An evolutionary squeaky wheel optimisation approach to personnel scheduling
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 impr...
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| Format: | Article |
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2009
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| Online Access: | https://eprints.nottingham.ac.uk/1238/ |
| _version_ | 1848790567425671168 |
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| author | Aickelin, Uwe Li, Jingpeng Burke, Edmund |
| author_facet | Aickelin, Uwe Li, Jingpeng Burke, Edmund |
| author_sort | Aickelin, Uwe |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T18:14:40Z |
| format | Article |
| id | nottingham-1238 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T18:14:40Z |
| publishDate | 2009 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-12382020-05-04T20:26:23Z https://eprints.nottingham.ac.uk/1238/ An evolutionary squeaky wheel optimisation approach to personnel scheduling Aickelin, Uwe Li, Jingpeng Burke, Edmund 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. 2009-03 Article PeerReviewed Aickelin, Uwe, Li, Jingpeng and Burke, Edmund (2009) An evolutionary squeaky wheel optimisation approach to personnel scheduling. IEEE Transactions on Evolutionary Computation, 13 (2). pp. 433-443. ISSN 1089-778X http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4657382&tag=1 doi:10.1109/TEVC.2008.2004262 doi:10.1109/TEVC.2008.2004262 |
| spellingShingle | Aickelin, Uwe Li, Jingpeng Burke, Edmund An evolutionary squeaky wheel optimisation approach to personnel scheduling |
| title | An evolutionary squeaky wheel optimisation approach to personnel scheduling |
| title_full | An evolutionary squeaky wheel optimisation approach to personnel scheduling |
| title_fullStr | An evolutionary squeaky wheel optimisation approach to personnel scheduling |
| title_full_unstemmed | An evolutionary squeaky wheel optimisation approach to personnel scheduling |
| title_short | An evolutionary squeaky wheel optimisation approach to personnel scheduling |
| title_sort | evolutionary squeaky wheel optimisation approach to personnel scheduling |
| url | https://eprints.nottingham.ac.uk/1238/ https://eprints.nottingham.ac.uk/1238/ https://eprints.nottingham.ac.uk/1238/ |