Selective Maintenance Optimization for a Multi-State System Considering Human Reliability
In an actual industrial or military operations environment, a multi-state system (MSS) consisting of multi-state components often needs to perform multiple missions in succession. To improve the probability of the system successfully completing the next mission, all the maintenance activities need t...
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MDPI AG
2019-05-01
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Series: | Symmetry |
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Online Access: | https://www.mdpi.com/2073-8994/11/5/652 |
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author | Zhonghao Zhao Boping Xiao Naichao Wang Xiaoyuan Yan Lin Ma |
author_facet | Zhonghao Zhao Boping Xiao Naichao Wang Xiaoyuan Yan Lin Ma |
author_sort | Zhonghao Zhao |
collection | DOAJ |
description | In an actual industrial or military operations environment, a multi-state system (MSS) consisting of multi-state components often needs to perform multiple missions in succession. To improve the probability of the system successfully completing the next mission, all the maintenance activities need to be performed during maintenance breaks between any two consecutive missions under limited maintenance resources. In such case, selective maintenance is a widely used maintenance policy. As a typical discrete mathematics problem, selective maintenance has received widespread attention. In this work, a selective maintenance model considering human reliability for multi-component systems is investigated. Each maintenance worker can be in one of multiple discrete working levels due to their human error probability (HEP). The state of components after maintenance is assumed to be random and follow an identified probability distribution. To solve the problem, this paper proposes a human reliability model and a method to determine the state distribution of components after maintenance. The objective of selective maintenance scheduling is to find the maintenance action with the optimal reliability for each component in a maintenance break subject to constraints of time and cost. In place of an enumerative method, a genetic algorithm (GA) is employed to solve the complicated optimization problem taking human reliability into account. The results show the importance of considering human reliability in selective maintenance scheduling for an MSS. |
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format | Article |
id | doaj.art-25901d3eb59c474b9b3b94596310e035 |
institution | Directory Open Access Journal |
issn | 2073-8994 |
language | English |
last_indexed | 2024-04-11T20:46:54Z |
publishDate | 2019-05-01 |
publisher | MDPI AG |
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series | Symmetry |
spelling | doaj.art-25901d3eb59c474b9b3b94596310e0352022-12-22T04:03:59ZengMDPI AGSymmetry2073-89942019-05-0111565210.3390/sym11050652sym11050652Selective Maintenance Optimization for a Multi-State System Considering Human ReliabilityZhonghao Zhao0Boping Xiao1Naichao Wang2Xiaoyuan Yan3Lin Ma4School of Reliability and System Engineering, Beihang University, Beijing 100191, ChinaSchool of Reliability and System Engineering, Beihang University, Beijing 100191, ChinaSchool of Reliability and System Engineering, Beihang University, Beijing 100191, ChinaSchool of Reliability and System Engineering, Beihang University, Beijing 100191, ChinaSchool of Reliability and System Engineering, Beihang University, Beijing 100191, ChinaIn an actual industrial or military operations environment, a multi-state system (MSS) consisting of multi-state components often needs to perform multiple missions in succession. To improve the probability of the system successfully completing the next mission, all the maintenance activities need to be performed during maintenance breaks between any two consecutive missions under limited maintenance resources. In such case, selective maintenance is a widely used maintenance policy. As a typical discrete mathematics problem, selective maintenance has received widespread attention. In this work, a selective maintenance model considering human reliability for multi-component systems is investigated. Each maintenance worker can be in one of multiple discrete working levels due to their human error probability (HEP). The state of components after maintenance is assumed to be random and follow an identified probability distribution. To solve the problem, this paper proposes a human reliability model and a method to determine the state distribution of components after maintenance. The objective of selective maintenance scheduling is to find the maintenance action with the optimal reliability for each component in a maintenance break subject to constraints of time and cost. In place of an enumerative method, a genetic algorithm (GA) is employed to solve the complicated optimization problem taking human reliability into account. The results show the importance of considering human reliability in selective maintenance scheduling for an MSS.https://www.mdpi.com/2073-8994/11/5/652selective maintenancemulti-state systemhuman reliabilityoptimizationgenetic algorithm |
spellingShingle | Zhonghao Zhao Boping Xiao Naichao Wang Xiaoyuan Yan Lin Ma Selective Maintenance Optimization for a Multi-State System Considering Human Reliability Symmetry selective maintenance multi-state system human reliability optimization genetic algorithm |
title | Selective Maintenance Optimization for a Multi-State System Considering Human Reliability |
title_full | Selective Maintenance Optimization for a Multi-State System Considering Human Reliability |
title_fullStr | Selective Maintenance Optimization for a Multi-State System Considering Human Reliability |
title_full_unstemmed | Selective Maintenance Optimization for a Multi-State System Considering Human Reliability |
title_short | Selective Maintenance Optimization for a Multi-State System Considering Human Reliability |
title_sort | selective maintenance optimization for a multi state system considering human reliability |
topic | selective maintenance multi-state system human reliability optimization genetic algorithm |
url | https://www.mdpi.com/2073-8994/11/5/652 |
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