A heuristic incremental modeling approach to course timetabling

Don Banks; Peter Van Beek; Amnon Meisels

doi:10.1007/3-540-64575-6_37

A heuristic incremental modeling approach to course timetabling

Don Banks, Peter Van Beek, Amnon Meisels

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

15 Scopus citations

Abstract

The general timetabling problem is an assignment of activities to fixed time intervals, adhering to a predefined set of resource availabilities. Timetabling problems are difficult to solve and can be extremely time-consuming without some computer assistance. In this paper the application of constraint-based reasoning to timetable generation is examined. Specifically, we consider how a timetabling problem can be represented as a Constraint Satisfaction Problem (CSP), and propose an algorithm for its solution which improves upon the basic idea of backtracking. Normally, when a backtracking routine fails to find a solution, there is nothing of value returned to the user; however, our algorithm extends this process by iteratively adding constraints to the CSP representation. A generalized random model of timetabling problems is proposed. This model creates a diverse range of problem instances, which are used to verify our search algorithm and identify the characteristics of difficult timetabling problems.

Original language	English
Title of host publication	Advances in Artificial Intelligence - 12th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, AI 1998, Proceedings
Editors	Robert E. Mercer, Eric Neufeld
Publisher	Springer Verlag
Pages	16-29
Number of pages	14
ISBN (Print)	3540645756, 9783540645757
DOIs	https://doi.org/10.1007/3-540-64575-6_37
State	Published - 1998
Externally published	Yes
Event	12th Biennial Conference on Artificial Intelligence, AI 1998 - Vancouver, Canada Duration: 18 Jun 1998 → 20 Jun 1998

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	1418
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	12th Biennial Conference on Artificial Intelligence, AI 1998
Country/Territory	Canada
City	Vancouver
Period	18/06/98 → 20/06/98

Bibliographical note

Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 1998.

Access to Document

10.1007/3-540-64575-6_37

Cite this

Banks, D., Van Beek, P., & Meisels, A. (1998). A heuristic incremental modeling approach to course timetabling. In R. E. Mercer, & E. Neufeld (Eds.), Advances in Artificial Intelligence - 12th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, AI 1998, Proceedings (pp. 16-29). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1418). Springer Verlag. https://doi.org/10.1007/3-540-64575-6_37

Banks, Don ; Van Beek, Peter ; Meisels, Amnon. / A heuristic incremental modeling approach to course timetabling. Advances in Artificial Intelligence - 12th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, AI 1998, Proceedings. editor / Robert E. Mercer ; Eric Neufeld . Springer Verlag, 1998. pp. 16-29 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{c1acc03f506444618d0776bcc0d198a8,

title = "A heuristic incremental modeling approach to course timetabling",

abstract = "The general timetabling problem is an assignment of activities to fixed time intervals, adhering to a predefined set of resource availabilities. Timetabling problems are difficult to solve and can be extremely time-consuming without some computer assistance. In this paper the application of constraint-based reasoning to timetable generation is examined. Specifically, we consider how a timetabling problem can be represented as a Constraint Satisfaction Problem (CSP), and propose an algorithm for its solution which improves upon the basic idea of backtracking. Normally, when a backtracking routine fails to find a solution, there is nothing of value returned to the user; however, our algorithm extends this process by iteratively adding constraints to the CSP representation. A generalized random model of timetabling problems is proposed. This model creates a diverse range of problem instances, which are used to verify our search algorithm and identify the characteristics of difficult timetabling problems.",

author = "Don Banks and {Van Beek}, Peter and Amnon Meisels",

note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 1998.; 12th Biennial Conference on Artificial Intelligence, AI 1998 ; Conference date: 18-06-1998 Through 20-06-1998",

year = "1998",

doi = "10.1007/3-540-64575-6_37",

language = "אנגלית",

isbn = "3540645756",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "16--29",

