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A Selectively Fuzzified Back Propagation Network Approach for Precisely Estimating the Cycle Time Range in Wafer Fabrication

Author

Listed:
  • Yu-Cheng Wang

    (Department of Aeronautical Engineering, Chaoyang University of Technology, Taichung 41349, Taiwan)

  • Horng-Ren Tsai

    (Department of Information Technology, Lingtung University, Taichung 408213, Taiwan)

  • Toly Chen

    (Department of Industrial Engineering and Management, National Yang Ming Chiao Tung University, 1001, University Road, Hsinchu 30010, Taiwan)

Abstract

Forecasting the cycle time of each job is a critical task for a factory. However, recent studies have shown that it is a challenging task, even with state-of-the-art deep learning techniques. To address this challenge, a selectively fuzzified back propagation network (SFBPN) approach is proposed to estimate the range of a cycle time, the results of which provide valuable information for many managerial purposes. The SFBPN approach is distinct from existing methods, because the thresholds on both the hidden and output layers of a back propagation network are fuzzified to tighten the range of a cycle time, while most of the existing methods only fuzzify the threshold on the output node. In addition, a random search and local optimization algorithm is also proposed to derive the optimal values of the fuzzy thresholds. The proposed methodology is applied to a real case from the literature. The experimental results show that the proposed methodology improved the forecasting precision by up to 65%.

Suggested Citation

  • Yu-Cheng Wang & Horng-Ren Tsai & Toly Chen, 2021. "A Selectively Fuzzified Back Propagation Network Approach for Precisely Estimating the Cycle Time Range in Wafer Fabrication," Mathematics, MDPI, vol. 9(12), pages 1-18, June.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:12:p:1430-:d:577922
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    References listed on IDEAS

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