A Bayesian optimization algorithm for the nurse scheduling problem
A Bayesian optimization algorithm for the nurse scheduling problem is presented, which involves choosing a suitable scheduling rule from a set for each nurse’s assignment. Unlike our previous work that used GAs to implement implicit learning, the learning in the proposed algorithm is explicit, i.e....
| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Published: |
2003
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| Online Access: | https://eprints.nottingham.ac.uk/1247/ |
| _version_ | 1848790569317302272 |
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| author | Li, Jingpeng Aickelin, Uwe |
| author_facet | Li, Jingpeng Aickelin, Uwe |
| author_sort | Li, Jingpeng |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | A Bayesian optimization algorithm for the nurse scheduling problem is presented, which involves choosing a suitable scheduling rule from a set for each nurse’s assignment. Unlike our previous work that used GAs to implement implicit learning, the learning in the proposed algorithm is explicit, i.e. eventually, we will be able to identify and mix building blocks directly. The Bayesian optimization algorithm is applied to implement such explicit learning by
building a Bayesian network of the joint distribution of solutions. The conditional probability of each variable in the network is computed according to an initial set of promising solutions. Subsequently, each new instance for each variable is generated by using the corresponding conditional probabilities, until all variables have been generated, i.e. in our case, a new rule string has been obtained. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the conditional probabilities for all nodes in the Bayesian network are updated again using the current set of promising rule strings. Computational results from 52 real data instances demonstrate the success of this approach. It
is also suggested that the learning mechanism in the
proposed approach might be suitable for other scheduling problems. |
| first_indexed | 2025-11-14T18:14:42Z |
| format | Conference or Workshop Item |
| id | nottingham-1247 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T18:14:42Z |
| publishDate | 2003 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-12472020-05-04T20:32:05Z https://eprints.nottingham.ac.uk/1247/ A Bayesian optimization algorithm for the nurse scheduling problem Li, Jingpeng Aickelin, Uwe A Bayesian optimization algorithm for the nurse scheduling problem is presented, which involves choosing a suitable scheduling rule from a set for each nurse’s assignment. Unlike our previous work that used GAs to implement implicit learning, the learning in the proposed algorithm is explicit, i.e. eventually, we will be able to identify and mix building blocks directly. The Bayesian optimization algorithm is applied to implement such explicit learning by building a Bayesian network of the joint distribution of solutions. The conditional probability of each variable in the network is computed according to an initial set of promising solutions. Subsequently, each new instance for each variable is generated by using the corresponding conditional probabilities, until all variables have been generated, i.e. in our case, a new rule string has been obtained. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the conditional probabilities for all nodes in the Bayesian network are updated again using the current set of promising rule strings. Computational results from 52 real data instances demonstrate the success of this approach. It is also suggested that the learning mechanism in the proposed approach might be suitable for other scheduling problems. 2003 Conference or Workshop Item PeerReviewed Li, Jingpeng and Aickelin, Uwe (2003) A Bayesian optimization algorithm for the nurse scheduling problem. In: 2003 Congress on Evolutionary Computation (CEC2003), 8-12 Dec. 2003, Canberra, Australia. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1299938 |
| spellingShingle | Li, Jingpeng Aickelin, Uwe A Bayesian optimization algorithm for the nurse scheduling problem |
| title | A Bayesian optimization algorithm for the nurse scheduling problem |
| title_full | A Bayesian optimization algorithm for the nurse scheduling problem |
| title_fullStr | A Bayesian optimization algorithm for the nurse scheduling problem |
| title_full_unstemmed | A Bayesian optimization algorithm for the nurse scheduling problem |
| title_short | A Bayesian optimization algorithm for the nurse scheduling problem |
| title_sort | bayesian optimization algorithm for the nurse scheduling problem |
| url | https://eprints.nottingham.ac.uk/1247/ https://eprints.nottingham.ac.uk/1247/ |