001 package net.sf.cpsolver.coursett.heuristics;
002
003 import java.util.ArrayList;
004 import java.util.Collection;
005 import java.util.Iterator;
006 import java.util.List;
007
008 import net.sf.cpsolver.coursett.model.Lecture;
009 import net.sf.cpsolver.coursett.model.Placement;
010 import net.sf.cpsolver.coursett.model.TimetableModel;
011 import net.sf.cpsolver.ifs.extension.Extension;
012 import net.sf.cpsolver.ifs.extension.MacPropagation;
013 import net.sf.cpsolver.ifs.heuristics.VariableSelection;
014 import net.sf.cpsolver.ifs.solution.Solution;
015 import net.sf.cpsolver.ifs.solver.Solver;
016 import net.sf.cpsolver.ifs.util.DataProperties;
017 import net.sf.cpsolver.ifs.util.ToolBox;
018
019 /**
020 * Lecture (variable) selection. <br>
021 * <br>
022 * If there are one or more variables unassigned, the variable selection
023 * criterion picks one of them randomly. We have tried several approaches using
024 * domain sizes, number of previous assignments, numbers of constraints in which
025 * the variable participates, etc., but there was no significant improvement in
026 * this timetabling problem towards the random selection of an unassigned
027 * variable. The reason is, that it is easy to go back when a wrong variable is
028 * picked - such a variable is unassigned when there is a conflict with it in
029 * some of the subsequent iterations. <br>
030 * <br>
031 * When all variables are assigned, an evaluation is made for each variable
032 * according to the above described weights. The variable with the worst
033 * evaluation is selected. This variable promises the best improvement in
034 * optimization. <br>
035 * <br>
036 * Parameters (selection among unassigned lectures):
037 * <table border='1'>
038 * <tr>
039 * <th>Parameter</th>
040 * <th>Type</th>
041 * <th>Comment</th>
042 * </tr>
043 * <tr>
044 * <td>Lecture.RouletteWheelSelection</td>
045 * <td>{@link Boolean}</td>
046 * <td>Roulette wheel selection</td>
047 * </tr>
048 * <tr>
049 * <td>Lecture.RandomWalkProb</td>
050 * <td>{@link Double}</td>
051 * <td>Random walk probability</td>
052 * </tr>
053 * <tr>
054 * <td>Lecture.DomainSizeWeight</td>
055 * <td>{@link Double}</td>
056 * <td>Domain size weight</td>
057 * </tr>
058 * <tr>
059 * <td>Lecture.NrAssignmentsWeight</td>
060 * <td>{@link Double}</td>
061 * <td>Number of assignments weight</td>
062 * </tr>
063 * <tr>
064 * <td>Lecture.InitialAssignmentWeight</td>
065 * <td>{@link Double}</td>
066 * <td>Initial assignment weight</td>
067 * </tr>
068 * <tr>
069 * <td>Lecture.NrConstraintsWeight</td>
070 * <td>{@link Double}</td>
071 * <td>Number of constraint weight</td>
072 * </tr>
073 * </table>
074 * <br>
075 * Parameters (selection among assigned lectures, when the solution is
076 * complete):
077 * <table border='1'>
078 * <tr>
079 * <th>Parameter</th>
080 * <th>Type</th>
081 * <th>Comment</th>
082 * </tr>
083 * <tr>
084 * <td>Comparator.HardStudentConflictWeight</td>
085 * <td>{@link Double}</td>
086 * <td>Hard student conflict weight</td>
087 * </tr>
