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An ant colony based timetabling tool

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  • Thepphakorn, Thatchai
  • Pongcharoen, Pupong
  • Hicks, Chris

Abstract

The timetabling of lecturers, seminars, practical sessions and examinations is a core business process for academic institutions. A feasible timetable must satisfy hard constraints. An optimum timetable will additionally satisfy soft constraints, which are not absolutely essential. An Ant Colony based Timetabling (ANCOT) tool has been developed for solving timetabling problems. New variants of Ant Colony Optimisation (ACO) called the Best-Worst Ant System (BWAS) and the Best-Worst Ant Colony System (BWACS) were embedded in the ANCOT program. Local Search (LS) strategies were developed and embedded into BWAS and BWACS to enhance their efficiency and to help find the best timetable with the lowest number of soft constraint violations. Statistical tools for experimental design and analysis were adopted to investigate the factors affecting the BWAS performance. Eight benchmark problems were used for evaluating the performance. For large problems, the BWACS produced the best timetable and was better than the other ACO variants. The best proposed local search strategy enhanced the performance of both the BWAS and the BWACS by up to 74.5%, but this was at the expense of longer execution time.

Suggested Citation

  • Thepphakorn, Thatchai & Pongcharoen, Pupong & Hicks, Chris, 2014. "An ant colony based timetabling tool," International Journal of Production Economics, Elsevier, vol. 149(C), pages 131-144.
    • Handle: RePEc:eee:proeco:v:149:y:2014:i:c:p:131-144
    DOI: 10.1016/j.ijpe.2013.04.026
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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. Thapatsuwan, Peeraya & Pongcharoen, Pupong & Hicks, Chris & Chainate, Warattapop, 2012. "Development of a stochastic optimisation tool for solving the multiple container packing problems," International Journal of Production Economics, Elsevier, vol. 140(2), pages 737-748.
    3. Fred Glover, 1989. "Tabu Search---Part I," INFORMS Journal on Computing, INFORMS, vol. 1(3), pages 190-206, August.
    4. Gerald Lach & Marco Lübbecke, 2012. "Curriculum based course timetabling: new solutions to Udine benchmark instances," Annals of Operations Research, Springer, vol. 194(1), pages 255-272, April.
    5. Clemens Nothegger & Alfred Mayer & Andreas Chwatal & Günther Raidl, 2012. "Solving the post enrolment course timetabling problem by ant colony optimization," Annals of Operations Research, Springer, vol. 194(1), pages 325-339, April.
    6. Pongcharoen, P. & Promtet, W. & Yenradee, P. & Hicks, C., 2008. "Stochastic Optimisation Timetabling Tool for university course scheduling," International Journal of Production Economics, Elsevier, vol. 112(2), pages 903-918, April.
    7. Daskalaki, S. & Birbas, T. & Housos, E., 2004. "An integer programming formulation for a case study in university timetabling," European Journal of Operational Research, Elsevier, vol. 153(1), pages 117-135, February.
    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.
    9. 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.
    10. Lee, Yusin & Chen, Chuen-Yih, 2009. "A heuristic for the train pathing and timetabling problem," Transportation Research Part B: Methodological, Elsevier, vol. 43(8-9), pages 837-851, September.
    11. Edmund Burke & Jakub Mareček & Andrew Parkes & Hana Rudová, 2012. "A branch-and-cut procedure for the Udine Course Timetabling problem," Annals of Operations Research, Springer, vol. 194(1), pages 71-87, April.
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    Cited by:

    1. Vitayasak, Srisatja & Pongcharoen, Pupong & Hicks, Chris, 2017. "A tool for solving stochastic dynamic facility layout problems with stochastic demand using either a Genetic Algorithm or modified Backtracking Search Algorithm," International Journal of Production Economics, Elsevier, vol. 190(C), pages 146-157.
    2. Song, Kwonsik & Kim, Sooyoung & Park, Moonseo & Lee, Hyun-Soo, 2017. "Energy efficiency-based course timetabling for university buildings," Energy, Elsevier, vol. 139(C), pages 394-405.

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