Building Better Nurse Scheduling Algorithms
The aim of this research is twofold: Firstly, to model and solve a complex nurse scheduling problem with an integer programming formulation and evolutionary algorithms. Secondly, to detail a novel statistical method of comparing and hence building better scheduling algorithms by identifying successf...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer Verlag (Germany)
2004
|
| Subjects: | |
| Online Access: | https://eprints.nottingham.ac.uk/612/ |
| _version_ | 1848790444065947648 |
|---|---|
| author | Aickelin, Uwe White, Paul |
| author_facet | Aickelin, Uwe White, Paul |
| author_sort | Aickelin, Uwe |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | The aim of this research is twofold: Firstly, to model and solve a complex nurse scheduling problem with an integer programming formulation and evolutionary algorithms. Secondly, to detail a novel statistical method of comparing and hence building better scheduling algorithms by identifying successful algorithm modifications. The comparison method captures the results of algorithms in a single figure that can then be compared using traditional statistical techniques. Thus, the proposed method of comparing algorithms is an objective procedure designed to assist in the process of improving an algorithm. This is achieved even when some results are non-numeric or missing due to infeasibility. The final algorithm outperforms all previous evolutionary algorithms, which relied on human expertise for modification. |
| first_indexed | 2025-11-14T18:12:42Z |
| format | Article |
| id | nottingham-612 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T18:12:42Z |
| publishDate | 2004 |
| publisher | Springer Verlag (Germany) |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-6122021-05-31T14:47:48Z https://eprints.nottingham.ac.uk/612/ Building Better Nurse Scheduling Algorithms Aickelin, Uwe White, Paul The aim of this research is twofold: Firstly, to model and solve a complex nurse scheduling problem with an integer programming formulation and evolutionary algorithms. Secondly, to detail a novel statistical method of comparing and hence building better scheduling algorithms by identifying successful algorithm modifications. The comparison method captures the results of algorithms in a single figure that can then be compared using traditional statistical techniques. Thus, the proposed method of comparing algorithms is an objective procedure designed to assist in the process of improving an algorithm. This is achieved even when some results are non-numeric or missing due to infeasibility. The final algorithm outperforms all previous evolutionary algorithms, which relied on human expertise for modification. Springer Verlag (Germany) 2004 Article PeerReviewed application/pdf en https://eprints.nottingham.ac.uk/612/1/04annals_nurse.pdf Aickelin, Uwe and White, Paul (2004) Building Better Nurse Scheduling Algorithms. Annals of Operations Research, 128 . pp. 159-177. ISSN 0254-5330 Nurse scheduling evolutionary algorithms integer programming statistical comparison method http://springerlink.metapress.com/content/101740/ |
| spellingShingle | Nurse scheduling evolutionary algorithms integer programming statistical comparison method Aickelin, Uwe White, Paul Building Better Nurse Scheduling Algorithms |
| title | Building Better Nurse Scheduling Algorithms |
| title_full | Building Better Nurse Scheduling Algorithms |
| title_fullStr | Building Better Nurse Scheduling Algorithms |
| title_full_unstemmed | Building Better Nurse Scheduling Algorithms |
| title_short | Building Better Nurse Scheduling Algorithms |
| title_sort | building better nurse scheduling algorithms |
| topic | Nurse scheduling evolutionary algorithms integer programming statistical comparison method |
| url | https://eprints.nottingham.ac.uk/612/ https://eprints.nottingham.ac.uk/612/ |