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

✍ Scribed by Uwe Aickelin; Kathryn A. Dowsland

Publisher
Springer US
Year
2000
Tongue
English
Weight
234 KB
Volume
3
Category
Article
ISSN
1094-6136

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✦ Synopsis

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 (GA) paradigm is not well equipped to handle the con#ict between objectives and constraints that typically occur in such problems. In order to overcome this, successful implementations frequently make use of problem speci"c knowledge. This paper is concerned with the development of a GA for a nurse rostering problem at a major U.K. hospital. The structure of the constraints is used as the basis for a co-evolutionary strategy using co-operating subpopulations. Problem-speci"c knowledge is also used to de"ne a system of incentives and disincentives, and a complementary mutation operator. Empirical results based on 52 weeks of 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.

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