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A hybrid evolutionary approach to the nurse rostering problem

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


Ruibin Bai

Edmund K. Burke

Graham Kendall

Jingpeng Li

Barry McCollum


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.


Bai, R., Burke, E. K., Kendall, G., Li, J., & McCollum, B. (2010). A hybrid evolutionary approach to the nurse rostering problem. IEEE Transactions on Evolutionary Computation, 14(4),

Journal Article Type Article
Acceptance Date Jan 1, 2010
Online Publication Date Jul 29, 2010
Publication Date Aug 31, 2010
Deposit Date Nov 2, 2017
Publicly Available Date Nov 2, 2017
Journal IEEE Transactions on Evolutionary Computation
Print ISSN 1089-778X
Electronic ISSN 1089-778X
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 14
Issue 4
Keywords Constrained optimization; constraint handling; evolutionary algorithm; local search; nurse rostering; simulated annealing hyper-heuristics
Public URL
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Copyright Statement Copyright information regarding this work can be found at the following address:
Additional Information (c) 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.


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Copyright Statement
Copyright information regarding this work can be found at the following address:

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