A hybrid evolutionary approach to the nurse rostering problem

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

Abstract

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.

Citation

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), https://doi.org/10.1109/tevc.2009.2033583

• 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
• DOI: https://doi.org/10.1109/tevc.2009.2033583
• Keywords: Constrained optimization; constraint handling; evolutionary algorithm; local search; nurse rostering; simulated annealing hyper-heuristics
• Public URL: http://eprints.nottingham.ac.uk/id/eprint/47488
• Publisher URL: https://doi.org/10.1109/tevc.2009.2033583
• Copyright Statement: Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf
• 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.