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
Hlavní autoři: An, Youjun, Chen, Xiaohui, Gao, Kaizhou, Zhang, Lin, Li, Yinghe, Zhao, Ziye
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.02.2023
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ISSN:0957-4174, 1873-6793
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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.
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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Snippet Production scheduling and maintenance planning are two of the most important tasks in the modern manufacturing workshop. Meanwhile, due to the dynamic order...
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StartPage 118711
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
URI https://dx.doi.org/10.1016/j.eswa.2022.118711
Volume 212
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