An Indirect Genetic Algorithm for a Nurse Scheduling Problem

Computers & Operations Research, 31(5), pp 761-778, 2004

25 Pages Posted: 17 Aug 2016

See all articles by Uwe Aickelin

Uwe Aickelin

University of Melbourne - School of Computing and Information Systems

Kathryn A. Dowsland

University of Wales, Swansea - School of Business and Economics

Date Written: January 1, 2004

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.

Keywords: Genetic Algorithms, Heuristics, Manpower Scheduling

Suggested Citation

Aickelin, Uwe and Dowsland, Kathryn A., An Indirect Genetic Algorithm for a Nurse Scheduling Problem (January 1, 2004). Computers & Operations Research, 31(5), pp 761-778, 2004, Available at SSRN: https://ssrn.com/abstract=2824144

Uwe Aickelin (Contact Author)

University of Melbourne - School of Computing and Information Systems ( email )

Australia

Kathryn A. Dowsland

University of Wales, Swansea - School of Business and Economics ( email )

Singleton Park
Swansea, Wales SA2 8PP
United Kingdom

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