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A review of hyper-heuristics for educational timetabling

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  • Nelishia Pillay

    (University of KwaZulu-Natal)

Abstract

Educational timetabling problems, namely, university examination timetabling, university course timetabling and school timetabling, are combinatorial optimization problems requiring the allocation of resources so as to satisfy a specified set of constraints. Hyper-heuristics have been successfully applied to a variety of combinatorial optimization problems. This is a rapidly growing field which aims at providing generalized solutions to combinatorial optimization problems by exploring a heuristic space instead of a solution space. From the research conducted thus far it is evident that hyper-heuristics are effective at solving educational timetabling problems and have the potential of advancing this field by providing a generalized solution to educational timetabling as a whole. Given this, the paper provides an overview and critical analysis of hyper-heuristics for educational timetabling and proposes future research directions, focusing on using hyper-heuristics to provide a generalized solution to educational timetabling.

Suggested Citation

  • Nelishia Pillay, 2016. "A review of hyper-heuristics for educational timetabling," Annals of Operations Research, Springer, vol. 239(1), pages 3-38, April.
    • Handle: RePEc:spr:annopr:v:239:y:2016:i:1:d:10.1007_s10479-014-1688-1
    DOI: 10.1007/s10479-014-1688-1
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    References listed on IDEAS

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    1. N Pillay, 2012. "Evolving hyper-heuristics for the uncapacitated examination timetabling problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 63(1), pages 47-58, January.
    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. 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.
    4. 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.
    5. Soria-Alcaraz, Jorge A. & Ochoa, Gabriela & Swan, Jerry & Carpio, Martin & Puga, Hector & Burke, Edmund K., 2014. "Effective learning hyper-heuristics for the course timetabling problem," European Journal of Operational Research, Elsevier, vol. 238(1), pages 77-86.
    6. Edmund K Burke & Michel Gendreau & Matthew Hyde & Graham Kendall & Gabriela Ochoa & Ender Özcan & Rong Qu, 2013. "Hyper-heuristics: a survey of the state of the art," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 64(12), pages 1695-1724, December.
    7. 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.
    8. 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.
    9. 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.
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    Cited by:

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    2. Drake, John H. & Kheiri, Ahmed & Özcan, Ender & Burke, Edmund K., 2020. "Recent advances in selection hyper-heuristics," European Journal of Operational Research, Elsevier, vol. 285(2), pages 405-428.
    3. P. Solano Cutillas & D. Pérez-Perales & M. M. E. Alemany Díaz, 2022. "A mathematical programming tool for an efficient decision-making on teaching assignment under non-regular time schedules," Operational Research, Springer, vol. 22(3), pages 2899-2942, July.
    4. 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.
    5. Aslan, Ayse & Bakir, Ilke & Vis, Iris F.A., 2020. "A dynamic thompson sampling hyper-heuristic framework for learning activity planning in personalized learning," European Journal of Operational Research, Elsevier, vol. 286(2), pages 673-688.
    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.
    7. Zhenyuan Liu & Jiongbing Lu & Zaisheng Liu & Guangrui Liao & Hao Howard Zhang & Junwu Dong, 2019. "Patient scheduling in hemodialysis service," Journal of Combinatorial Optimization, Springer, vol. 37(1), pages 337-362, January.

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