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Improved local search approaches to solve the post enrolment course timetabling problem

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  • Goh, Say Leng
  • Kendall, Graham
  • Sabar, Nasser R.

Abstract

In this work, we are addressing the post enrollment course timetabling (PE-CTT) problem. We combine different local search algorithms into an iterative two stage procedure. In the first stage, Tabu Search with Sampling and Perturbation (TSSP) is used to generate feasible solutions. In the second stage, we propose an improved variant of Simulated Annealing (SA), which we call Simulated Annealing with Reheating (SAR), to improve the solution quality of feasible solutions. SAR has three features: a novel neighborhood examination scheme, a new way of estimating local optima and a reheating scheme. SAR eliminates the need for extensive tuning as is often required in conventional SA. The proposed methodologies are tested on the three most studied datasets from the scientific literature. Our algorithms perform well and our results are competitive, if not better, compared to the benchmarks set by the state of the art methods. New best known results are provided for many instances.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ejores:v:261:y:2017:i:1:p:17-29
    DOI: 10.1016/j.ejor.2017.01.040
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    References listed on IDEAS

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    1. Sabar, Nasser R. & Ayob, Masri & Kendall, Graham & Qu, Rong, 2012. "A honey-bee mating optimization algorithm for educational timetabling problems," European Journal of Operational Research, Elsevier, vol. 216(3), pages 533-543.
    2. de Werra, D., 1985. "An introduction to timetabling," European Journal of Operational Research, Elsevier, vol. 19(2), pages 151-162, February.
    3. 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.
    4. 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.
    5. 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.
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    Cited by:

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    3. 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.
    4. Swan, Jerry & Adriaensen, Steven & Brownlee, Alexander E.I. & Hammond, Kevin & Johnson, Colin G. & Kheiri, Ahmed & Krawiec, Faustyna & Merelo, J.J. & Minku, Leandro L. & Özcan, Ender & Pappa, Gisele L, 2022. "Metaheuristics “In the Large”," European Journal of Operational Research, Elsevier, vol. 297(2), pages 393-406.
    5. 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.
    6. 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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