Integrated optimization of condition-based preventive maintenance and production rescheduling with multi-phase processing speed selection and old machine scrap

Youjun An, Xiaohui Chen, Jiawen Hu, Lin Zhang and Ziye Zhao

Reliability Engineering and System Safety, 2023, vol. 238, issue C

Abstract: With the usage and aging of machine, condition-based preventive maintenance (CBPM) and old machine scrap are two common phenomena in the actual production, and the latter may lead to the original production-maintenance planning no longer available. Under this context, this paper addresses an integrated optimization problem of CBPM and production rescheduling with multi-phase processing speed selection and old machine scrap. More precisely, (1) a CBPM policy with sixteen inspection strategies and multi-phase processing speed selection is proposed to find some selectable maintenance plans for each machine; (2) a hybrid rescheduling strategy (HRS) is designed for responding to the dynamic event, and a rescheduling strategy is adaptively selected according to the average utilization rate (A1) of idle time of existing machines; and (3) an adaptive clustering-based bi-population co-evolutionary algorithm (ACBCA) is developed to solve the studied problem. In the numerical simulation, Taguchi method is first employed to find the optimal parameter setting for the proposed ACBCA. Second, the effect of predefined threshold of A1 is analyzed, and the optimal value is 0.3. Next, the superiority and competitiveness of the proposed ACBCA, CBPM policy and HRS are all demonstrated by comparing with other algorithms, CBPM policies and rescheduling strategies, respectively.

Keywords: Condition-based preventive maintenance; Flexible job-shop rescheduling; Multi-phase processing speed selection; Old machine scrap; Adaptive clustering-based bi-population co-evolutionary algorithm (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:238:y:2023:i:c:s0951832023003137

DOI: 10.1016/j.ress.2023.109399

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