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Network reliability evaluation of manufacturing systems by using a deep learning approach

Author

Listed:
  • Cheng-Fu Huang

    (Feng Chia University)

  • Ding-Hsiang Huang

    (Tunghai University)

  • Yi-Kuei Lin

    (National Yang Ming Chiao Tung University
    Chaoyang University of Technology
    China Medical University Hospital, China Medical University
    Asia University)

  • Yi-Fan Chen

    (National Yang Ming Chiao Tung University)

Abstract

A manufacturing system with reworking actions is constructed as a stochastic-flow manufacturing networks (SFMN) because components (arcs and nodes) are with multi-state capacity. Network reliability is a useful indicator of the performance of an SFMN. It is defined as the probability that that a SFMN can satisfy a given demand. However, the network scale becomes complex in the environment of Industry 4.0 and big data context. The algorithm YKLIN (Lin and Chang in Computers & Industrial Engineering 63:1209–1219, 2012b) cannot calculate network reliability in time for those large cases. For responding network reliability immediately, this paper utilizes an architecture of a deep neural network (DNN) to propose a prediction model for network reliability evaluation. The proposed prediction model can estimate network reliability with a small error (root-mean-square error (RMSE) = 0.0022) in the numerical case. Furthermore, compared to the algorithm YKLIN, the computational time is significantly reduced for a large tile manufacturing system with 14 production lines. In detail, the algorithm YKLIN takes 56.78 s for evaluating network reliability of each data point, whereas the proposed model only takes 0.02 s. The proposed DNN model provides a feasible and efficient approach to achieve network reliability immediately for the real-world manufacturing system in the industry 4.0 environment.

Suggested Citation

  • Cheng-Fu Huang & Ding-Hsiang Huang & Yi-Kuei Lin & Yi-Fan Chen, 2025. "Network reliability evaluation of manufacturing systems by using a deep learning approach," Annals of Operations Research, Springer, vol. 348(1), pages 75-92, May.
    • Handle: RePEc:spr:annopr:v:348:y:2025:i:1:d:10.1007_s10479-022-04911-0
    DOI: 10.1007/s10479-022-04911-0
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    References listed on IDEAS

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