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Energy-efficient on/off control in serial production lines with Bernoulli machines

Author

Listed:
  • Xiaohan Wang

    (School of Automation, Beijing Institute of Technology)

  • Yaping Dai

    (School of Automation, Beijing Institute of Technology)

  • Zhiyang Jia

    (School of Automation, Beijing Institute of Technology)

Abstract

Achieving energy-efficient production has always been a critical issue for the manufacturing industry. In many production systems, the waste of energy mainly results from unbalanced machine efficiency, which brings about a considerable amount of idle time. One of the most important approaches to addressing this problem is to adopt an On/Off control policy on those underutilized machines. In this paper, serial production lines with multiple unreliable machines and finite buffers are considered. All machines are assumed to obey the Bernoulli reliability model, which is commonly used when the machine downtime is relatively short and comparable to the production cycle time (e.g., engine assembly). Besides, considering that in industrial practice, machines usually have to go through a preparation phase during mode switching, a non-negligible warm-up (cool-down) period is taken into account. An On/Off control policy is calculated for lines with two or three machines based on a Markov decision process (MDP) model, then the results are extended to multi-machine lines using a decomposition procedure. Numerical experiments show that the proposed policy achieves better system performance compared to the threshold policy that is commonly used in energy-efficient On/Off control.

Suggested Citation

  • Xiaohan Wang & Yaping Dai & Zhiyang Jia, 2024. "Energy-efficient on/off control in serial production lines with Bernoulli machines," Flexible Services and Manufacturing Journal, Springer, vol. 36(1), pages 103-128, March.
    • Handle: RePEc:spr:flsman:v:36:y:2024:i:1:d:10.1007_s10696-022-09481-8
    DOI: 10.1007/s10696-022-09481-8
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    References listed on IDEAS

    as
    1. Feifan Wang & Feng Ju & Ningxuan Kang, 2019. "Transient analysis and real-time control of geometric serial lines with residence time constraints," IISE Transactions, Taylor & Francis Journals, vol. 51(7), pages 709-728, July.
    2. Ningxuan Kang & Cong Zhao & Jingshan Li & John A. Horst, 2016. "A Hierarchical structure of key performance indicators for operation management and continuous improvement in production systems," International Journal of Production Research, Taylor & Francis Journals, vol. 54(21), pages 6333-6350, November.
    3. Wen Su & Xiaolei Xie & Jingshan Li & Li Zheng & Shaw C. Feng, 2017. "Reducing energy consumption in serial production lines with Bernoulli reliability machines," International Journal of Production Research, Taylor & Francis Journals, vol. 55(24), pages 7356-7379, December.
    4. Zhiyang Jia & Liang Zhang & Jorge Arinez & Guoxian Xiao, 2016. "Performance analysis for serial production lines with Bernoulli Machines and Real-time WIP-based Machine switch-on/off control," International Journal of Production Research, Taylor & Francis Journals, vol. 54(21), pages 6285-6301, November.
    5. Liang Zhang & Chuanfeng Wang & Jorge Arinez & Stephan Biller, 2013. "Transient analysis of Bernoulli serial lines: performance evaluation and system-theoretic properties," IISE Transactions, Taylor & Francis Journals, vol. 45(5), pages 528-543.
    6. Zou, Jing & Chang, Qing & Arinez, Jorge & Xiao, Guoxian, 2017. "Data-driven modeling and real-time distributed control for energy efficient manufacturing systems," Energy, Elsevier, vol. 127(C), pages 247-257.
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