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An evolutionary squeaky wheel optimisation approach to personnel scheduling

Aickelin, Uwe; Li, Jingpeng; Burke, Edmund

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Authors

Uwe Aickelin

Jingpeng Li

Edmund Burke



Abstract

The quest for robust heuristics that are able to solve more than one problem is ongoing. In this paper, we present, discuss and analyse a technique called Evolutionary Squeaky Wheel Optimisation and apply it to two different personnel scheduling problems. Evolutionary Squeaky Wheel Optimisation improves the original Squeaky Wheel Optimisation’s effectiveness and execution speed by incorporating two additional steps (Selection and Mutation) for added evolution. In the Evolutionary Squeaky Wheel Optimisation, a cycle of Analysis-Selection-Mutation-Prioritization-Construction continues until stopping
conditions are reached. The aim of the Analysis step is to identify below average solution components by calculating a fitness value for all components. The Selection step then chooses amongst these underperformers and discards some
probabilistically based on fitness. The Mutation step further discards a few components at random. Solutions can become incomplete and thus repairs may be required. The repair is carried out by using the Prioritization step to first produce priorities that determine an order by which the following Construction step then schedules the remaining components. Therefore, improvements in the
Evolutionary Squeaky Wheel Optimisation is achieved by selective solution disruption mixed with iterative improvement and constructive repair. Strong experimental results are reported on two different domains of personnel scheduling: bus and rail driver scheduling and hospital nurse scheduling.

Citation

Aickelin, U., Li, J., & Burke, E. (2009). An evolutionary squeaky wheel optimisation approach to personnel scheduling. IEEE Transactions on Evolutionary Computation, 13(2), https://doi.org/10.1109/TEVC.2008.2004262

Journal Article Type Article
Publication Date Mar 1, 2009
Deposit Date Mar 15, 2010
Publicly Available Date Mar 28, 2024
Journal IEEE Transactions on Evolutionary Computation
Print ISSN 1089-778X
Electronic ISSN 1089-778X
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 13
Issue 2
DOI https://doi.org/10.1109/TEVC.2008.2004262
Public URL https://nottingham-repository.worktribe.com/output/1013829
Publisher URL http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4657382&tag=1
Additional Information "©20xx IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE."

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