A Bayesian optimization algorithm for the nurse scheduling problem

Li, Jingpeng; Aickelin, Uwe

Abstract

A Bayesian optimization algorithm for the nurse scheduling problem is presented, which involves choosing a suitable scheduling rule from a set for each nurse’s assignment. Unlike our previous work that used GAs to implement implicit learning, the learning in the proposed algorithm is explicit, i.e. eventually, we will be able to identify and mix building blocks directly. The Bayesian optimization algorithm is applied to implement such explicit learning by
building a Bayesian network of the joint distribution of solutions. The conditional probability of each variable in the network is computed according to an initial set of promising solutions. Subsequently, each new instance for each variable is generated by using the corresponding conditional probabilities, until all variables have been generated, i.e. in our case, a new rule string has been obtained. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the conditional probabilities for all nodes in the Bayesian network are updated again using the current set of promising rule strings. Computational results from 52 real data instances demonstrate the success of this approach. It
is also suggested that the learning mechanism in the
proposed approach might be suitable for other scheduling problems.

Citation

Li, J., & Aickelin, U. A Bayesian optimization algorithm for the nurse scheduling problem. Presented at 2003 Congress on Evolutionary Computation (CEC2003)

• Conference Name: 2003 Congress on Evolutionary Computation (CEC2003)
• End Date: Dec 12, 2003
• Publication Date: Jan 1, 2003
• Deposit Date: Aug 29, 2012
• Publicly Available Date: Aug 29, 2012
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1109/cec.2003.1299938
• Public URL: https://nottingham-repository.worktribe.com/output/1022430
• Publisher URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1299938
• Additional Information: Lecture published in: 2003 Congress on Evolutionary Computation: CEC 2003: proceedings, 8-12 December 2003, Canberra, Australia. Piscataway, N.J.: IEEE, 2003. 4 v. (ISBN: 9780780378049), Vol. 3, p. 2149-2156