Bayesian Optimisation Algorithm for Nurse Scheduling, Scalable Optimization via Probabilistic Modeling
Our research has shown that schedules can be built mimicking a human scheduler by using a set of rules that involve domain knowledge. This chapter presents a Bayesian Optimization Algorithm (BOA) for the nurse scheduling problem that chooses such suitable scheduling rules from a set for each nurse’...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Book Section |
| Published: |
Springer
2006
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| Online Access: | https://eprints.nottingham.ac.uk/586/ |
| _version_ | 1848790437347721216 |
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| author | Li, Jingpeng Aickelin, Uwe |
| author2 | Pelikan, M |
| author_facet | Pelikan, M Li, Jingpeng Aickelin, Uwe |
| author_sort | Li, Jingpeng |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Our research has shown that schedules can be built mimicking a human scheduler by using a set of rules that involve domain knowledge. This chapter presents a Bayesian Optimization Algorithm (BOA) for the nurse scheduling problem that chooses such suitable scheduling rules from a set for each nurse’s assignment. Based on the idea of using probabilistic models, the BOA builds a Bayesian network for the set of promising solutions and samples these networks to generate new candidate solutions. Computational results from 52 real data instances demonstrate the success of this approach. It is also suggested that the learning mechanism in the proposed algorithm may be suitable for other scheduling problems. |
| first_indexed | 2025-11-14T18:12:36Z |
| format | Book Section |
| id | nottingham-586 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T18:12:36Z |
| publishDate | 2006 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-5862020-05-04T20:30:04Z https://eprints.nottingham.ac.uk/586/ Bayesian Optimisation Algorithm for Nurse Scheduling, Scalable Optimization via Probabilistic Modeling Li, Jingpeng Aickelin, Uwe Our research has shown that schedules can be built mimicking a human scheduler by using a set of rules that involve domain knowledge. This chapter presents a Bayesian Optimization Algorithm (BOA) for the nurse scheduling problem that chooses such suitable scheduling rules from a set for each nurse’s assignment. Based on the idea of using probabilistic models, the BOA builds a Bayesian network for the set of promising solutions and samples these networks to generate new candidate solutions. Computational results from 52 real data instances demonstrate the success of this approach. It is also suggested that the learning mechanism in the proposed algorithm may be suitable for other scheduling problems. Springer Pelikan, M Sastry, K Cantu-Paz, E 2006 Book Section PeerReviewed Li, Jingpeng and Aickelin, Uwe (2006) Bayesian Optimisation Algorithm for Nurse Scheduling, Scalable Optimization via Probabilistic Modeling. In: Algorithms to Applications (Studies in Computational Intelligence). Springer, pp. 315-332. |
| spellingShingle | Li, Jingpeng Aickelin, Uwe Bayesian Optimisation Algorithm for Nurse Scheduling, Scalable Optimization via Probabilistic Modeling |
| title | Bayesian Optimisation Algorithm for Nurse Scheduling, Scalable Optimization via Probabilistic Modeling |
| title_full | Bayesian Optimisation Algorithm for Nurse Scheduling, Scalable Optimization via Probabilistic Modeling |
| title_fullStr | Bayesian Optimisation Algorithm for Nurse Scheduling, Scalable Optimization via Probabilistic Modeling |
| title_full_unstemmed | Bayesian Optimisation Algorithm for Nurse Scheduling, Scalable Optimization via Probabilistic Modeling |
| title_short | Bayesian Optimisation Algorithm for Nurse Scheduling, Scalable Optimization via Probabilistic Modeling |
| title_sort | bayesian optimisation algorithm for nurse scheduling, scalable optimization via probabilistic modeling |
| url | https://eprints.nottingham.ac.uk/586/ |