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Hybrid variable neighbourhood approaches to university exam timetabling

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  • Burke, E.K.
  • Eckersley, A.J.
  • McCollum, B.
  • Petrovic, S.
  • Qu, R.

Abstract

In this paper, we investigate variable neighbourhood search (VNS) approaches for the university examination timetabling problem. In addition to a basic VNS method, we introduce variants of the technique with different initialisation methods including a biased VNS and its hybridisation with a Genetic Algorithm. A number of different neighbourhood structures are analysed. It is demonstrated that the proposed technique is able to produce high quality solutions across a wide range of benchmark problem instances. In particular, we demonstrate that the Genetic Algorithm, which intelligently selects appropriate neighbourhoods to use within the biased VNS, produces the best known results in the literature, in terms of solution quality, on some of the benchmark instances. However, it requires relatively large amount of computational time. Possible extensions to this overall approach are also discussed.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ejores:v:206:y:2010:i:1:p:46-53
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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. 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.
    3. Hansen, Pierre & Mladenovic, Nenad, 2001. "Variable neighborhood search: Principles and applications," European Journal of Operational Research, Elsevier, vol. 130(3), pages 449-467, May.
    4. 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.
    5. White, George M. & Xie, Bill S. & Zonjic, Stevan, 2004. "Using tabu search with longer-term memory and relaxation to create examination timetables," European Journal of Operational Research, Elsevier, vol. 153(1), pages 80-91, February.
    6. 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.
    7. 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.
    8. 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. Edmund Burke & Nam Pham & Rong Qu & Jay Yellen, 2012. "Linear combinations of heuristics for examination timetabling," Annals of Operations Research, Springer, vol. 194(1), pages 89-109, April.
    2. Li, Jingpeng & Bai, Ruibin & Shen, Yindong & Qu, Rong, 2015. "Search with evolutionary ruin and stochastic rebuild: A theoretic framework and a case study on exam timetabling," European Journal of Operational Research, Elsevier, vol. 242(3), pages 798-806.
    3. Álvaro García-Sánchez & Araceli Hernández & Eduardo Caro & Gonzalo Jiménez, 2019. "Universidad Politécnica de Madrid Uses Integer Programming for Scheduling Weekly Assessment Activities," Interfaces, INFORMS, vol. 49(2), pages 104-116, March.
    4. Moritz Mühlenthaler & Rolf Wanka, 2016. "Fairness in academic course timetabling," Annals of Operations Research, Springer, vol. 239(1), pages 171-188, April.
    5. Johnes, Jill, 2015. "Operational Research in education," European Journal of Operational Research, Elsevier, vol. 243(3), pages 683-696.
    6. Wilson, Duncan T. & Hawe, Glenn I. & Coates, Graham & Crouch, Roger S., 2013. "A multi-objective combinatorial model of casualty processing in major incident response," European Journal of Operational Research, Elsevier, vol. 230(3), pages 643-655.
    7. Sabar, Nasser R. & Ayob, Masri & Kendall, Graham & Qu, Rong, 2012. "A honey-bee mating optimization algorithm for educational timetabling problems," European Journal of Operational Research, Elsevier, vol. 216(3), pages 533-543.
    8. 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.
    9. 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.
    10. 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.
    11. Edmund Burke & Rong Qu & Amr Soghier, 2014. "Adaptive selection of heuristics for improving exam timetables," Annals of Operations Research, Springer, vol. 218(1), pages 129-145, 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. Fonseca, George H.G. & Santos, Haroldo G. & Carrano, Eduardo G. & Stidsen, Thomas J.R., 2017. "Integer programming techniques for educational timetabling," European Journal of Operational Research, Elsevier, vol. 262(1), pages 28-39.
    14. Eng, KaiLun & Muhammed, Abdullah & Mohamed, Mohamad Afendee & Hasan, Sazlinah, 2020. "A hybrid heuristic of Variable Neighbourhood Descent and Great Deluge algorithm for efficient task scheduling in Grid computing," European Journal of Operational Research, Elsevier, vol. 284(1), pages 75-86.
    15. De Boeck, Liesje & Beliën, Jeroen & Creemers, Stefan, 2016. "A column generation approach for solving the examination-timetabling problemAuthor-Name: Woumans, Gert," European Journal of Operational Research, Elsevier, vol. 253(1), pages 178-194.
    16. R. A. Oude Vrielink & E. A. Jansen & E. W. Hans & J. Hillegersberg, 2019. "Practices in timetabling in higher education institutions: a systematic review," Annals of Operations Research, Springer, vol. 275(1), pages 145-160, April.
    17. Efstratios Rappos & Eric Thiémard & Stephan Robert & Jean-François Hêche, 2022. "A mixed-integer programming approach for solving university course timetabling problems," Journal of Scheduling, Springer, vol. 25(4), pages 391-404, August.
    18. 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.
    19. 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.
    20. 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.
    21. Turabieh, Hamza & Abdullah, Salwani, 2011. "An integrated hybrid approach to the examination timetabling problem," Omega, Elsevier, vol. 39(6), pages 598-607, December.

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