A hybrid evolutionary approach to the nurse rostering problem

Ruibin Bai, Edmund K. Burke, Graham Kendall, Jingpeng Li, Barry McCollum

Research output: Journal PublicationArticlepeer-review

75 Citations (Scopus)


Nurse rostering is an important search problem with many constraints. In the literature, a number of approaches have been investigated including penalty function methods to tackle these constraints within genetic algorithm frameworks. In this paper, we investigate an extension of a previously proposed stochastic ranking method, which has demonstrated superior performance to other constraint handling techniques when tested against a set of constrained optimization benchmark problems. An initial experiment on nurse rostering problems demonstrates that the stochastic ranking method is better at finding feasible solutions, but fails to obtain good results with regard to the objective function. To improve the performance of the algorithm, we hybridize it with a recently proposed simulated annealing hyper-heuristic (SAHH) within a local search and genetic algorithm framework. Computational results show that the hybrid algorithm performs better than both the genetic algorithm with stochastic ranking and the SAHH alone. The hybrid algorithm also outperforms the methods in the literature which have the previously best known results.

Original languageEnglish
Article number5532313
Pages (from-to)580-590
Number of pages11
JournalIEEE Transactions on Evolutionary Computation
Issue number4
Publication statusPublished - Aug 2010


  • Constrained optimization
  • constraint handling
  • evolutionary algorithm
  • local search
  • nurse rostering
  • simulated annealing hyper-heuristics

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Computational Theory and Mathematics


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