A hybrid multi-objective evolutionary algorithm for solving an adaptive flexible job-shop rescheduling problem with real-time order acceptance and condition-based preventive maintenance
Production scheduling and maintenance planning are two of the most important tasks in the modern manufacturing workshop. Meanwhile, due to the dynamic order arrival and real-time machine monitoring information updating, the integrated optimization of them becoming more complex and meaningful. Theref...
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| Vydáno v: | Expert systems with applications Ročník 212; s. 118711 |
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| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier Ltd
01.02.2023
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| Témata: | |
| ISSN: | 0957-4174, 1873-6793 |
| On-line přístup: | Získat plný text |
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| Abstract | Production scheduling and maintenance planning are two of the most important tasks in the modern manufacturing workshop. Meanwhile, due to the dynamic order arrival and real-time machine monitoring information updating, the integrated optimization of them becoming more complex and meaningful. Therefore, this study intends to address an adaptive flexible job-shop rescheduling problem with real-time order acceptance (ROA) and condition-based preventive maintenance (CBPM). More precisely, the main innovative works are described as follows: (1) a CBPM policy with both imperfect preventive maintenance (PM) and four inspection strategies is designed to find the optimal maintenance planning for each production machine; (2) a multi-objective optimization model is developed for the concerned problem; and (3) a hybrid multi-objective evolutionary algorithm (HMOEA) with hybrid initialization method, hybrid local search operators and adaptive rescheduling strategies is proposed. In the numerical simulation, the performance and competitiveness of the proposed CBPM policy are first demonstrated by comparing with other maintenance policies. Second, the effectiveness and superiority of parameter setting, order sorting rules, improved operators and overall performance of the proposed algorithm are verified by internal analysis of the algorithm. Third, an adaptive rescheduling strategy pool is constructed by running three rescheduling strategies on all rescheduling scenarios. Finally, a comprehensive sensitivity analysis is performed to illustrate the impact of several critical parameters on the adaptive rescheduling problem, and the results and comparisons show that the proposed HMOEA algorithm and order acceptance strategy have good robustness in most parameters.
•An adaptive flexible job-shop rescheduling problem with new order arrival is studied.•A new condition-based maintenance policy with several inspection schemes is designed.•An integrated multi-objective optimization model with several targets is developed.•An adaptive rescheduling strategy pool with three response methods is constructed.•A hybrid multi-objective evolutionary algorithm with several operators is proposed. |
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| AbstractList | Production scheduling and maintenance planning are two of the most important tasks in the modern manufacturing workshop. Meanwhile, due to the dynamic order arrival and real-time machine monitoring information updating, the integrated optimization of them becoming more complex and meaningful. Therefore, this study intends to address an adaptive flexible job-shop rescheduling problem with real-time order acceptance (ROA) and condition-based preventive maintenance (CBPM). More precisely, the main innovative works are described as follows: (1) a CBPM policy with both imperfect preventive maintenance (PM) and four inspection strategies is designed to find the optimal maintenance planning for each production machine; (2) a multi-objective optimization model is developed for the concerned problem; and (3) a hybrid multi-objective evolutionary algorithm (HMOEA) with hybrid initialization method, hybrid local search operators and adaptive rescheduling strategies is proposed. In the numerical simulation, the performance and competitiveness of the proposed CBPM policy are first demonstrated by comparing with other maintenance policies. Second, the effectiveness and superiority of parameter setting, order sorting rules, improved operators and overall performance of the proposed algorithm are verified by internal analysis of the algorithm. Third, an adaptive rescheduling strategy pool is constructed by running three rescheduling strategies on all rescheduling scenarios. Finally, a comprehensive sensitivity analysis is performed to illustrate the impact of several critical parameters on the adaptive rescheduling problem, and the results and comparisons show that the proposed HMOEA algorithm and order acceptance strategy have good robustness in most parameters.
•An adaptive flexible job-shop rescheduling problem with new order arrival is studied.•A new condition-based maintenance policy with several inspection schemes is designed.•An integrated multi-objective optimization model with several targets is developed.•An adaptive rescheduling strategy pool with three response methods is constructed.•A hybrid multi-objective evolutionary algorithm with several operators is proposed. |
| ArticleNumber | 118711 |
| Author | Chen, Xiaohui Gao, Kaizhou Li, Yinghe Zhang, Lin Zhao, Ziye An, Youjun |
| Author_xml | – sequence: 1 givenname: Youjun orcidid: 0000-0001-5316-2116 surname: An fullname: An, Youjun organization: State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400030, China – sequence: 2 givenname: Xiaohui orcidid: 0000-0003-0268-7558 surname: Chen fullname: Chen, Xiaohui email: chenxiaohui@cqu.edu.cn organization: State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400030, China – sequence: 3 givenname: Kaizhou surname: Gao fullname: Gao, Kaizhou email: gaokaizh@aliyun.com organization: Macau Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and Systems, Macau University of Science and Technology, Macao 999078, China – sequence: 4 givenname: Lin surname: Zhang fullname: Zhang, Lin organization: State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400030, China – sequence: 5 givenname: Yinghe surname: Li fullname: Li, Yinghe organization: State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400030, China – sequence: 6 givenname: Ziye surname: Zhao fullname: Zhao, Ziye organization: State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400030, China |
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| Keywords | Hybrid multi-objective evolutionary algorithm Real-time order acceptance Adaptive flexible job-shop rescheduling problem Condition-based preventive maintenance |
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| SubjectTerms | Adaptive flexible job-shop rescheduling problem Condition-based preventive maintenance Hybrid multi-objective evolutionary algorithm Real-time order acceptance |
| Title | A hybrid multi-objective evolutionary algorithm for solving an adaptive flexible job-shop rescheduling problem with real-time order acceptance and condition-based preventive maintenance |
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