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Local search and constraint programming for the post enrolment-based course timetabling problem

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  • Hadrien Cambazard
  • Emmanuel Hebrard
  • Barry O’Sullivan
  • Alexandre Papadopoulos

Abstract

We present a variety of approaches for solving the post enrolment-based course timetabling problem, which was proposed as Track 2 of the 2007 International Timetabling Competition. We approach the problem using local search and constraint programming techniques. We show how to take advantage of a list-colouring relaxation of the problem. Our local search approach won Track 2 of the 2007 competition. Our best constraint programming approach uses an original problem decomposition. Incorporating this into a large neighbourhood search scheme seems promising, and provides motivation for studying complete approaches in further detail. Copyright Springer Science+Business Media, LLC 2012

Suggested Citation

  • Hadrien Cambazard & Emmanuel Hebrard & Barry O’Sullivan & Alexandre Papadopoulos, 2012. "Local search and constraint programming for the post enrolment-based course timetabling problem," Annals of Operations Research, Springer, vol. 194(1), pages 111-135, April.
    • Handle: RePEc:spr:annopr:v:194:y:2012:i:1:p:111-135:10.1007/s10479-010-0737-7
    DOI: 10.1007/s10479-010-0737-7
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    References listed on IDEAS

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    1. de Werra, D., 1985. "An introduction to timetabling," European Journal of Operational Research, Elsevier, vol. 19(2), pages 151-162, February.
    2. Vipul Jain & Ignacio E. Grossmann, 2001. "Algorithms for Hybrid MILP/CP Models for a Class of Optimization Problems," INFORMS Journal on Computing, INFORMS, vol. 13(4), pages 258-276, November.
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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. Kadri Sylejmani & Edon Gashi & Adrian Ymeri, 2023. "Simulated annealing with penalization for university course timetabling," Journal of Scheduling, Springer, vol. 26(5), pages 497-517, October.
    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. Ceschia, Sara & Di Gaspero, Luca & Schaerf, Andrea, 2023. "Educational timetabling: Problems, benchmarks, and state-of-the-art results," European Journal of Operational Research, Elsevier, vol. 308(1), pages 1-18.
    5. Soria-Alcaraz, Jorge A. & Ochoa, Gabriela & Sotelo-Figeroa, Marco A. & Burke, Edmund K., 2017. "A methodology for determining an effective subset of heuristics in selection hyper-heuristics," European Journal of Operational Research, Elsevier, vol. 260(3), pages 972-983.
    6. W. Jaśkowski & M. Szubert & P. Gawron, 2016. "A hybrid MIP-based large neighborhood search heuristic for solving the machine reassignment problem," Annals of Operations Research, Springer, vol. 242(1), pages 33-62, July.
    7. 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.
    8. Ghaith Jaradat & Masri Ayob & Zulkifli Ahmad, 2014. "On the performance of Scatter Search for post-enrolment course timetabling problems," Journal of Combinatorial Optimization, Springer, vol. 27(3), pages 417-439, April.
    9. 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.

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