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BOA for nurse scheduling

Li, Jingpeng; Aickelin, Uwe

Authors

Jingpeng Li

Uwe Aickelin



Contributors

Martin Pelikan
Editor

Kumara Sastry
Editor

Erick Cant�-Paz
Editor

Abstract

Our research has shown that schedules can be built mimicking a human scheduler by using a set of rules that involve domain knowledge. This chapter presents a Bayesian Optimization Algorithm (BOA)for the nurse scheduling problem that chooses such suitable scheduling rules from a set for each nurse’s assignment.
Based on the idea of using probabilistic models, the BOA builds a Bayesian network for the set of promising solutions and samples these networks to generate new candidate solutions. Computational results from 52 real data instances demonstrate the success of this approach. It is also suggested that the learning mechanism in the proposed algorithm may be suitable for other scheduling problems.

Citation

Li, J., & Aickelin, U. (2006). BOA for nurse scheduling. In M. Pelikan, K. Sastry, & E. Cantú-Paz (Eds.), Scalable optimization via probabilistic modeling: from algorithms to applications. Springer

Publication Date Jan 1, 2006
Deposit Date Aug 10, 2011
Publicly Available Date Aug 10, 2011
Peer Reviewed Peer Reviewed
Volume 33
Issue 33
Series Title Studies in computational intelligence
Book Title Scalable optimization via probabilistic modeling: from algorithms to applications
ISBN 9783540349532
Public URL https://nottingham-repository.worktribe.com/output/1019208

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