Computer Science – Neural and Evolutionary Computing
Scientific paper
2008-04-03
Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications (Studies in Computational Intelligence), edi
Computer Science
Neural and Evolutionary Computing
Scientific paper
10.1007/978-3-540-34954-9_14
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 nurses 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.
Aickelin Uwe
Li Jingpeng
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