088 * <tr>
089 * <td>Comparator.StudentConflictWeight</td>
090 * <td>{@link Double}</td>
091 * <td>Student conflict weight</td>
092 * </tr>
093 * <tr>
094 * <td>Comparator.TimePreferenceWeight</td>
095 * <td>{@link Double}</td>
096 * <td>Time preference weight</td>
097 * </tr>
098 * <tr>
099 * <td>Comparator.ContrPreferenceWeight</td>
100 * <td>{@link Double}</td>
101 * <td>Group constraint preference weight</td>
102 * </tr>
103 * <tr>
104 * <td>Comparator.RoomPreferenceWeight</td>
105 * <td>{@link Double}</td>
106 * <td>Room preference weight</td>
107 * </tr>
108 * <tr>
109 * <td>Comparator.UselessSlotWeight</td>
110 * <td>{@link Double}</td>
111 * <td>Useless slot weight</td>
112 * </tr>
113 * <tr>
114 * <td>Comparator.TooBigRoomWeight</td>
115 * <td>{@link Double}</td>
116 * <td>Too big room weight</td>
117 * </tr>
118 * <tr>
119 * <td>Comparator.DistanceInstructorPreferenceWeight</td>
120 * <td>{@link Double}</td>
121 * <td>Distance (of the rooms of the back-to-back classes) based instructor
122 * preferences weight</td>
123 * </tr>
124 * <tr>
125 * <td>Comparator.DeptSpreadPenaltyWeight</td>
126 * <td>{@link Double}</td>
127 * <td>Department balancing penalty (see
128 * {@link net.sf.cpsolver.coursett.constraint.DepartmentSpreadConstraint})</td>
129 * </tr>
130 * </table>
131 * <br>
132 * Parameters (selection among subset of lectures (faster)):
133 * <table border='1'>
134 * <tr>
135 * <th>Parameter</th>
136 * <th>Type</th>
137 * <th>Comment</th>
138 * </tr>
139 * <tr>
140 * <td>Lecture.SelectionSubSet</td>
141 * <td>{@link Boolean}</td>
142 * <td>Selection among subset of lectures (faster)</td>
143 * </tr>
144 * <tr>
145 * <td>Lecture.SelectionSubSetMinSize</td>
146 * <td>{@link Double}</td>
147 * <td>Minimal subset size</td>
148 * </tr>
149 * <tr>
150 * <td>Lecture.SelectionSubSetPart</td>
151 * <td>{@link Double}</td>
152 * <td>Subset size in percentage of all lectures available for selection</td>
153 * </tr>
154 * </table>
155 *
156 * @see PlacementSelection
157 * @version CourseTT 1.2 (University Course Timetabling)<br>
158 * Copyright (C) 2006 - 2010 Tomas Muller<br>
159 * <a href="mailto:muller@unitime.org">muller@unitime.org</a><br>
160 * <a href="http://muller.unitime.org">http://muller.unitime.org</a><br>
161 * <br>
162 * This library is free software; you can redistribute it and/or modify
163 * it under the terms of the GNU Lesser General Public License as
164 * published by the Free Software Foundation; either version 3 of the
165 * License, or (at your option) any later version. <br>
166 * <br>
167 * This library is distributed in the hope that it will be useful, but
168 * WITHOUT ANY WARRANTY; without even the implied warranty of
169 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
170 * Lesser General Public License for more details. <br>
171 * <br>
172 * You should have received a copy of the GNU Lesser General Public
173 * License along with this library; if not see
174 * <a href='http://www.gnu.org/licenses/'>http://www.gnu.org/licenses/</a>.
