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An Indirect Genetic Algorithm for a Nurse Scheduling Problem

Aickelin, Uwe; Dowsland, Kathryn

Authors

Uwe Aickelin

Kathryn Dowsland



Abstract

This paper describes a Genetic Algorithms approach to a manpower-scheduling problem arising at a major UK hospital. Although Genetic Algorithms have been successfully used for similar problems in the past, they always had to overcome the limitations of the classical Genetic Algorithms paradigm in handling the conflict between objectives and constraints. The approach taken here is to use an indirect coding based on permutations of the nurses, and a heuristic decoder that builds schedules from these permutations. Computational experiments based on 52 weeks of live data are used to evaluate three different decoders with varying levels of intelligence, and four well-known crossover operators. Results are further enhanced by introducing a hybrid crossover operator and by making use of simple bounds to reduce the size of the solution space. The results reveal that the proposed algorithm is able to find high quality solutions and is both faster and more flexible than a recently published Tabu Search approach.

Citation

Aickelin, U., & Dowsland, K. (2004). An Indirect Genetic Algorithm for a Nurse Scheduling Problem. Computers and Operations Research, 31(5), doi:10.1016/S0305-0548(03)00034-0

Journal Article Type Article
Publication Date Jan 1, 2004
Deposit Date Oct 30, 2007
Publicly Available Date Oct 30, 2007
Journal Computers & Operations Research
Electronic ISSN 0305-0548
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 31
Issue 5
DOI https://doi.org/10.1016/S0305-0548%2803%2900034-0
Keywords Genetic Algorithms, Heuristics, Manpower Scheduling
Public URL http://eprints.nottingham.ac.uk/id/eprint/661
Publisher URL http://www.elsevier.com/wps/find/journaldescription.cws_home/300/description#description
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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