An estimation of distribution algorithm with intelligent local search for rule-based nurse rostering.
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| Title: | An estimation of distribution algorithm with intelligent local search for rule-based nurse rostering. |
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| Authors: | Aickelin, U.1, Burke, E. K.1, Li, J.1 jpl@cs.nott.ac.uk |
| Source: | Journal of the Operational Research Society. Dec2007, Vol. 58 Issue 12, p1574-1585. 12p. 1 Diagram, 1 Chart, 1 Graph. |
| Subjects: | Estimation theory, Distribution (Probability theory), Algorithms, Nurses, Lists, Medical laws, Memetics, Mathematical models, Computational complexity, Mathematical programming |
| Abstract: | This paper proposes a new memetic evolutionary algorithm to achieve explicit learning in rule-based nurse rostering, which involves applying a set of heuristic rules for each nurse's assignment. The main framework of the algorithm is an estimation of distribution algorithm, in which an ant-miner methodology improves the individual solutions produced in each generation. Unlike our previous work (where learning is implicit), the learning in the memetic estimation of distribution algorithm is explicit, that is, we are able to identify building blocks directly. The overall approach learns by building a probabilistic model, that is, an estimation of the probability distribution of individual nurse-rule pairs that are used to construct schedules. The local search processor (ie the ant-miner) reinforces nurse-rule pairs that receive higher rewards, A challenging real-world nurse rostering problem is used as the test problem. Computational results show that the proposed approach outperforms most existing approaches. It is suggested that the learning methodologies suggested in this paper may be applied to other scheduling problems where schedules are built systematically according to specific rules. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of the Operational Research Society is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 27710398 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: An estimation of distribution algorithm with intelligent local search for rule-based nurse rostering. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Aickelin%2C+U%2E%22">Aickelin, U.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Burke%2C+E%2E+K%2E%22">Burke, E. K.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Li%2C+J%2E%22">Li, J.</searchLink><relatesTo>1</relatesTo><i> jpl@cs.nott.ac.uk</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+the+Operational+Research+Society%22">Journal of the Operational Research Society</searchLink>. Dec2007, Vol. 58 Issue 12, p1574-1585. 12p. 1 Diagram, 1 Chart, 1 Graph. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Estimation+theory%22">Estimation theory</searchLink><br /><searchLink fieldCode="DE" term="%22Distribution+%28Probability+theory%29%22">Distribution (Probability theory)</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Nurses%22">Nurses</searchLink><br /><searchLink fieldCode="DE" term="%22Lists%22">Lists</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+laws%22">Medical laws</searchLink><br /><searchLink fieldCode="DE" term="%22Memetics%22">Memetics</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+models%22">Mathematical models</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+complexity%22">Computational complexity</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+programming%22">Mathematical programming</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper proposes a new memetic evolutionary algorithm to achieve explicit learning in rule-based nurse rostering, which involves applying a set of heuristic rules for each nurse's assignment. The main framework of the algorithm is an estimation of distribution algorithm, in which an ant-miner methodology improves the individual solutions produced in each generation. Unlike our previous work (where learning is implicit), the learning in the memetic estimation of distribution algorithm is explicit, that is, we are able to identify building blocks directly. The overall approach learns by building a probabilistic model, that is, an estimation of the probability distribution of individual nurse-rule pairs that are used to construct schedules. The local search processor (ie the ant-miner) reinforces nurse-rule pairs that receive higher rewards, A challenging real-world nurse rostering problem is used as the test problem. Computational results show that the proposed approach outperforms most existing approaches. It is suggested that the learning methodologies suggested in this paper may be applied to other scheduling problems where schedules are built systematically according to specific rules. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of the Operational Research Society is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1057/palgrave.jors.2602308 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 1574 Subjects: – SubjectFull: Estimation theory Type: general – SubjectFull: Distribution (Probability theory) Type: general – SubjectFull: Algorithms Type: general – SubjectFull: Nurses Type: general – SubjectFull: Lists Type: general – SubjectFull: Medical laws Type: general – SubjectFull: Memetics Type: general – SubjectFull: Mathematical models Type: general – SubjectFull: Computational complexity Type: general – SubjectFull: Mathematical programming Type: general Titles: – TitleFull: An estimation of distribution algorithm with intelligent local search for rule-based nurse rostering. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Aickelin, U. – PersonEntity: Name: NameFull: Burke, E. K. – PersonEntity: Name: NameFull: Li, J. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2007 Type: published Y: 2007 Identifiers: – Type: issn-print Value: 01605682 Numbering: – Type: volume Value: 58 – Type: issue Value: 12 Titles: – TitleFull: Journal of the Operational Research Society Type: main |
| ResultId | 1 |
