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Hybrid variable neighbourhood approaches to university exam timetabling (2006)
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@TECHREPORT{Burke06hybridvariable,
author = {E. K. Burke and A. J. Eckersley and B. Mccollum and S. Petrovic and R. Qu},
title = {Hybrid variable neighbourhood approaches to university exam timetabling},
institution = {},
year = {2006}
}
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Abstract
Abstract. In this paper, we investigate variable neighbourhood search (VNS) approaches for the university examination timetabling problem. In addition to a basic VNS method, we introduce variants of the technique with different initialisation methods including a biased VNS and its hybridisation with a Genetic Algorithm. A number of different neighbourhood structures are analysed. It is demonstrated that the proposed technique is able to produce high quality solutions across a wide range of benchmark problem instances. In particular, we demonstrate that the Genetic Algorithm, which intelligently selects approporiate neighbourhoods to use within the biased VNS produces the best known results in the literature, in terms of solution quality, on some of the the benchmark instances, although it requires relatively large amount of computational time. Possible extensions to this overall approach are also discussed. 1
Keyphrases
hybrid variable neighbourhood approach university exam genetic algorithm biased vns known result wide range high quality solution benchmark problem instance overall approach different neighbourhood structure benchmark instance computational time possible extension variable neighbourhood search large amount basic vns method approporiate neighbourhood university examination solution quality different initialisation method
