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Monte Carlo hyper-heuristics for examination timetabling

Author

Listed:
  • Edmund Burke
  • Graham Kendall
  • Mustafa Mısır
  • Ender Özcan

Abstract

Automating the neighbourhood selection process in an iterative approach that uses multiple heuristics is not a trivial task. Hyper-heuristics are search methodologies that not only aim to provide a general framework for solving problem instances at different difficulty levels in a given domain, but a key goal is also to extend the level of generality so that different problems from different domains can also be solved. Indeed, a major challenge is to explore how the heuristic design process might be automated. Almost all existing iterative selection hyper-heuristics performing single point search contain two successive stages; heuristic selection and move acceptance. Different operators can be used in either of the stages. Recent studies explore ways of introducing learning mechanisms into the search process for improving the performance of hyper-heuristics. In this study, a broad empirical analysis is performed comparing Monte Carlo based hyper-heuristics for solving capacitated examination timetabling problems. One of these hyper-heuristics is an approach that overlaps two stages and presents them in a single algorithmic body. A learning heuristic selection method (L) operates in harmony with a simulated annealing move acceptance method using reheating (SA) based on some shared variables. Yet, the heuristic selection and move acceptance methods can be separated as the proposed approach respects the common selection hyper-heuristic framework. The experimental results show that simulated annealing with reheating as a hyper-heuristic move acceptance method has significant potential. On the other hand, the learning hyper-heuristic using simulated annealing with reheating move acceptance (L–SA) performs poorly due to certain weaknesses, such as the choice of rewarding mechanism and the evaluation of utility values for heuristic selection as compared to some other hyper-heuristics in examination timetabling. Trials with other heuristic selection methods confirm that the best alternative for the simulated annealing with reheating move acceptance for examination timetabling is a previously proposed strategy known as the choice function. Copyright Springer Science+Business Media, LLC 2012

Suggested Citation

  • Edmund Burke & Graham Kendall & Mustafa Mısır & Ender Özcan, 2012. "Monte Carlo hyper-heuristics for examination timetabling," Annals of Operations Research, Springer, vol. 196(1), pages 73-90, July.
    • Handle: RePEc:spr:annopr:v:196:y:2012:i:1:p:73-90:10.1007/s10479-010-0782-2
    DOI: 10.1007/s10479-010-0782-2
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    References listed on IDEAS

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    1. Dowsland, Kathryn A. & Soubeiga, Eric & Burke, Edmund, 2007. "A simulated annealing based hyperheuristic for determining shipper sizes for storage and transportation," European Journal of Operational Research, Elsevier, vol. 179(3), pages 759-774, June.
    2. 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.
    3. 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.
    4. S Abdullah & S Ahmadi & E K Burke & M Dror & B McCollum, 2007. "A tabu-based large neighbourhood search methodology for the capacitated examination timetabling problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(11), pages 1494-1502, November.
    5. Massimiliano Caramia & Paolo Dell'Olmo & Giuseppe F. Italiano, 2008. "Novel Local-Search-Based Approaches to University Examination Timetabling," INFORMS Journal on Computing, INFORMS, vol. 20(1), pages 86-99, February.
    6. K A Dowsland & J M Thompson, 2005. "Ant colony optimization for the examination scheduling problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(4), pages 426-438, April.
    7. Burke, Edmund K. & McCollum, Barry & Meisels, Amnon & Petrovic, Sanja & Qu, Rong, 2007. "A graph-based hyper-heuristic for educational timetabling problems," European Journal of Operational Research, Elsevier, vol. 176(1), pages 177-192, January.
    8. E.K. Burke & J.P. Newall, 2004. "Solving Examination Timetabling Problems through Adaption of Heuristic Orderings," Annals of Operations Research, Springer, vol. 129(1), pages 107-134, July.
    9. Michael W. Carter, 1986. "OR Practice—A Survey of Practical Applications of Examination Timetabling Algorithms," Operations Research, INFORMS, vol. 34(2), pages 193-202, April.
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    Cited by:

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    2. Johnes, Jill, 2015. "Operational Research in education," European Journal of Operational Research, Elsevier, vol. 243(3), pages 683-696.
    3. Mohammed Al-Betar & Ahamad Khader & Iyad Doush, 2014. "Memetic techniques for examination timetabling," Annals of Operations Research, Springer, vol. 218(1), pages 23-50, July.
    4. Alejandro Cataldo & Juan-Carlos Ferrer & Jaime Miranda & Pablo A. Rey & Antoine Sauré, 2017. "An integer programming approach to curriculum-based examination timetabling," Annals of Operations Research, Springer, vol. 258(2), pages 369-393, November.
    5. T. Godwin, 2022. "Obtaining quality business school examination timetable under heterogeneous elective selections through surrogacy," OPSEARCH, Springer;Operational Research Society of India, vol. 59(3), pages 1055-1093, September.
    6. Kheiri, Ahmed & Özcan, Ender, 2016. "An iterated multi-stage selection hyper-heuristic," European Journal of Operational Research, Elsevier, vol. 250(1), pages 77-90.

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