A Bayesian Optimisation Algorithm for the Nurse Scheduling Problem

8 Pages Posted: 11 Sep 2016

See all articles by Jingpeng Li

Jingpeng Li

University of Nottingham

Uwe Aickelin

University of Melbourne - School of Computing and Information Systems

Date Written: January 1, 2003

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.

Suggested Citation

Li, Jingpeng and Aickelin, Uwe, A Bayesian Optimisation Algorithm for the Nurse Scheduling Problem (January 1, 2003). Available at SSRN: https://ssrn.com/abstract=2832026 or http://dx.doi.org/10.2139/ssrn.2832026

Jingpeng Li

University of Nottingham ( email )

University Park
Nottingham, NG8 1BB
United Kingdom

Uwe Aickelin (Contact Author)

University of Melbourne - School of Computing and Information Systems ( email )

Australia

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