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Effective learning hyper-heuristics for the course timetabling problem

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  • Soria-Alcaraz, Jorge A.
  • Ochoa, Gabriela
  • Swan, Jerry
  • Carpio, Martin
  • Puga, Hector
  • Burke, Edmund K.

Abstract

Course timetabling is an important and recurring administrative activity in most educational institutions. This article combines a general modeling methodology with effective learning hyper-heuristics to solve this problem. The proposed hyper-heuristics are based on an iterated local search procedure that autonomously combines a set of move operators. Two types of learning for operator selection are contrasted: a static (offline) approach, with a clear distinction between training and execution phases; and a dynamic approach that learns on the fly. The resulting algorithms are tested over the set of real-world instances collected by the first and second International Timetabling competitions. The dynamic scheme statistically outperforms the static counterpart, and produces competitive results when compared to the state-of-the-art, even producing a new best-known solution. Importantly, our study illustrates that algorithms with increased autonomy and generality can outperform human designed problem-specific algorithms.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:ejores:v:238:y:2014:i:1:p:77-86
    DOI: 10.1016/j.ejor.2014.03.046
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    References listed on IDEAS

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    1. 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.
    2. Burke, Edmund K. & Curtois, Tim, 2014. "New approaches to nurse rostering benchmark instances," European Journal of Operational Research, Elsevier, vol. 237(1), pages 71-81.
    3. Michel Gendreau & Jean-Yves Potvin (ed.), 2010. "Handbook of Metaheuristics," International Series in Operations Research and Management Science, Springer, number 978-1-4419-1665-5, December.
    4. 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.
    5. 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.
    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. 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. 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. 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.
    10. 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:

    1. W. B. Yates & E. C. Keedwell, 2019. "An analysis of heuristic subsequences for offline hyper-heuristic learning," Journal of Heuristics, Springer, vol. 25(3), pages 399-430, June.
    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. Johnes, Jill, 2015. "Operational Research in education," European Journal of Operational Research, Elsevier, vol. 243(3), pages 683-696.
    4. Nelishia Pillay, 2016. "A review of hyper-heuristics for educational timetabling," Annals of Operations Research, Springer, vol. 239(1), pages 3-38, April.
    5. Kheiri, Ahmed & Özcan, Ender, 2016. "An iterated multi-stage selection hyper-heuristic," European Journal of Operational Research, Elsevier, vol. 250(1), pages 77-90.
    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. Felipe Rosa-Rivera & Jose I. Nunez-Varela & Cesar A. Puente-Montejano & Sandra E. Nava-Muñoz, 2021. "Measuring the complexity of university timetabling instances," Journal of Scheduling, Springer, vol. 24(1), pages 103-121, February.
    8. Zhang, Yuchang & Bai, Ruibin & Qu, Rong & Tu, Chaofan & Jin, Jiahuan, 2022. "A deep reinforcement learning based hyper-heuristic for combinatorial optimisation with uncertainties," European Journal of Operational Research, Elsevier, vol. 300(2), pages 418-427.

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