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Memetic techniques for examination timetabling

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  • Mohammed Al-Betar
  • Ahamad Khader
  • Iyad Doush

Abstract

In this paper, we investigate the effectiveness of combining the main components of the memetic algorithms (MAs) on the quality of solutions produced for Uncapacitated Examination Timetabling Problem (UETP). These components are recombination, randomness, and neighbourhood structures. The Harmony Search Algorithm (HSA), which is a variation of MA, is used to perform different combinations of these components. It has three main components: Memory Consideration using the recombination, Random Consideration using the randomness and Pitch Adjustment using the neighbourhood structures (or local search). The combinations among MA components are evaluated using 17 different scenarios each of which reflects a combination of one, two or three components. The results show that the system that combines the three components (recombination, randomness, and neighbourhood structures) provides the best results. Furthermore, the best results obtained from the convergence scenarios were compared with 22 other methods that used a de facto dataset defined by Carter et al. (in Journal of the Operational Research Society 74:373–383, 1996 ) for UETP. The results exceed those produced by the previous methods in 2 out of 12 datasets. Copyright Springer Science+Business Media New York 2014

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  • 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.
    • Handle: RePEc:spr:annopr:v:218:y:2014:i:1:p:23-50:10.1007/s10479-013-1500-7
    DOI: 10.1007/s10479-013-1500-7
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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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. Mohammed Al-Betar & Ahamad Khader, 2012. "A harmony search algorithm for university course timetabling," Annals of Operations Research, Springer, vol. 194(1), pages 3-31, April.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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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    3. 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.
    4. Lahasan, Badr Mohammed & Venkat, Ibrahim & Al-Betar, Mohammed Azmi & Lutfi, Syaheerah Lebai & Wilde, Philippe De, 2016. "Recognizing faces prone to occlusions and common variations using optimal face subgraphs," Applied Mathematics and Computation, Elsevier, vol. 283(C), pages 316-332.

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