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Adaptive selection of heuristics for improving exam timetables

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  • Edmund Burke
  • Rong Qu
  • Amr Soghier

Abstract

This paper presents a hyper-heuristic approach which hybridises low-level heuristic moves to improve timetables. Exams which cause a soft-constraint violation in the timetable are ordered and rescheduled to produce a better timetable. It is observed that both the order in which exams are rescheduled and the heuristic moves used to reschedule the exams and improve the timetable affect the quality of the solution produced. After testing different combinations in a hybrid hyper-heuristic approach, the Kempe chain move heuristic and time-slot swapping heuristic proved to be the best heuristic moves to use in a hybridisation. Similarly, it was shown that ordering the exams using Saturation Degree and breaking any ties using Largest Weighted Degree produce the best results. Based on these observations, a methodology is developed to adaptively hybridise the Kempe chain move and timeslot swapping heuristics in two stages. In the first stage, random heuristic sequences are generated and automatically analysed. The heuristics repeated in the best sequences are fixed while the rest are kept empty. In the second stage, sequences are generated by randomly assigning heuristics to the empty positions in an attempt to find the best heuristic sequence. Finally, the generated sequences are applied to the problem. The approach is tested on the Toronto benchmark and the exam timetabling track of the second International Timetabling Competition, to evaluate its generality. The hyper-heuristic with low-level improvement heuristics approach was found to generalise well over the two different datasets and performed comparably to the state of the art approaches. Copyright Springer Science+Business Media, LLC 2014

Suggested Citation

  • 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.
  • Handle: RePEc:spr:annopr:v:218:y:2014:i:1:p:129-145:10.1007/s10479-012-1140-3
    DOI: 10.1007/s10479-012-1140-3
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    References listed on IDEAS

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    1. 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.
    2. Graham Kendall & Naimah Mohd Hussin, 2005. "An Investigation of a Tabu-Search-Based Hyper-Heuristic for Examination Timetabling," Springer Books, in: Graham Kendall & Edmund K. Burke & Sanja Petrovic & Michel Gendreau (ed.), Multidisciplinary Scheduling: Theory and Applications, pages 309-328, Springer.
    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. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
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    Cited by:

    1. 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.
    2. Johnes, Jill, 2015. "Operational Research in education," European Journal of Operational Research, Elsevier, vol. 243(3), pages 683-696.
    3. 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.
    4. 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.
    5. Michele Battistutta & Andrea Schaerf & Tommaso Urli, 2017. "Feature-based tuning of single-stage simulated annealing for examination timetabling," Annals of Operations Research, Springer, vol. 252(2), pages 239-254, May.

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