editor = "Mercer, {Robert E.} and {Neufeld }, Eric",

booktitle = "Advances in Artificial Intelligence - 12th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, AI 1998, Proceedings",

address = "גרמניה",

}

Banks, D, Van Beek, P & Meisels, A 1998, A heuristic incremental modeling approach to course timetabling. in RE Mercer & E Neufeld (eds), Advances in Artificial Intelligence - 12th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, AI 1998, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1418, Springer Verlag, pp. 16-29, 12th Biennial Conference on Artificial Intelligence, AI 1998, Vancouver, Canada, 18/06/98. https://doi.org/10.1007/3-540-64575-6_37

A heuristic incremental modeling approach to course timetabling. / Banks, Don; Van Beek, Peter; Meisels, Amnon.
Advances in Artificial Intelligence - 12th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, AI 1998, Proceedings. ed. / Robert E. Mercer; Eric Neufeld . Springer Verlag, 1998. p. 16-29 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1418).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A heuristic incremental modeling approach to course timetabling

AU - Banks, Don

AU - Van Beek, Peter

AU - Meisels, Amnon

N1 - Publisher Copyright: © Springer-Verlag Berlin Heidelberg 1998.

PY - 1998

Y1 - 1998

N2 - The general timetabling problem is an assignment of activities to fixed time intervals, adhering to a predefined set of resource availabilities. Timetabling problems are difficult to solve and can be extremely time-consuming without some computer assistance. In this paper the application of constraint-based reasoning to timetable generation is examined. Specifically, we consider how a timetabling problem can be represented as a Constraint Satisfaction Problem (CSP), and propose an algorithm for its solution which improves upon the basic idea of backtracking. Normally, when a backtracking routine fails to find a solution, there is nothing of value returned to the user; however, our algorithm extends this process by iteratively adding constraints to the CSP representation. A generalized random model of timetabling problems is proposed. This model creates a diverse range of problem instances, which are used to verify our search algorithm and identify the characteristics of difficult timetabling problems.

AB - The general timetabling problem is an assignment of activities to fixed time intervals, adhering to a predefined set of resource availabilities. Timetabling problems are difficult to solve and can be extremely time-consuming without some computer assistance. In this paper the application of constraint-based reasoning to timetable generation is examined. Specifically, we consider how a timetabling problem can be represented as a Constraint Satisfaction Problem (CSP), and propose an algorithm for its solution which improves upon the basic idea of backtracking. Normally, when a backtracking routine fails to find a solution, there is nothing of value returned to the user; however, our algorithm extends this process by iteratively adding constraints to the CSP representation. A generalized random model of timetabling problems is proposed. This model creates a diverse range of problem instances, which are used to verify our search algorithm and identify the characteristics of difficult timetabling problems.

UR - http://www.scopus.com/inward/record.url?scp=77956263702&partnerID=8YFLogxK

U2 - 10.1007/3-540-64575-6_37

DO - 10.1007/3-540-64575-6_37

M3 - ???researchoutput.researchoutputtypes.contributiontobookanthology.conference???

AN - SCOPUS:77956263702

SN - 3540645756

SN - 9783540645757

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 16

EP - 29

BT - Advances in Artificial Intelligence - 12th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, AI 1998, Proceedings

A2 - Mercer, Robert E.

A2 - Neufeld , Eric

PB - Springer Verlag

T2 - 12th Biennial Conference on Artificial Intelligence, AI 1998

Y2 - 18 June 1998 through 20 June 1998

ER -

Banks D, Van Beek P, Meisels A. A heuristic incremental modeling approach to course timetabling. In Mercer RE, Neufeld E, editors, Advances in Artificial Intelligence - 12th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, AI 1998, Proceedings. Springer Verlag. 1998. p. 16-29. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/3-540-64575-6_37

A heuristic incremental modeling approach to course timetabling

Abstract

Publication series

Conference

Bibliographical note

Access to Document

Other files and links

Fingerprint

Cite this