A Memetic Approach to the Nurse Rostering Problem

  • Authors:

  • Edmund Burke;Peter Cowling;Patrick De Causmaecker;Greet Vanden Berghe

  • Affiliations:

  • School of Computer Science & IT, University of Nottingham, Jubilee Campus, Nottingham, NG8 1BB, UK. ekb@cs.nott.ac.uk;School of Computer Science & IT, University of Nottingham, Jubilee Campus, Nottingham, NG8 1BB, UK. pic@cs.nott.ac.uk;KaHo St.-Lieven, Procestechnieken en Bedrijfsbeleid, Gebr. Desmetstraat 1, 9000 Gent, Belgium. patdc@kahosl.be;KaHo St.-Lieven, Procestechnieken en Bedrijfsbeleid, Gebr. Desmetstraat 1, 9000 Gent, Belgium. greetvb@kahosl.be

  • Venue:
  • Applied Intelligence
  • Year:
  • 2001

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Abstract

Constructing timetables of work for personnel in healthcare institutions is known to be a highly constrained and difficult problem to solve. In this paper, we discuss a commercial system, together with the model it uses, for this rostering problem. We show that tabu search heuristics can be made effective, particularly for obtaining reasonably good solutions quickly for smaller rostering problems. We discuss the robustness issues, which arise in practice, for tabu search heuristics. This paper introduces a range of new memetic approaches for the problem, which use a steepest descent improvement heuristic within a genetic algorithm framework. We provide empirical evidence to demonstrate the best features of a memetic algorithm for the rostering problem, particularly the nature of an effective recombination operator, and show that these memetic approaches can handle initialisation parameters and a range of instances more robustly than tabu search algorithms, at the expense of longer solution times. Having presented tabu search and memetic approaches (both with benefits and drawbacks) we finally present an algorithm that is a hybrid of both approaches. This technique produces better solutions than either of the earlier approaches and it is relatively unaffected by initialisation and parameter changes, combining some of the best features of each approach to create a hybrid which is greater than the sum of its component algorithms.