Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/15820
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Linear combinations of heuristics for examination timetabling
Author(s): Burke, Edmund
Pham, Nam
Qu, Rong
Yellen, Jay
Contact Email: e.k.burke@stir.ac.uk
Keywords: Examination timetabling
Weighted graph
Constructive heuristics
Heuristic combinations
Graph colouring heuristics
Issue Date: Apr-2012
Date Deposited: 8-Jul-2013
Citation: Burke E, Pham N, Qu R & Yellen J (2012) Linear combinations of heuristics for examination timetabling. Annals of Operations Research, 194 (1), pp. 89-109. https://doi.org/10.1007/s10479-011-0854-y
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.
DOI Link: 10.1007/s10479-011-0854-y
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