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Stochastic Optimisation Timetabling Tool for university course scheduling

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  • Pongcharoen, P.
  • Promtet, W.
  • Yenradee, P.
  • Hicks, C.

Abstract

University timetabling is an NP-hard problem, which means that the amount of computation required to find solutions increases exponentially with problem size. Timetabling is subject to hard constraints that must be satisfied in order to produce feasible timetables and soft constraints, which are not absolutely essential. This paper describes the Stochastic Optimisation Timetabling Tool (SOTT) that has been developed for university course timetabling. Genetic Algorithms (GA), Simulated Annealing (SA) and random search are embedded in the SOTT. The algorithms include a repair process, which ensures that all infeasible timetables are rectified. This prevents clashes and ensures that the rooms are sufficiently large to accommodate the classes. The algorithms also evaluate timetables in terms of soft constraints: minimising student movement; avoiding fragmentation in the timetables for students and lecturers; and satisfying lecturers' preferences for the timing of classes. The algorithms were tested using two sets of timetabling data from a collaborating university. Both GA and SA produced very good timetables, but the results obtained from SA were slightly better than those using GA. However, the GA was 54% faster than SA.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:proeco:v:112:y:2008:i:2:p:903-918
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    References listed on IDEAS

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    Cited by:

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    2. Vermuyten, Hendrik & Lemmens, Stef & Marques, Inês & Beliën, Jeroen, 2016. "Developing compact course timetables with optimized student flows," European Journal of Operational Research, Elsevier, vol. 251(2), pages 651-661.
    3. 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.
    4. R. Alan Bowman, 2021. "Developing Optimal Student Plans of Study," Interfaces, INFORMS, vol. 51(6), pages 409-421, November.
    5. Fabian Dunke & Stefan Nickel, 2023. "A matheuristic for customized multi-level multi-criteria university timetabling," Annals of Operations Research, Springer, vol. 328(2), pages 1313-1348, September.
    6. 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.
    7. da Cunha, Joaquim J. & de Souza, Mauricio C., 2018. "A linearized model for academic staff assignment in a Brazilian university focusing on performance gain in quality indicators," International Journal of Production Economics, Elsevier, vol. 197(C), pages 43-51.

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