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Exact/heuristic hybrids using rVNS and hyperheuristics for workforce scheduling
Remde, Stephen M. Cowling, Peter I. Dahal, Keshav P. Colledge, N.J.
Remde, Stephen M.
Cowling, Peter I.
Dahal, Keshav P.
Colledge, N.J.
Publication Date
2007
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© 2008 Springer-Verlag. Reproduced in accordance with the publisher's self-archiving policy. Original publication is available at http://www.springerlink.com
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Accepted for publication
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Abstract
In this paper we study a complex real-world workforce scheduling
problem. We propose a method of splitting the problem into smaller parts and
solving each part using exhaustive search. These smaller parts comprise a
combination of choosing a method to select a task to be scheduled and a method
to allocate resources, including time, to the selected task. We use reduced
Variable Neighbourhood Search (rVNS) and hyperheuristic approaches to
decide which sub problems to tackle. The resulting methods are compared to
local search and Genetic Algorithm approaches. Parallelisation is used to
perform nearly one CPU-year of experiments. The results show that the new
methods can produce results fitter than the Genetic Algorithm in less time and
that they are far superior to any of their component techniques. The method
used to split up the problem is generalisable and could be applied to a wide
range of optimisation problems.
Version
Accepted manuscript
Citation
Remde SM, Cowling PI, Dahal KP et al (2007)
Exact/heuristic hybrids using rVNS and hyperheuristics for workforce scheduling.
In: Evolutionary computation in combinatorial optimization. Proceedings of the 7th European Conference (EvoCOP 2007) Valencia, Spain, April 11-13, 2007: 188-197.
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Conference paper