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Joint decision-making of parallel machine scheduling restricted in job-machine release time and preventive maintenance with remaining useful life constraints

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  • He, Xinxin
  • Wang, Zhijian
  • Li, Yanfeng
  • Khazhina, Svetlana
  • Du, Wenhua
  • Wang, Junyuan
  • Wang, Wenzhao

Abstract

The machine remaining useful life (RUL), the job-machine release time and the correlation between the maintenance duration and the machine enlistment age are, in this paper, collectively emphasized at the parallel machine scheduling problem. Based on this, a corresponding mixed integer programming model is constructed to minimize the makespan and the processing loss beyond the machine RUL threshold, where a discrete teaching and learning based optimization algorithm is applied to solve this NP-hard problem, and a fault mode-assisted gated recurrent unit (FGRU) life prediction method is used to guide the predictive maintenance initiation time of all machines. In addition, this paper demonstrates that the FGRU method is more accurate than three common methods (Encoder-Decoder Recurrent Neural Network, Bidirectional Long Short-Term Memory and GRU) through two actual bearing degradation cases, and shows through three benchmark cases that the joint decision-making can effectively reduce the time cost of manufacturing enterprises.

Suggested Citation

  • He, Xinxin & Wang, Zhijian & Li, Yanfeng & Khazhina, Svetlana & Du, Wenhua & Wang, Junyuan & Wang, Wenzhao, 2022. "Joint decision-making of parallel machine scheduling restricted in job-machine release time and preventive maintenance with remaining useful life constraints," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    • Handle: RePEc:eee:reensy:v:222:y:2022:i:c:s0951832022000977
    DOI: 10.1016/j.ress.2022.108429
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    Cited by:

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    2. Ta, Yuntian & Li, Yanfeng & Cai, Wenan & Zhang, Qianqian & Wang, Zhijian & Dong, Lei & Du, Wenhua, 2023. "Adaptive staged remaining useful life prediction method based on multi-sensor and multi-feature fusion," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
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    5. Yang, Ningning & Wang, Zhijian & Cai, Wenan & Li, Yanfeng, 2023. "Data Regeneration Based on Multiple Degradation Processes for Remaining Useful Life Estimation," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    6. Rasay, Hasan & Taghipour, Sharareh & Sharifi, Mani, 2022. "An integrated Maintenance and Statistical Process Control Model for a Deteriorating Production Process," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    7. Zhou, Kai-Li & Cheng, De-Jun & Zhang, Han-Bing & Hu, Zhong-tai & Zhang, Chun-Yan, 2023. "Deep learning-based intelligent multilevel predictive maintenance framework considering comprehensive cost," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    8. Ding, Wanmeng & Li, Jimeng & Mao, Weilin & Meng, Zong & Shen, Zhongjie, 2023. "Rolling bearing remaining useful life prediction based on dilated causal convolutional DenseNet and an exponential model," Reliability Engineering and System Safety, Elsevier, vol. 232(C).

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