Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/31391
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Author(s): Li, Jingpeng
Aickelin, Uwe
Contact Email: jli@cs.stir.ac.uk
Title: A Bayesian optimization algorithm for the nurse scheduling problem
Citation: Li J & Aickelin U (2003) A Bayesian optimization algorithm for the nurse scheduling problem. In: The 2003 Congress on Evolutionary Computation, 2003. CEC '03. The 2003 Congress on Evolutionary Computation, 2003. CEC '03., Canberra, Australia, 08.12.2003-12.12.2003. Piscataway, NJ, USA: IEEE. https://doi.org/10.1109/cec.2003.1299938
Issue Date: Dec-2003
Conference Name: The 2003 Congress on Evolutionary Computation, 2003. CEC '03.
Conference Dates: 2003-12-08 - 2003-12-12
Conference Location: Canberra, Australia
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.
Status: VoR - Version of Record
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