Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/31314
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: The Application of Bayesian Optimization and Classifier Systems in Nurse Scheduling
Editor(s): Bullinaria, John A
Lozano, José A
Smith, Jim
Merelo-Guervós, Juan Julián
Burke, Edmund K
Yao, Xin
Rowe, Jonathan E
Tiňo, Peter
Kabán, Ata
Schwefel, Hans-Paul
Citation: Li J & Aickelin U (2004) The Application of Bayesian Optimization and Classifier Systems in Nurse Scheduling. In: Bullinaria JA, Lozano JA, Smith J, Merelo-Guervós JJ, Burke EK, Yao X, Rowe JE, Tiňo P, Kabán A & Schwefel H (eds.) Parallel Problem Solving from Nature - PPSN VIII. Lecture Notes in Computer Science, 3242. PPSN 2004: International Conference on Parallel Problem Solving from Nature, Birmingham, UK, 18.09.2004-22.09.2004. Berlin Heidelberg: Springer, pp. 581-590. https://doi.org/10.1007/978-3-540-30217-9_59
Issue Date: 2004
Series/Report no.: Lecture Notes in Computer Science, 3242
Conference Name: PPSN 2004: International Conference on Parallel Problem Solving from Nature
Conference Dates: 2004-09-18 - 2004-09-22
Conference Location: Birmingham, UK
Abstract: Two ideas taken from Bayesian optimization and classifier systems are presented for personnel scheduling based on choosing a suitable scheduling rule from a set for each person’s assignment. Unlike our previous work of using genetic algorithms whose learning is implicit, the learning in both approaches is explicit, i.e. we are able to identify building blocks directly. To achieve this target, the Bayesian optimization algorithm builds a Bayesian network of the joint probability distribution of the rules used to construct solutions, while the adapted classifier system assigns each rule a strength value that is constantly updated according to its usefulness in the current situation. Computational results from 52 real data instances of nurse scheduling demonstrate the success of both approaches. It is also suggested that the learning mechanism in the proposed approaches might be suitable for other scheduling problems.
Status: VoR - Version of Record
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