A hybrid evolutionary approach to the nurse rostering problem

Bai, Ruibin and Burke, Edmund K. and Kendall, Graham and Li, Jingpeng and McCollum, Barry (2010) A hybrid evolutionary approach to the nurse rostering problem. IEEE Transactions on Evolutionary Computation, 14 (4). pp. 580-590. ISSN 1089-778X

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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.

Item Type: Article
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.
Keywords: Constrained optimization; constraint handling; evolutionary algorithm; local search; nurse rostering; simulated annealing hyper-heuristics
Schools/Departments: University of Nottingham Ningbo China > Faculty of Science and Engineering > School of Computer Science
University of Nottingham, UK > Faculty of Science > School of Computer Science
Identification Number: 10.1109/tevc.2009.2033583
Depositing User: LIN, Zhiren
Date Deposited: 02 Nov 2017 08:33
Last Modified: 04 Nov 2017 20:12
URI: http://eprints.nottingham.ac.uk/id/eprint/47488

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