175 */
176 public class LectureSelection implements VariableSelection<Lecture, Placement> {
177 private double iRandomWalkProb;
178 private double iDomainSizeWeight;
179 private double iGoodValuesWeight;
180 private double iNrAssignmentsWeight;
181 private double iConstraintsWeight;
182 private double iInitialAssignmentWeight;
183 private boolean iRouletteWheelSelection;
184 private boolean iUnassignWhenNotGood;
185
186 private boolean iSubSetSelection;
187 private double iSelectionSubSetPart;
188 private int iSelectionSubSetMinSize;
189 private boolean iInteractiveMode;
190
191 private boolean iRW = false;
192 private boolean iMPP = false;
193
194 private MacPropagation<Lecture, Placement> iProp = null;
195
196 private int iTabuSize = 0;
197 private ArrayList<Lecture> iTabu = null;
198 private int iTabuPos = 0;
199
200 private int iVariableChanceIteration = 1000;
201 private double iVariableChanceProb = 0.05;
202
203 public LectureSelection(DataProperties properties) {
204 iRouletteWheelSelection = properties.getPropertyBoolean("Lecture.RouletteWheelSelection", true);
205 iUnassignWhenNotGood = properties.getPropertyBoolean("Lecture.UnassignWhenNotGood", false);
206 iRW = properties.getPropertyBoolean("General.RandomWalk", true);
207 iRandomWalkProb = (!iRW ? 0.0 : properties.getPropertyDouble("Lecture.RandomWalkProb", 1.00));
208 iGoodValuesWeight = properties.getPropertyDouble("Lecture.GoodValueProb", 1.0);
209 iDomainSizeWeight = properties.getPropertyDouble("Lecture.DomainSizeWeight", 30.0);
210
211 iInteractiveMode = properties.getPropertyBoolean("General.InteractiveMode", false);
212
213 iNrAssignmentsWeight = properties.getPropertyDouble("Lecture.NrAssignmentsWeight", 10.0);
214 iConstraintsWeight = properties.getPropertyDouble("Lecture.NrConstraintsWeight", 0.0);
215 iMPP = properties.getPropertyBoolean("General.MPP", false);
216 iInitialAssignmentWeight = (!iMPP ? 0.0 : properties.getPropertyDouble("Lecture.InitialAssignmentWeight", 20.0));
217
218 iSubSetSelection = properties.getPropertyBoolean("Lecture.SelectionSubSet", true);
219 iSelectionSubSetMinSize = properties.getPropertyInt("Lecture.SelectionSubSetMinSize", 10);
220 iSelectionSubSetPart = properties.getPropertyDouble("Lecture.SelectionSubSetPart", 0.2);
221
222 iTabuSize = properties.getPropertyInt("Lecture.TabuSize", 20);
223 if (iTabuSize > 0)
224 iTabu = new ArrayList<Lecture>(iTabuSize);
225
226 iVariableChanceIteration = properties.getPropertyInt("Lecture.VariableChanceIteration", 1000);
227 iVariableChanceProb = properties.getPropertyDouble("Lecture.VariableChanceProb", 0.05);
228 }
229
230 @Override
231 public void init(Solver<Lecture, Placement> solver) {
232 for (Extension<Lecture, Placement> extension : solver.getExtensions()) {
233 if (MacPropagation.class.isInstance(extension))
234 iProp = (MacPropagation<Lecture, Placement>) extension;
235 }
236 }
237
238 @Override
239 public Lecture selectVariable(Solution<Lecture, Placement> solution) {
240 TimetableModel model = (TimetableModel) solution.getModel();
241 Collection<Lecture> unassignedVariables = model.unassignedVariables();
242 if (iInteractiveMode) {
243 // remove variables that have no values
244 unassignedVariables = new ArrayList<Lecture>(unassignedVariables.size());
245 for (Lecture variable : model.unassignedVariables()) {
246 if (!variable.values().isEmpty())
247 unassignedVariables.add(variable);
248 }
249 }
250
251 if (unassignedVariables.isEmpty()) {
252 Collection<Lecture> variables = model.perturbVariables();
