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Linear combinations of heuristics for examination timetabling

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  • Edmund Burke
  • Nam Pham
  • Rong Qu
  • Jay Yellen

Abstract

Although they are simple techniques from the early days of timetabling research, graph colouring heuristics are still attracting significant research interest in the timetabling research community. These heuristics involve simple ordering strategies to first select and colour those vertices that are most likely to cause trouble if deferred until later. Most of this work used a single heuristic to measure the difficulty of a vertex. Relatively less attention has been paid to select an appropriate colour for the selected vertex. Some recent work has demonstrated the superiority of combining a number of different heuristics for vertex and colour selection. In this paper, we explore this direction and introduce a new strategy of using linear combinations of heuristics for weighted graphs which model the timetabling problems under consideration. The weights of the heuristic combinations define specific roles that each simple heuristic contributes to the process of ordering vertices. We include specific explanations for the design of our strategy and present the experimental results on a set of benchmark real world examination timetabling problem instances. New best results for several instances have been obtained using this method when compared with other constructive methods applied to this benchmark dataset. Copyright Springer Science+Business Media, LLC 2012

Suggested Citation

  • Edmund Burke & Nam Pham & Rong Qu & Jay Yellen, 2012. "Linear combinations of heuristics for examination timetabling," Annals of Operations Research, Springer, vol. 194(1), pages 89-109, April.
  • Handle: RePEc:spr:annopr:v:194:y:2012:i:1:p:89-109:10.1007/s10479-011-0854-y
    DOI: 10.1007/s10479-011-0854-y
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    References listed on IDEAS

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    1. Nonobe, Koji & Ibaraki, Toshihide, 1998. "A tabu search approach to the constraint satisfaction problem as a general problem solver," European Journal of Operational Research, Elsevier, vol. 106(2-3), pages 599-623, April.
    2. Nirbhay K. Mehta, 1981. "The Application of a Graph Coloring Method to an Examination Scheduling Problem," Interfaces, INFORMS, vol. 11(5), pages 57-65, October.
    3. Burke, E.K. & Eckersley, A.J. & McCollum, B. & Petrovic, S. & Qu, R., 2010. "Hybrid variable neighbourhood approaches to university exam timetabling," European Journal of Operational Research, Elsevier, vol. 206(1), pages 46-53, October.
    4. Krarup, J. & de Werra, D., 1982. "Chromatic optimisation: Limitations, objectives, uses, references," European Journal of Operational Research, Elsevier, vol. 11(1), pages 1-19, September.
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    Cited by:

    1. Álvaro García-Sánchez & Araceli Hernández & Eduardo Caro & Gonzalo Jiménez, 2019. "Universidad Politécnica de Madrid Uses Integer Programming for Scheduling Weekly Assessment Activities," Interfaces, INFORMS, vol. 49(2), pages 104-116, March.
    2. Seizinger, Markus & Brunner, Jens O., 2023. "Optimized planning of nursing curricula in dual vocational schools focusing on the German health care system," European Journal of Operational Research, Elsevier, vol. 304(3), pages 1223-1241.
    3. Johnes, Jill, 2015. "Operational Research in education," European Journal of Operational Research, Elsevier, vol. 243(3), pages 683-696.
    4. Alejandro Cataldo & Juan-Carlos Ferrer & Jaime Miranda & Pablo A. Rey & Antoine Sauré, 2017. "An integer programming approach to curriculum-based examination timetabling," Annals of Operations Research, Springer, vol. 258(2), pages 369-393, November.
    5. T. Godwin, 2022. "Obtaining quality business school examination timetable under heterogeneous elective selections through surrogacy," OPSEARCH, Springer;Operational Research Society of India, vol. 59(3), pages 1055-1093, September.

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