schedule representations.")]
[StorableClass]
public class DirectScheduleGTCrossover : DirectScheduleCrossover {
public IValueLookupParameter MutationProbabilityParameter {
get { return (IValueLookupParameter)Parameters["MutationProbability"]; }
}
[StorableConstructor]
protected DirectScheduleGTCrossover(bool deserializing) : base(deserializing) { }
protected DirectScheduleGTCrossover(DirectScheduleGTCrossover original, Cloner cloner) : base(original, cloner) { }
public DirectScheduleGTCrossover()
: base() {
Parameters.Add(new ValueLookupParameter("MutationProbability", "The probability that a task from the conflict set is chosen randomly instead of from one of the parents."));
}
public override IDeepCloneable Clone(Cloner cloner) {
return new DirectScheduleGTCrossover(this, cloner);
}
public static Schedule Apply(IRandom random, Schedule parent1, Schedule parent2, ItemList jobData, double mutProp) {
var child = new Schedule(parent1.Resources.Count);
//Reset scheduled tasks in result
foreach (Job j in jobData) {
foreach (Task t in j.Tasks) {
t.IsScheduled = false;
}
}
//GT-Algorithm
//STEP 0 - Compute a list of "earliest operations"
ItemList earliestTasksList = GTAlgorithmUtils.GetEarliestNotScheduledTasks(jobData);
while (earliestTasksList.Count > 0) {
//STEP 1 - Get earliest not scheduled operation with minimal earliest completing time
Task minimal = GTAlgorithmUtils.GetTaskWithMinimalEC(earliestTasksList, child);
int conflictedResourceNr = minimal.ResourceNr;
Resource conflictedResource = child.Resources[conflictedResourceNr];
//STEP 2 - Compute a conflict set of all operations that can be scheduled on the conflicted resource
ItemList conflictSet = GTAlgorithmUtils.GetConflictSetForTask(minimal, earliestTasksList, jobData, child);
//STEP 3 - Select a task from the conflict set
int progressOnResource = conflictedResource.Tasks.Count;
Task selectedTask = null;
if (random.NextDouble() < mutProp) {
//Mutation
selectedTask = conflictSet[random.Next(conflictSet.Count)];
} else {
//Crossover
selectedTask = SelectTaskFromConflictSet(conflictSet, ((random.Next(2) == 0) ? parent1 : parent2), conflictedResourceNr, progressOnResource);
}
//STEP 4 - Add the selected task to the current schedule
selectedTask.IsScheduled = true;
double startTime = GTAlgorithmUtils.ComputeEarliestStartTime(selectedTask, child);
child.ScheduleTask(selectedTask.ResourceNr, startTime, selectedTask.Duration, selectedTask.JobNr);
//STEP 5 - Back to STEP 1
earliestTasksList = GTAlgorithmUtils.GetEarliestNotScheduledTasks(jobData);
}
return child;
}
private static Task SelectTaskFromConflictSet(ItemList conflictSet, Schedule usedParent, int conflictedResourceNr, int progressOnResource) {
//Apply Crossover
foreach (ScheduledTask st in usedParent.Resources[conflictedResourceNr].Tasks) {
foreach (Task t in conflictSet) {
if (st.JobNr == t.JobNr)
return t;
}
}
return conflictSet[0];
}
public override Schedule Cross(IRandom random, Schedule parent1, Schedule parent2) {
var jobData = (ItemList)JobDataParameter.ActualValue.Clone();
var mutProp = MutationProbabilityParameter.ActualValue;
return Apply(random, parent1, parent2, jobData, mutProp.Value);
}
}
}