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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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    1. Nagar, Amit & Haddock, Jorge & Heragu, Sunderesh, 1995. "Multiple and bicriteria scheduling: A literature survey," European Journal of Operational Research, Elsevier, vol. 81(1), pages 88-104, February.
    2. 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.
    3. Pongcharoen, P. & Hicks, C. & Braiden, P. M. & Stewardson, D. J., 2002. "Determining optimum Genetic Algorithm parameters for scheduling the manufacturing and assembly of complex products," International Journal of Production Economics, Elsevier, vol. 78(3), pages 311-322, August.
    4. Pongcharoen, P. & Hicks, C. & Braiden, P. M., 2004. "The development of genetic algorithms for the finite capacity scheduling of complex products, with multiple levels of product structure," European Journal of Operational Research, Elsevier, vol. 152(1), pages 215-225, January.
    5. Drexl, Andreas & Salewski, Frank, 1997. "Distribution requirements and compactness constraints in school timetabling," European Journal of Operational Research, Elsevier, vol. 102(1), pages 193-214, October.
    6. P. Pongcharoen & D. J. Stewardson & C. Hicks & P. M. Braiden, 2001. "Applying designed experiments to optimize the performance of genetic algorithms used for scheduling complex products in the capital goods industry," Journal of Applied Statistics, Taylor & Francis Journals, vol. 28(3-4), pages 441-455.
    7. Asratian, A. S. & de Werra, D., 2002. "A generalized class-teacher model for some timetabling problems," European Journal of Operational Research, Elsevier, vol. 143(3), pages 531-542, December.
    8. Costa, Daniel, 1994. "A tabu search algorithm for computing an operational timetable," European Journal of Operational Research, Elsevier, vol. 76(1), pages 98-110, July.
    9. 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.
    10. Daskalaki, S. & Birbas, T., 2005. "Efficient solutions for a university timetabling problem through integer programming," European Journal of Operational Research, Elsevier, vol. 160(1), pages 106-120, January.
    11. Triki, E. & Collette, Y. & Siarry, P., 2005. "A theoretical study on the behavior of simulated annealing leading to a new cooling schedule," European Journal of Operational Research, Elsevier, vol. 166(1), pages 77-92, October.
    12. Alvarez-Valdes, Ramon & Crespo, Enric & Tamarit, Jose M., 2002. "Design and implementation of a course scheduling system using Tabu Search," European Journal of Operational Research, Elsevier, vol. 137(3), pages 512-523, March.
    13. Kolonko, M., 1999. "Some new results on simulated annealing applied to the job shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 113(1), pages 123-136, February.
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    Cited by:

    1. Jaime Miranda, 2010. "eClasSkeduler: A Course Scheduling System for the Executive Education Unit at the Universidad de Chile," Interfaces, INFORMS, vol. 40(3), pages 196-207, June.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. R. Alan Bowman, 2021. "Developing Optimal Student Plans of Study," Interfaces, INFORMS, vol. 51(6), pages 409-421, November.

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