Academic Journal
Optimal maintenance policy with degradation-shock dependence and a reliability constraint using simulation optimisation.
| Τίτλος: | Optimal maintenance policy with degradation-shock dependence and a reliability constraint using simulation optimisation. |
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| Συγγραφείς: | Chang, Kuo-Hao1,2 (AUTHOR) chang@mx.nthu.edu.tw, Chen, Yan-Shuo3 (AUTHOR), Chen, Chun-Hung4 (AUTHOR) |
| Πηγή: | International Journal of Production Research. Feb2026, Vol. 64 Issue 4, p1202-1226. 25p. |
| Θεματικοί όροι: | *Reliability in engineering, Imperialist competitive algorithm, Optimization algorithms, Machine part failures, Failure mode & effects analysis, Sensitivity analysis |
| Περίληψη: | Machine failures can generally be divided into soft failures caused by the normal usage of the equipment itself and hard failures caused by external impacts. Soft failures and hard failures may also affect each other, meaning that external impacts can accelerate the degradation of the equipment's performance and make it more prone to failures as the number of impacts increases. We introduce a new maintenance and reliability model which considers the dependencies between hard and soft failure modes for individual machine types, allowing hard failure thresholds to be dynamically adjusted. This model, based on a manufacturing system of general topology, not only incorporates degradation-shock dependence, but also includes a reliability constraint. The model is set up such that once the degradation level of any machine exceeds a predetermined threshold, maintenance and repair actions, both imperfect and perfect, will be executed. In order to determine the optimal thresholds for maintenance and upkeep strategy, we develop a simulation optimisation algorithm, known as the Jaya-Imperialist Competitive Algorithm (JICA). This method is based on the Imperialist Competitive Algorithm (ICA) and combines the Jaya Algorithm with an adaptive penalty function. Empirical analysis has confirmed that, compared to a set of benchmark algorithms, the algorithm proposed in this study can obtain a superior maintenance and repair strategy while demonstrating greater efficiency. We also perform sensitivity analysis on several important parameters, which can facilitate a deeper understanding of the optimisation model and support optimal decision-making. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Production Research 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.) | |
| Βάση Δεδομένων: | Business Source Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Optimal maintenance policy with degradation-shock dependence and a reliability constraint using simulation optimisation. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Chang%2C+Kuo-Hao%22">Chang, Kuo-Hao</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> chang@mx.nthu.edu.tw</i><br /><searchLink fieldCode="AR" term="%22Chen%2C+Yan-Shuo%22">Chen, Yan-Shuo</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Chun-Hung%22">Chen, Chun-Hung</searchLink><relatesTo>4</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Production+Research%22">International Journal of Production Research</searchLink>. Feb2026, Vol. 64 Issue 4, p1202-1226. 25p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Reliability+in+engineering%22">Reliability in engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Imperialist+competitive+algorithm%22">Imperialist competitive algorithm</searchLink><br /><searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+part+failures%22">Machine part failures</searchLink><br /><searchLink fieldCode="DE" term="%22Failure+mode+%26+effects+analysis%22">Failure mode & effects analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Sensitivity+analysis%22">Sensitivity analysis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Machine failures can generally be divided into soft failures caused by the normal usage of the equipment itself and hard failures caused by external impacts. Soft failures and hard failures may also affect each other, meaning that external impacts can accelerate the degradation of the equipment's performance and make it more prone to failures as the number of impacts increases. We introduce a new maintenance and reliability model which considers the dependencies between hard and soft failure modes for individual machine types, allowing hard failure thresholds to be dynamically adjusted. This model, based on a manufacturing system of general topology, not only incorporates degradation-shock dependence, but also includes a reliability constraint. The model is set up such that once the degradation level of any machine exceeds a predetermined threshold, maintenance and repair actions, both imperfect and perfect, will be executed. In order to determine the optimal thresholds for maintenance and upkeep strategy, we develop a simulation optimisation algorithm, known as the Jaya-Imperialist Competitive Algorithm (JICA). This method is based on the Imperialist Competitive Algorithm (ICA) and combines the Jaya Algorithm with an adaptive penalty function. Empirical analysis has confirmed that, compared to a set of benchmark algorithms, the algorithm proposed in this study can obtain a superior maintenance and repair strategy while demonstrating greater efficiency. We also perform sensitivity analysis on several important parameters, which can facilitate a deeper understanding of the optimisation model and support optimal decision-making. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Production Research 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: 10.1080/00207543.2025.2566975 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 25 StartPage: 1202 Subjects: – SubjectFull: Reliability in engineering Type: general – SubjectFull: Imperialist competitive algorithm Type: general – SubjectFull: Optimization algorithms Type: general – SubjectFull: Machine part failures Type: general – SubjectFull: Failure mode & effects analysis Type: general – SubjectFull: Sensitivity analysis Type: general Titles: – TitleFull: Optimal maintenance policy with degradation-shock dependence and a reliability constraint using simulation optimisation. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Chang, Kuo-Hao – PersonEntity: Name: NameFull: Chen, Yan-Shuo – PersonEntity: Name: NameFull: Chen, Chun-Hung IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 02 Text: Feb2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 00207543 Numbering: – Type: volume Value: 64 – Type: issue Value: 4 Titles: – TitleFull: International Journal of Production Research Type: main |
| ResultId | 1 |