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A multiobjective framework for heavily constrained examination timetabling problems

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  • Christine Mumford

Abstract

University examination timetabling is a challenging set partitioning problem that comes in many variations, and real world applications usually carry multiple constraints and require the simultaneous optimization of several (often conflicting) objectives. This paper presents a multiobjective framework capable of solving heavily constrained timetabling problems. In this prototype study, we focus on the two objectives: minimizing timetable length while simultaneously optimizing the spread of examinations for individual students. Candidate solutions are presented to a multiobjective memetic algorithm as orderings of examinations, and a greedy algorithm is used to construct violation free timetables from permutation sequences of exams. The role of the multiobjective algorithm is to iteratively improve a population of orderings, with respect to the given objectives, using various mutation and reordering heuristics. Copyright Springer Science+Business Media, LLC 2010

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  • Christine Mumford, 2010. "A multiobjective framework for heavily constrained examination timetabling problems," Annals of Operations Research, Springer, vol. 180(1), pages 3-31, November.
    • Handle: RePEc:spr:annopr:v:180:y:2010:i:1:p:3-31:10.1007/s10479-008-0490-3
    DOI: 10.1007/s10479-008-0490-3
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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. & 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.
    3. 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.
    4. 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. Johnes, Jill, 2015. "Operational Research in education," European Journal of Operational Research, Elsevier, vol. 243(3), pages 683-696.
    2. 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.
    3. Gülcin Ermis & Can Akkan, 2019. "Search algorithms for improving the pareto front in a timetabling problem with a solution network-based robustness measure," Annals of Operations Research, Springer, vol. 275(1), pages 101-121, April.
    4. T. Godwin, 2022. "Obtaining quality business school examination timetable under heterogeneous elective selections through surrogacy," OPSEARCH, Springer;Operational Research Society of India, vol. 59(3), pages 1055-1093, September.

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