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Bayesian Optimisation Algorithm for Nurse Scheduling, Scalable Optimization via Probabilistic Modeling

Li, Jingpeng; Aickelin, Uwe

Authors

Jingpeng Li

Uwe Aickelin



Contributors

M Pelikan
Editor

K Sastry
Editor

E Cantu-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.

Peer Reviewed Peer Reviewed
Volume Chapte
Book Title Algorithms to Applications (Studies in Computational Intelligence)
APA6 Citation Li, J., & Aickelin, U. Bayesian Optimisation Algorithm for Nurse Scheduling, Scalable Optimization via Probabilistic Modeling. In M. Pelikan, K. Sastry, & E. Cantu-Paz (Eds.), Algorithms to Applications (Studies in Computational Intelligence)Springer
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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