253 if (variables.isEmpty())
254 variables = model.assignedVariables();
255
256 if (iRW && ToolBox.random() <= iRandomWalkProb)
257 return ToolBox.random(variables);
258
259 List<Lecture> selectionVariables = new ArrayList<Lecture>();
260 double worstValue = 0.0;
261
262 for (Iterator<Lecture> i1 = (iSubSetSelection ? ToolBox.subSet(variables, iSelectionSubSetPart,
263 iSelectionSubSetMinSize) : variables).iterator(); i1.hasNext();) {
264 Lecture selectedVariable = i1.next();
265
266 if (iTabu != null && iTabu.contains(selectedVariable))
267 continue;
268
269 double value = selectedVariable.getAssignment().toDouble();
270
271 if (selectionVariables.isEmpty() || value > worstValue) {
272 selectionVariables.clear();
273 selectionVariables.add(selectedVariable);
274 worstValue = value;
275 } else if (worstValue == value) {
276 selectionVariables.add(selectedVariable);
277 }
278 }
279
280 Lecture selectedVariable = ToolBox.random(selectionVariables);
281
282 if (selectedVariable == null)
283 selectedVariable = ToolBox.random(variables);
284
285 if (selectedVariable != null && iTabu != null) {
286 if (iTabu.size() == iTabuPos)
287 iTabu.add(selectedVariable);
288 else
289 iTabu.set(iTabuPos, selectedVariable);
290 iTabuPos = (iTabuPos + 1) % iTabuSize;
291 }
292
293 return selectedVariable;
294 } else {
295 if (iVariableChanceIteration > 0) {
296 List<Lecture> variablesWithChance = new ArrayList<Lecture>(unassignedVariables.size());
297 for (Lecture v : unassignedVariables) {
298 if (v.countAssignments() > iVariableChanceIteration)
299 continue;
300 variablesWithChance.add(v);
301 }
302
303 if (variablesWithChance.isEmpty() && ToolBox.random() <= iVariableChanceProb
304 && !model.assignedVariables().isEmpty())
305 return ToolBox.random(model.assignedVariables());
306
307 if (ToolBox.random() <= iRandomWalkProb) {
308 if (!variablesWithChance.isEmpty())
309 return ToolBox.random(variablesWithChance);
310 else
311 return ToolBox.random(unassignedVariables);
312 }
313 } else {
314 if (ToolBox.random() <= iRandomWalkProb)
315 return ToolBox.random(unassignedVariables);
316 }
317
318 if (iProp != null && iUnassignWhenNotGood) {
319 List<Lecture> noGoodVariables = new ArrayList<Lecture>();
320 for (Iterator<Lecture> i1 = ToolBox.subSet(unassignedVariables, iSelectionSubSetPart,
321 iSelectionSubSetMinSize).iterator(); i1.hasNext();) {
322 Lecture variable = i1.next();
323 if (iProp.goodValues(variable).isEmpty())
324 noGoodVariables.add(variable);
325 }
326 if (!noGoodVariables.isEmpty()) {
327 if (ToolBox.random() < 0.02)
328 return ToolBox.random(model.assignedVariables());
329 for (int attempt = 0; attempt < 10; attempt++) {
330 Lecture noGoodVariable = ToolBox.random(noGoodVariables);
331 Placement noGoodValue = ToolBox.random(noGoodVariable.values());
332 if (!iProp.noGood(noGoodValue).isEmpty())
333 return ToolBox.random(iProp.noGood(noGoodValue)).variable();
334 }
335 }
336 }
337
338 if (iRouletteWheelSelection) {
339 int iMaxDomainSize = 0;
340 int iMaxGoodDomainSize = 0;
341 int iMaxConstraints = 0;
342 long iMaxNrAssignments = 0;
343 Collection<Lecture> variables = (iSubSetSelection ? ToolBox.subSet(unassignedVariables,
344 iSelectionSubSetPart, iSelectionSubSetMinSize) : unassignedVariables);
345 for (Lecture variable : variables) {
346
347 if (iTabu != null && iTabu.contains(variable))
348 continue;
349
350 iMaxDomainSize = Math.max(iMaxDomainSize, variable.values().size());
351 iMaxGoodDomainSize = (iProp == null ? 0 : Math.max(iMaxGoodDomainSize, iProp.goodValues(variable)
