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Solving the post enrolment course timetabling problem by ant colony optimization

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  • Clemens Nothegger
  • Alfred Mayer
  • Andreas Chwatal
  • Günther Raidl

Abstract

In this work we present a new approach to tackle the problem of Post Enrolment Course Timetabling as specified for the International Timetabling Competition 2007 (ITC2007), competition track 2. The heuristic procedure is based on Ant Colony Optimization (ACO) where artificial ants successively construct solutions based on pheromones (stigmergy) and local information. The key feature of our algorithm is the use of two distinct but simplified pheromone matrices in order to improve convergence but still provide enough flexibility for effectively guiding the solution construction process. We show that by parallelizing the algorithm we can improve the solution quality significantly. We applied our algorithm to the instances used for the ITC2007. The results document that our approach is among the leading algorithms for this problem; in all cases the optimal solution could be found. Furthermore we discuss the characteristics of the instances where the algorithm performs especially well. Copyright Springer Science+Business Media, LLC 2012

Suggested Citation

  • Clemens Nothegger & Alfred Mayer & Andreas Chwatal & Günther Raidl, 2012. "Solving the post enrolment course timetabling problem by ant colony optimization," Annals of Operations Research, Springer, vol. 194(1), pages 325-339, April.
    • Handle: RePEc:spr:annopr:v:194:y:2012:i:1:p:325-339:10.1007/s10479-012-1078-5
    DOI: 10.1007/s10479-012-1078-5
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    References listed on IDEAS

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    1. Burke, Edmund Kieran & Petrovic, Sanja, 2002. "Recent research directions in automated timetabling," European Journal of Operational Research, Elsevier, vol. 140(2), pages 266-280, July.
    2. Barry McCollum & Andrea Schaerf & Ben Paechter & Paul McMullan & Rhyd Lewis & Andrew J. Parkes & Luca Di Gaspero & Rong Qu & Edmund K. Burke, 2010. "Setting the Research Agenda in Automated Timetabling: The Second International Timetabling Competition," INFORMS Journal on Computing, INFORMS, vol. 22(1), pages 120-130, February.
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    Cited by:

    1. Lewis, R. & Thompson, J., 2015. "Analysing the effects of solution space connectivity with an effective metaheuristic for the course timetabling problem," European Journal of Operational Research, Elsevier, vol. 240(3), pages 637-648.
    2. Say Leng Goh & Graham Kendall & Nasser R. Sabar & Salwani Abdullah, 2020. "An effective hybrid local search approach for the post enrolment course timetabling problem," OPSEARCH, Springer;Operational Research Society of India, vol. 57(4), pages 1131-1163, December.
    3. Goh, Say Leng & Kendall, Graham & Sabar, Nasser R., 2017. "Improved local search approaches to solve the post enrolment course timetabling problem," European Journal of Operational Research, Elsevier, vol. 261(1), pages 17-29.
    4. Fabian Dunke & Stefan Nickel, 2023. "A matheuristic for customized multi-level multi-criteria university timetabling," Annals of Operations Research, Springer, vol. 328(2), pages 1313-1348, September.
    5. Thepphakorn, Thatchai & Pongcharoen, Pupong & Hicks, Chris, 2014. "An ant colony based timetabling tool," International Journal of Production Economics, Elsevier, vol. 149(C), pages 131-144.

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