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Analysing the effects of solution space connectivity with an effective metaheuristic for the course timetabling problem

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  • Lewis, R.
  • Thompson, J.

Abstract

This paper provides a mathematical treatment of the NP-hard post enrolment-based course timetabling problem and presents a powerful two-stage metaheuristic-based algorithm to approximately solve it. We focus particularly on the issue of solution space connectivity and demonstrate that when this is increased via specialised neighbourhood operators, the quality of solutions achieved is generally enhanced. Across a well-known suite of benchmark problem instances, our proposed algorithm is shown to produce results that are superior to all other methods appearing in the literature; however, we also make note of those instances where our algorithm struggles in comparison to others and offer evidence as to why.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ejores:v:240:y:2015:i:3:p:637-648
    DOI: 10.1016/j.ejor.2014.07.041
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Eliashberg, J. & Hegie, Q. & Ho, J. & Huisman, D. & Miller, S.J. & Swami, S. & Weinberg, C.B. & Wierenga, B., 2007. "Demand-driven scheduling of movies in a multiplex," Econometric Institute Research Papers EI 2007-17, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    4. 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.
    5. Lü, Zhipeng & Hao, Jin-Kao, 2010. "Adaptive Tabu Search for course timetabling," European Journal of Operational Research, Elsevier, vol. 200(1), pages 235-244, January.
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    7. Rhyd Lewis, 2012. "A time-dependent metaheuristic algorithm for post enrolment-based course timetabling," Annals of Operations Research, Springer, vol. 194(1), pages 273-289, April.
    8. J. Broek & C. Hurkens, 2012. "An IP-based heuristic for the post enrolment course timetabling problem of the ITC2007," Annals of Operations Research, Springer, vol. 194(1), pages 439-454, April.
    9. Alberto Caprara & Matteo Fischetti & Paolo Toth, 2002. "Modeling and Solving the Train Timetabling Problem," Operations Research, INFORMS, vol. 50(5), pages 851-861, October.
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    Cited by:

    1. Johnes, Jill, 2015. "Operational Research in education," European Journal of Operational Research, Elsevier, vol. 243(3), pages 683-696.
    2. 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.
    3. Dhananjay Thiruvady & Rhyd Lewis & Kerri Morgan, 2020. "Tackling the maximum happy vertices problem in large networks," 4OR, Springer, vol. 18(4), pages 507-527, December.
    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. Alexander Kiefer & Richard F. Hartl & Alexander Schnell, 2017. "Adaptive large neighborhood search for the curriculum-based course timetabling problem," Annals of Operations Research, Springer, vol. 252(2), pages 255-282, May.
    6. 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.
    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.

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