352 .size()));
353 iMaxConstraints = Math.max(iMaxConstraints, variable.constraints().size());
354 iMaxNrAssignments = Math.max(iMaxNrAssignments, variable.countAssignments());
355 }
356
357 List<Integer> points = new ArrayList<Integer>();
358 int totalPoints = 0;
359
360 for (Lecture variable : variables) {
361
362 long pointsThisVariable = Math
363 .round(iDomainSizeWeight
364 * (((double) (iMaxDomainSize - variable.values().size())) / ((double) iMaxDomainSize))
365 + (iProp == null ? 0.0
366 : iGoodValuesWeight
367 * (((double) (iMaxGoodDomainSize - iProp.goodValues(variable)
368 .size())) / ((double) iMaxGoodDomainSize)))
369 + iNrAssignmentsWeight
370 * (((double) variable.countAssignments()) / ((double) iMaxNrAssignments))
371 + iConstraintsWeight
372 * (((double) (iMaxConstraints - variable.constraints().size())) / ((double) iMaxConstraints))
373 + iInitialAssignmentWeight
374 * (variable.getInitialAssignment() != null ? model.conflictValues(
375 variable.getInitialAssignment()).size() : 0.0));
376 if (pointsThisVariable > 0) {
377 totalPoints += pointsThisVariable;
378 points.add(totalPoints);
379 }
380 }
381
382 if (totalPoints > 0) {
383 int rndPoints = ToolBox.random(totalPoints);
384 Iterator<Lecture> x = variables.iterator();
385 for (int i = 0; x.hasNext() && i < points.size(); i++) {
386 Lecture variable = x.next();
387 int tp = points.get(i);
388 if (tp > rndPoints) {
389 if (variable != null && iTabu != null) {
390 if (iTabu.size() == iTabuPos)
391 iTabu.add(variable);
392 else
393 iTabu.set(iTabuPos, variable);
394 iTabuPos = (iTabuPos + 1) % iTabuSize;
395 }
396 return variable;
397 }
398 }
399 }
400
401 } else {
402
403 List<Lecture> selectionVariables = null;
404 long bestGood = 0;
405 for (Iterator<Lecture> i = ToolBox.subSet(unassignedVariables, iSelectionSubSetPart,
406 iSelectionSubSetMinSize).iterator(); i.hasNext();) {
407 Lecture variable = i.next();
408
409 if (iTabu != null && iTabu.contains(variable))
410 continue;
411
412 long good = (long) (iDomainSizeWeight * variable.values().size() + iGoodValuesWeight
413 * (iProp == null ? 0 : iProp.goodValues(variable).size()) + iNrAssignmentsWeight
414 * variable.countAssignments() + iConstraintsWeight * variable.constraints().size() + iInitialAssignmentWeight
415 * (variable.getInitialAssignment() != null ? model.conflictValues(
416 variable.getInitialAssignment()).size() : 0.0));
417 if (selectionVariables == null || bestGood > good) {
418 if (selectionVariables == null)
419 selectionVariables = new ArrayList<Lecture>();
420 else
421 selectionVariables.clear();
422 bestGood = good;
423 selectionVariables.add(variable);
424 } else if (good == bestGood) {
425 selectionVariables.add(variable);
426 }
427 }
428
429 if (!selectionVariables.isEmpty()) {
430 Lecture selectedVariable = ToolBox.random(selectionVariables);
431
432 if (selectedVariable != null && iTabu != null) {
433 if (iTabu.size() == iTabuPos)
434 iTabu.add(selectedVariable);
435 else
436 iTabu.set(iTabuPos, selectedVariable);
437 iTabuPos = (iTabuPos + 1) % iTabuSize;
438 }
439
440 return selectedVariable;
441 }
442 }
443
444 Lecture selectedVariable = ToolBox.random(unassignedVariables);
445
446 if (selectedVariable != null && iTabu != null) {
447 if (iTabu.size() == iTabuPos)
448 iTabu.add(selectedVariable);
449 else
450 iTabu.set(iTabuPos, selectedVariable);
451 iTabuPos = (iTabuPos + 1) % iTabuSize;
452 }
453
454 return selectedVariable;
455 }
456 }
457
458 }