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Integer programming for minimal perturbation problems in university course timetabling

Author

Listed:
  • Antony E. Phillips

    (The University of Auckland)

  • Cameron G. Walker

    (The University of Auckland)

  • Matthias Ehrgott

    (Lancaster University)

  • David M. Ryan

    (The University of Auckland)

Abstract

In this paper we present a general integer programming-based approach for the minimal perturbation problem in university course timetabling. This problem arises when an existing timetable contains hard constraint violations, or infeasibilities, which need to be resolved. The objective is to resolve these infeasibilities while minimising the disruption or perturbation to the remainder of the timetable. This situation commonly occurs in practical timetabling, for example when there are unexpected changes to course enrolments or available rooms. Our method attempts to resolve each infeasibility in the smallest neighbourhood possible, by utilising the exactness of integer programming. Operating within a neighbourhood of minimal size keeps the computations fast, and does not permit large movements of course events, which cause widespread disruption to timetable structure. We demonstrate the application of this method using examples based on real data from the University of Auckland.

Suggested Citation

  • Antony E. Phillips & Cameron G. Walker & Matthias Ehrgott & David M. Ryan, 2017. "Integer programming for minimal perturbation problems in university course timetabling," Annals of Operations Research, Springer, vol. 252(2), pages 283-304, May.
    • Handle: RePEc:spr:annopr:v:252:y:2017:i:2:d:10.1007_s10479-015-2094-z
    DOI: 10.1007/s10479-015-2094-z
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    References listed on IDEAS

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    1. Barry McCollum & Edmund Burke, 2014. "The practice and theory of automated timetabling," Annals of Operations Research, Springer, vol. 218(1), pages 1-2, July.
    2. Alex Bonutti & Fabio Cesco & Luca Gaspero & Andrea Schaerf, 2012. "Benchmarking curriculum-based course timetabling: formulations, data formats, instances, validation, visualization, and results," Annals of Operations Research, Springer, vol. 194(1), pages 59-70, April.
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    Cited by:

    1. Alexandre Lemos & Pedro T. Monteiro & Inês Lynce, 2021. "Disruptions in timetables: a case study at Universidade de Lisboa," Journal of Scheduling, Springer, vol. 24(1), pages 35-48, February.
    2. Letsios, Dimitrios & Mistry, Miten & Misener, Ruth, 2021. "Exact lexicographic scheduling and approximate rescheduling," European Journal of Operational Research, Elsevier, vol. 290(2), pages 469-478.
    3. Gülcü, Ayla & Akkan, Can, 2020. "Robust university course timetabling problem subject to single and multiple disruptions," European Journal of Operational Research, Elsevier, vol. 283(2), pages 630-646.
    4. Can Akkan & Ayla Gülcü & Zeki Kuş, 2022. "Bi-criteria simulated annealing for the curriculum-based course timetabling problem with robustness approximation," Journal of Scheduling, Springer, vol. 25(4), pages 477-501, August.
    5. Alexandre Lemos & Pedro T. Monteiro & Inês Lynce, 2022. "Introducing UniCorT: an iterative university course timetabling tool with MaxSAT," Journal of Scheduling, Springer, vol. 25(4), pages 371-390, August.

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