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Solving Examination Timetabling Problems through Adaption of Heuristic Orderings

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  • E.K. Burke
  • J.P. Newall

Abstract

Heuristic ordering based methods, very similar to those used for graph colouring problems, have long been applied successfully to the examination timetabling problem. Despite the success of these methods on real life problems, even with limited computing resources, the approach has the fundamental flaw that it is only as effective as the heuristic that is used. We present a method that adapts to suit a particular problem instance “on the fly.” This method provides an alternative to existing forms of ‘backtracking,’ which are often required to cope with the possible unsuitability of a heuristic. We present a range of experiments on benchmark problems to test and evaluate the approach. In comparison to other published approaches to solving this problem, the adaptive method is more general, significantly quicker and easier to implement and produces results that are at least comparable (if not better) than the current state of the art. We also demonstrate the level of generality of this approach by starting it with the inverse of a known good heuristic, a null ordering and random orderings, showing that the adaptive method can transform a bad heuristic ordering into a good one. Copyright Kluwer Academic Publishers 2004

Suggested Citation

  • 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.
  • Handle: RePEc:spr:annopr:v:129:y:2004:i:1:p:107-134:10.1023/b:anor.0000030684.30824.08
    DOI: 10.1023/B:ANOR.0000030684.30824.08
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    Citations

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    Cited by:

    1. Edmund Burke & Jingpeng Li & Rong Qu, 2012. "A Pareto-based search methodology for multi-objective nurse scheduling," Annals of Operations Research, Springer, vol. 196(1), pages 91-109, July.
    2. Pillay, N. & Banzhaf, W., 2009. "A study of heuristic combinations for hyper-heuristic systems for the uncapacitated examination timetabling problem," European Journal of Operational Research, Elsevier, vol. 197(2), pages 482-491, September.
    3. Syariza Abdul-Rahman & Edmund Burke & Andrzej Bargiela & Barry McCollum & Ender Özcan, 2014. "A constructive approach to examination timetabling based on adaptive decomposition and ordering," Annals of Operations Research, Springer, vol. 218(1), pages 3-21, July.
    4. G N Beligiannis & C Moschopoulos & S D Likothanassis, 2009. "A genetic algorithm approach to school timetabling," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(1), pages 23-42, January.
    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. Qu, Rong & Burke, Edmund K. & McCollum, Barry, 2009. "Adaptive automated construction of hybrid heuristics for exam timetabling and graph colouring problems," European Journal of Operational Research, Elsevier, vol. 198(2), pages 392-404, October.
    7. Christine Mumford, 2010. "A multiobjective framework for heavily constrained examination timetabling problems," Annals of Operations Research, Springer, vol. 180(1), pages 3-31, November.
    8. Burke, E.K. & Eckersley, A.J. & McCollum, B. & Petrovic, S. & Qu, R., 2010. "Hybrid variable neighbourhood approaches to university exam timetabling," European Journal of Operational Research, Elsevier, vol. 206(1), pages 46-53, October.
    9. Johnes, Jill, 2015. "Operational Research in education," European Journal of Operational Research, Elsevier, vol. 243(3), pages 683-696.
    10. Nelishia Pillay, 2016. "A review of hyper-heuristics for educational timetabling," Annals of Operations Research, Springer, vol. 239(1), pages 3-38, April.
    11. 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.
    12. Edmund K. Burke & Yuri Bykov, 2016. "An Adaptive Flex-Deluge Approach to University Exam Timetabling," INFORMS Journal on Computing, INFORMS, vol. 28(4), pages 781-794, November.
    13. R Qu & E K Burke, 2009. "Hybridizations within a graph-based hyper-heuristic framework for university timetabling problems," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(9), pages 1273-1285, September.
    14. Christos Gogos & Panayiotis Alefragis & Efthymios Housos, 2012. "An improved multi-staged algorithmic process for the solution of the examination timetabling problem," Annals of Operations Research, Springer, vol. 194(1), pages 203-221, April.
    15. Taha Arbaoui & Jean-Paul Boufflet & Aziz Moukrim, 2015. "Preprocessing and an improved MIP model for examination timetabling," Annals of Operations Research, Springer, vol. 229(1), pages 19-40, June.
    16. Zhang, Defu & Liu, Yongkai & M'Hallah, Rym & Leung, Stephen C.H., 2010. "A simulated annealing with a new neighborhood structure based algorithm for high school timetabling problems," European Journal of Operational Research, Elsevier, vol. 203(3), pages 550-558, June.
    17. 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.
    18. 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.
    19. 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.
    20. Massimiliano Caramia & Paolo Dell’Olmo, 2007. "Coupling Stochastic and Deterministic Local Search in Examination Timetabling," Operations Research, INFORMS, vol. 55(2), pages 351-366, April.
    21. Barry McCollum & Paul McMullan & Andrew Parkes & Edmund Burke & Rong Qu, 2012. "A new model for automated examination timetabling," Annals of Operations Research, Springer, vol. 194(1), pages 291-315, April.
    22. Abdul Rahman, Syariza & Bargiela, Andrzej & Burke, Edmund K. & Özcan, Ender & McCollum, Barry & McMullan, Paul, 2014. "Adaptive linear combination of heuristic orderings in constructing examination timetables," European Journal of Operational Research, Elsevier, vol. 232(2), pages 287-297.

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