An Estimation of Distribution Algorithm with Intelligent Local Search for Rule-based Nurse Rostering
This paper proposes a new memetic evolutionary algorithm to achieve explicit learning in rule-based nurse rostering, which involves applying a set of heuristic rules for each nurse's assignment. The main framework of the algorithm is an estimation of distribution algorithm, in which an ant-mine...
| Main Authors: | , , |
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| Format: | Article |
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Palgrave
2007
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| Online Access: | https://eprints.nottingham.ac.uk/655/ |
| _version_ | 1848790456061657088 |
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| author | Aickelin, Uwe Burke, Edmund Li, Jingpeng |
| author_facet | Aickelin, Uwe Burke, Edmund Li, Jingpeng |
| author_sort | Aickelin, Uwe |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | This paper proposes a new memetic evolutionary algorithm to achieve explicit learning in rule-based nurse rostering, which involves applying a set of heuristic rules for each nurse's assignment. The main framework of the algorithm is an estimation of distribution algorithm, in which an ant-miner methodology improves the individual solutions produced in each generation. Unlike our previous work (where learning is implicit), the learning in the memetic estimation of distribution algorithm is explicit, i.e. we are able to identify building blocks directly. The overall approach learns by building a probabilistic model, i.e. an estimation of the probability distribution of individual nurse-rule pairs that are used to construct schedules. The local search processor (i.e. the ant-miner) reinforces nurse-rule pairs that receive higher rewards. A challenging real world nurse rostering problem is used as the test problem. Computational results show that the proposed approach outperforms most existing approaches. It is suggested that the learning methodologies suggested in this paper may be applied to other scheduling problems where schedules are built systematically according to specific rules. |
| first_indexed | 2025-11-14T18:12:54Z |
| format | Article |
| id | nottingham-655 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T18:12:54Z |
| publishDate | 2007 |
| publisher | Palgrave |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-6552020-05-04T20:28:36Z https://eprints.nottingham.ac.uk/655/ An Estimation of Distribution Algorithm with Intelligent Local Search for Rule-based Nurse Rostering Aickelin, Uwe Burke, Edmund Li, Jingpeng This paper proposes a new memetic evolutionary algorithm to achieve explicit learning in rule-based nurse rostering, which involves applying a set of heuristic rules for each nurse's assignment. The main framework of the algorithm is an estimation of distribution algorithm, in which an ant-miner methodology improves the individual solutions produced in each generation. Unlike our previous work (where learning is implicit), the learning in the memetic estimation of distribution algorithm is explicit, i.e. we are able to identify building blocks directly. The overall approach learns by building a probabilistic model, i.e. an estimation of the probability distribution of individual nurse-rule pairs that are used to construct schedules. The local search processor (i.e. the ant-miner) reinforces nurse-rule pairs that receive higher rewards. A challenging real world nurse rostering problem is used as the test problem. Computational results show that the proposed approach outperforms most existing approaches. It is suggested that the learning methodologies suggested in this paper may be applied to other scheduling problems where schedules are built systematically according to specific rules. Palgrave 2007 Article PeerReviewed Aickelin, Uwe, Burke, Edmund and Li, Jingpeng (2007) An Estimation of Distribution Algorithm with Intelligent Local Search for Rule-based Nurse Rostering. Journal of the Operational Research Society . (In Press) Nurse Rostering Estimation of Distribution Algorithm Local Search Ant Colony Optimization http://www.palgrave-journals.com/jors/index.html doi:10.1057/palgrave.jors.2602308 doi:10.1057/palgrave.jors.2602308 |
| spellingShingle | Nurse Rostering Estimation of Distribution Algorithm Local Search Ant Colony Optimization Aickelin, Uwe Burke, Edmund Li, Jingpeng An Estimation of Distribution Algorithm with Intelligent Local Search for Rule-based Nurse Rostering |
| title | An Estimation of Distribution Algorithm with Intelligent Local Search for Rule-based Nurse Rostering |
| title_full | An Estimation of Distribution Algorithm with Intelligent Local Search for Rule-based Nurse Rostering |
| title_fullStr | An Estimation of Distribution Algorithm with Intelligent Local Search for Rule-based Nurse Rostering |
| title_full_unstemmed | An Estimation of Distribution Algorithm with Intelligent Local Search for Rule-based Nurse Rostering |
| title_short | An Estimation of Distribution Algorithm with Intelligent Local Search for Rule-based Nurse Rostering |
| title_sort | estimation of distribution algorithm with intelligent local search for rule-based nurse rostering |
| topic | Nurse Rostering Estimation of Distribution Algorithm Local Search Ant Colony Optimization |
| url | https://eprints.nottingham.ac.uk/655/ https://eprints.nottingham.ac.uk/655/ https://eprints.nottingham.ac.uk/655/ |