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Erschienen in: Neural Computing and Applications 3/2017

24.10.2015 | Original Article

Hybridization of harmony search with hill climbing for highly constrained nurse rostering problem

verfasst von: Mohammed A. Awadallah, Mohammed Azmi Al-Betar, Ahamad Tajudin Khader, Asaju La’aro Bolaji, Mahmud Alkoffash

Erschienen in: Neural Computing and Applications | Ausgabe 3/2017

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Abstract

This paper proposes a hybrid harmony search algorithm (HHSA) for solving the highly constrained nurse rostering problem (NRP). The NRP is a combinatorial optimization problem tackled by assigning a set of shifts to a set of nurses; each has specific skills and work contract, to a predefined rostering period according to a set of constraints. The harmony search is a metaheuristic approach, where the metaheuristics are the most successful methods for tackling this problem. In HHSA, the harmony search algorithm is hybridized with the hill climbing optimizer to empower its exploitation capability. Furthermore, the memory consideration operator of the HHSA is modified by replacing the random selection scheme with the global-best concept of particle swarm optimization to accelerate its convergence rate. The standard dataset published in the first international nurse rostering competition 2010 (INRC2010) was utilized to evaluate the proposed HHSA. Several convergence scenarios have been employed to study the effects of the two HHSA modifications. Finally, a comparative evaluation against twelve other methods that worked on the INRC2010 dataset is carried out. The experimental results show that the proposed method achieved five new best results, and 33 best published results out of 69 instances as achieved by other comparative methods.

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Metadaten
Titel
Hybridization of harmony search with hill climbing for highly constrained nurse rostering problem
verfasst von
Mohammed A. Awadallah
Mohammed Azmi Al-Betar
Ahamad Tajudin Khader
Asaju La’aro Bolaji
Mahmud Alkoffash
Publikationsdatum
24.10.2015
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 3/2017
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-015-2076-8

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