A tensor based hyper-heuristic for nurse rostering
Nurse rostering is a well-known highly constrained scheduling problem requiring assignment of shifts to nurses satisfying a variety of constraints. Exact algorithms may fail to produce high quality solutions, hence (meta)heuristics are commonly preferred as solution methods which are often designed...
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| Format: | Article |
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Elsevier
2016
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| Online Access: | https://eprints.nottingham.ac.uk/32190/ |
| _version_ | 1848794353866113024 |
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| author | Asta, Shahriar Özcan, Ender Curtois, Tim |
| author_facet | Asta, Shahriar Özcan, Ender Curtois, Tim |
| author_sort | Asta, Shahriar |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Nurse rostering is a well-known highly constrained scheduling problem requiring assignment of shifts to nurses satisfying a variety of constraints. Exact algorithms may fail to produce high quality solutions, hence (meta)heuristics are commonly preferred as solution methods which are often designed and tuned for specific (group of) problem instances. Hyper-heuristics have emerged as general search methodologies that mix and manage a predefined set of low level heuristics while solving computationally hard problems. In this study, we describe an online learning hyper-heuristic employing a data science technique which is capable of self-improvement via tensor analysis for nurse rostering. The proposed approach is evaluated on a well-known nurse rostering benchmark consisting of a diverse collection of instances obtained from different hospitals across the world. The empirical results indicate the success of the tensor-based hyper-heuristic, improving upon the best-known solutions for four of the instances. |
| first_indexed | 2025-11-14T19:14:51Z |
| format | Article |
| id | nottingham-32190 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:14:51Z |
| publishDate | 2016 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-321902020-05-04T17:46:23Z https://eprints.nottingham.ac.uk/32190/ A tensor based hyper-heuristic for nurse rostering Asta, Shahriar Özcan, Ender Curtois, Tim Nurse rostering is a well-known highly constrained scheduling problem requiring assignment of shifts to nurses satisfying a variety of constraints. Exact algorithms may fail to produce high quality solutions, hence (meta)heuristics are commonly preferred as solution methods which are often designed and tuned for specific (group of) problem instances. Hyper-heuristics have emerged as general search methodologies that mix and manage a predefined set of low level heuristics while solving computationally hard problems. In this study, we describe an online learning hyper-heuristic employing a data science technique which is capable of self-improvement via tensor analysis for nurse rostering. The proposed approach is evaluated on a well-known nurse rostering benchmark consisting of a diverse collection of instances obtained from different hospitals across the world. The empirical results indicate the success of the tensor-based hyper-heuristic, improving upon the best-known solutions for four of the instances. Elsevier 2016-04-15 Article PeerReviewed Asta, Shahriar, Özcan, Ender and Curtois, Tim (2016) A tensor based hyper-heuristic for nurse rostering. Knowledge-Based Systems, 98 . pp. 185-199. ISSN 1872-7409 Nurse rostering; Personnel scheduling; Data science; Tensor factorization; Hyper-heuristics http://www.sciencedirect.com/science/article/pii/S0950705116000514 doi:10.1016/j.knosys.2016.01.031 doi:10.1016/j.knosys.2016.01.031 |
| spellingShingle | Nurse rostering; Personnel scheduling; Data science; Tensor factorization; Hyper-heuristics Asta, Shahriar Özcan, Ender Curtois, Tim A tensor based hyper-heuristic for nurse rostering |
| title | A tensor based hyper-heuristic for nurse rostering |
| title_full | A tensor based hyper-heuristic for nurse rostering |
| title_fullStr | A tensor based hyper-heuristic for nurse rostering |
| title_full_unstemmed | A tensor based hyper-heuristic for nurse rostering |
| title_short | A tensor based hyper-heuristic for nurse rostering |
| title_sort | tensor based hyper-heuristic for nurse rostering |
| topic | Nurse rostering; Personnel scheduling; Data science; Tensor factorization; Hyper-heuristics |
| url | https://eprints.nottingham.ac.uk/32190/ https://eprints.nottingham.ac.uk/32190/ https://eprints.nottingham.ac.uk/32190/ |