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'Exploiting problem structure in a genetic algorithm approach to a nurse rostering problem'

Aickelin, Uwe; Dowsland, Kathryn

Authors

Uwe Aickelin

Kathryn Dowsland



Abstract

There is considerable interest in the use of genetic algorithms to solve problems arising in the areas of scheduling and timetabling. However, the classical genetic algorithm paradigm is not well equipped to handle the conflict between objectives and constraints that typically occurs in such problems. In order to overcome this, successful implementations frequently make use of problem specific knowledge. This paper is concerned with the development of a GA for a nurse rostering problem at a major UK hospital. The structure of the constraints is used as the basis for a co-evolutionary strategy using co-operating sub-populations. Problem specific knowledge is also used to define a system of incentives and disincentives, and a complementary mutation operator. Empirical results based on 52 weeks of live data show how these features are able to improve an unsuccessful canonical GA to the point where it is able to provide a practical solution to the problem.

Citation

Aickelin, U., & Dowsland, K. (2000). 'Exploiting problem structure in a genetic algorithm approach to a nurse rostering problem'

Journal Article Type Article
Publication Date Jan 1, 2000
Deposit Date Oct 24, 2007
Publicly Available Date Oct 24, 2007
Journal Journal of Scheduling, 3 (3)
Peer Reviewed Peer Reviewed
Keywords manpower scheduling, genetic algorithms, heuristics, co-evolution
Public URL http://eprints.nottingham.ac.uk/id/eprint/665
Additional Information The original publication is available at www.springerlink.com

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