ttsolver.heuristics
Class LectureSelection

java.lang.Object
  ttsolver.heuristics.LectureSelection

All Implemented Interfaces:

VariableSelection

public class LectureSelection
extends Object
implements VariableSelection

Lecture (variable) selection.

If there are one or more variables unassigned, the variable selection criterion picks one of them randomly. We have tried several approaches using domain sizes, number of previous assignments, numbers of constraints in which the variable participates, etc., but there was no significant improvement in this timetabling problem towards the random selection of an unassigned variable. The reason is, that it is easy to go back when a wrong variable is picked - such a variable is unassigned when there is a conflict with it in some of the subsequent iterations.

When all variables are assigned, an evaluation is made for each variable according to the above described weights. The variable with the worst evaluation is selected. This variable promises the best improvement in optimization.

Parameters (selection among unassigned lectures):

Parameter	Type	Comment
Lecture.RouletteWheelSelection	Boolean	Roulette wheel selection
Lecture.RandomWalkProb	Double	Random walk probability
Lecture.DomainSizeWeight	Double	Domain size weight
Lecture.NrAssignmentsWeight	Double	Number of assignments weight
Lecture.InitialAssignmentWeight	Double	Initial assignment weight
Lecture.NrConstraintsWeight	Double	Number of constraint weight

Parameters (selection among assigned lectures, when the solution is complete):

Parameter	Type	Comment
Lecture.HardStudentConflictWeight	Double	Hard student conflict weight
Lecture.StudentConflictWeight	Double	Student conflict weight
Lecture.TimePreferenceWeight	Double	Time preference weight
Lecture.ContrPreferenceWeight	Double	Group constraint preference weight
Lecture.RoomPreferenceWeight	Double	Room preference weight
Lecture.UselessSlotWeight	Double	Useless slot weight
Lecture.TooBigRoomWeight	Double	Too big room weight
Lecture.DistanceInstructorPreferenceWeight	Double	Distance (of the rooms of the back-to-back classes) based instructor preferences weight
Lecture.DeptSpreadPenaltyWeight	Double	Department balancing penalty (see DepartmentSpreadConstraint)

Parameters (selection among subset of lectures (faster)):

Parameter	Type	Comment
Lecture.SelectionSubSet	Boolean	Selection among subset of lectures (faster)
Lecture.SelectionSubSetMinSize	Double	Minimal subset size
Lecture.SelectionSubSetPart	Double	Subset size in percentage of all lectures available for selection

Version:

1.0

Author:

Tomáš Müller

See Also:

PlacementSelection

Constructor Summary

LectureSelection(DataProperties properties)

Method Summary

void	init(Solver solver) Initialization
static Collection	parameters()
Variable	selectVariable(Solution solution) Variable selection

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

LectureSelection

public LectureSelection(DataProperties properties)

Method Detail

parameters

public static Collection parameters()

init

public void init(Solver solver)

Description copied from interface: VariableSelection

Initialization

Specified by:

init in interface VariableSelection

selectVariable

public Variable selectVariable(Solution solution)

Description copied from interface: VariableSelection

Variable selection

Specified by:

selectVariable in interface VariableSelection

Parameters:

solution - current solution