'Exploiting problem structure in a genetic algorithm approach to a nurse rostering problem'
There is considerable interest in the use of genetic algorithms to solve problems arising in the areas of scheduling and timetabling. However, the classical genetic algorithm paradigm is not well equipped to handle the conflict between objectives and constraints that typically occurs in such problem...
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| Format: | Article |
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2000
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| Online Access: | https://eprints.nottingham.ac.uk/665/ http://www3.interscience.wiley.com/cgi-bin/fulltext/72501094/PDFSTART |
| _version_ | 1848790458647445504 |
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| author | Aickelin, Uwe Dowsland, Kathryn |
| author_facet | Aickelin, Uwe Dowsland, Kathryn |
| author_sort | Aickelin, Uwe |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | There is considerable interest in the use of genetic algorithms to solve problems arising in the areas of scheduling and timetabling. However, the classical genetic algorithm paradigm is not well equipped to handle the conflict between objectives and constraints that typically occurs in such problems. In order to overcome this, successful implementations frequently make use of problem specific knowledge. This paper is concerned with the development of a GA for a nurse rostering problem at a major UK hospital. The structure of the constraints is used as the basis for a co-evolutionary strategy using co-operating sub-populations. Problem specific knowledge is also used to define a system of incentives and disincentives, and a complementary mutation operator. Empirical results based on 52 weeks of live data show how these features are able to improve an unsuccessful canonical GA to the point where it is able to provide a practical solution to the problem. |
| first_indexed | 2025-11-14T18:12:56Z |
| format | Article |
| id | nottingham-665 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T18:12:56Z |
| publishDate | 2000 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-6652020-05-04T20:32:52Z https://eprints.nottingham.ac.uk/665/ 'Exploiting problem structure in a genetic algorithm approach to a nurse rostering problem' Aickelin, Uwe Dowsland, Kathryn There is considerable interest in the use of genetic algorithms to solve problems arising in the areas of scheduling and timetabling. However, the classical genetic algorithm paradigm is not well equipped to handle the conflict between objectives and constraints that typically occurs in such problems. In order to overcome this, successful implementations frequently make use of problem specific knowledge. This paper is concerned with the development of a GA for a nurse rostering problem at a major UK hospital. The structure of the constraints is used as the basis for a co-evolutionary strategy using co-operating sub-populations. Problem specific knowledge is also used to define a system of incentives and disincentives, and a complementary mutation operator. Empirical results based on 52 weeks of live data show how these features are able to improve an unsuccessful canonical GA to the point where it is able to provide a practical solution to the problem. 2000 Article PeerReviewed Aickelin, Uwe and Dowsland, Kathryn (2000) 'Exploiting problem structure in a genetic algorithm approach to a nurse rostering problem'. Journal of Scheduling, 3 (3) . pp. 139-153. manpower scheduling genetic algorithms heuristics co-evolution http://www3.interscience.wiley.com/cgi-bin/fulltext/72501094/PDFSTART http://www3.interscience.wiley.com/cgi-bin/fulltext/72501094/PDFSTART |
| spellingShingle | manpower scheduling genetic algorithms heuristics co-evolution Aickelin, Uwe Dowsland, Kathryn 'Exploiting problem structure in a genetic algorithm approach to a nurse rostering problem' |
| title | 'Exploiting problem structure in a genetic algorithm approach to a nurse rostering problem' |
| title_full | 'Exploiting problem structure in a genetic algorithm approach to a nurse rostering problem' |
| title_fullStr | 'Exploiting problem structure in a genetic algorithm approach to a nurse rostering problem' |
| title_full_unstemmed | 'Exploiting problem structure in a genetic algorithm approach to a nurse rostering problem' |
| title_short | 'Exploiting problem structure in a genetic algorithm approach to a nurse rostering problem' |
| title_sort | 'exploiting problem structure in a genetic algorithm approach to a nurse rostering problem' |
| topic | manpower scheduling genetic algorithms heuristics co-evolution |
| url | https://eprints.nottingham.ac.uk/665/ https://eprints.nottingham.ac.uk/665/ http://www3.interscience.wiley.com/cgi-bin/fulltext/72501094/PDFSTART |