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Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: An estimation of distribution algorithm with intelligent local search for rule-based nurse rostering
Authors: Aickelin, Uwe
Burke, Edmund
Li, Jingpeng
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Keywords: nurse rostering
estimation of distribution algorithm
local search
ant colony optimization
Issue Date: Dec-2007
Publisher: Palgrave Macmillan
Citation: Aickelin U, Burke E & Li J (2007) An estimation of distribution algorithm with intelligent local search for rule-based nurse rostering, Journal of the Operational Research Society, 58 (12), pp. 1574-1585.
Abstract: This paper proposes a new memetic evolutionary algorithm to achieve explicit learning in rule-based nurse rostering, which involves applying a set of heuristic rules for each nurse's assignment. The main framework of the algorithm is an estimation of distribution algorithm, in which an ant-miner methodology improves the individual solutions produced in each generation. Unlike our previous work (where learning is implicit), the learning in the memetic estimation of distribution algorithm is explicit, that is, we are able to identify building blocks directly. The overall approach learns by building a probabilistic model, that is, an estimation of the probability distribution of individual nurse-rule pairs that are used to construct schedules. The local search processor (ie the ant-miner) reinforces nurse-rule pairs that receive higher rewards. A challenging real-world nurse rostering problem is used as the test problem. Computational results show that the proposed approach outperforms most existing approaches. It is suggested that the learning methodologies suggested in this paper may be applied to other scheduling problems where schedules are built systematically according to specific rules.
Type: Journal Article
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Affiliation: University of Nottingham
Deputy Principal's Office
Computing Science and Mathematics

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