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Ant colony optimization for the examination scheduling problem

Author

Listed:
  • K A Dowsland

    (Gower Optimal Algorithms Ltd. and Nottingham University)

  • J M Thompson

    (Cardiff University)

Abstract

Ant colony optimization is an evolutionary search procedure based on the way that ant colonies cooperate in locating shortest routes to food sources. Early implementations focussed on the travelling salesman and other routing problems but it is now being applied to an increasingly diverse range of combinatorial optimization problems. This paper is concerned with its application to the examination scheduling problem. It builds on an existing implementation for the graph colouring problem to produce clash-free timetables and goes on to consider the introduction of a number of additional practical constraints and objectives. A number of enhancements and modifications to the original algorithm are introduced and evaluated. Results based on real-examination scheduling problems including standard benchmark data (the Carter data set) show that the final implementation is able to compete effectively with the best-known solution approaches to the problem.

Suggested Citation

  • K A Dowsland & J M Thompson, 2005. "Ant colony optimization for the examination scheduling problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(4), pages 426-438, April.
    • Handle: RePEc:pal:jorsoc:v:56:y:2005:i:4:d:10.1057_palgrave.jors.2601830
    DOI: 10.1057/palgrave.jors.2601830
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    References listed on IDEAS

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    1. L M Gambardella & É D Taillard & M Dorigo, 1999. "Ant colonies for the quadratic assignment problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 50(2), pages 167-176, February.
    2. Balakrishnan, N, 1991. "Examination scheduling: A computerized application," Omega, Elsevier, vol. 19(1), pages 37-41.
    3. J Levine & F Ducatelle, 2004. "Ant colony optimization and local search for bin packing and cutting stock problems," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(7), pages 705-716, 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. 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.
    2. Tiago Pais & Paula Amaral, 2012. "Managing the tabu list length using a fuzzy inference system: an application to examination timetabling," Annals of Operations Research, Springer, vol. 194(1), pages 341-363, April.
    3. Johnes, Jill, 2015. "Operational Research in education," European Journal of Operational Research, Elsevier, vol. 243(3), pages 683-696.
    4. Wen, Charlie & Eksioglu, Sandra Duni & Greenwood, Allen & Zhang, Shu, 2010. "Crane scheduling in a shipbuilding environment," International Journal of Production Economics, Elsevier, vol. 124(1), pages 40-50, March.
    5. Matthew Naybour & Rasa Remenyte-Prescott & Matthew Boyd, 2024. "Ant colony optimisation of a community pharmacy dispensing process using Coloured Petri-Net simulation and UK pharmacy in-field data," Journal of Risk and Reliability, , vol. 238(1), pages 29-43, February.
    6. Massimiliano Caramia & Paolo Dell’Olmo, 2007. "Coupling Stochastic and Deterministic Local Search in Examination Timetabling," Operations Research, INFORMS, vol. 55(2), pages 351-366, April.
    7. 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.
    8. 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.
    9. 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.
    10. Martin Geiger, 2012. "Applying the threshold accepting metaheuristic to curriculum based course timetabling," Annals of Operations Research, Springer, vol. 194(1), pages 189-202, April.
    11. 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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