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Blockchain-Based Long-Term Capacity Planning for Semiconductor Supply Chain Manufacturers

Author

Listed:
  • Jian Yang

    (School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China)

  • Jichang Dong

    (School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China
    Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Beijing 100190, China)

  • Suixiang Gao

    (School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
    Zhongguancun Laboratory, Beijing 100194, China)

  • Guoqing Wang

    (School of Engineering Science, University of Chinese Academy of Sciences, Beijing 100190, China)

Abstract

The long-term production capacity planning of semiconductor supply chain manufacturers has a series of characteristics, such as large capital investment, fast technology upgrading, long lead-time of manufacturing equipment, and unstable market environment, which leads to the uncertainty of demand. In addition, blockchain technology is widely used to build consortium chains for information sharing across upstream and downstream enterprises, thus having the potential ability to help finance semiconductor manufacturers. This paper combines two uncertainty-oriented methods (stochastic programming and robust optimization) to examine the conversion of capacity between different product types and construct a two-stage mathematical planning model maximizing the net profit of manufacturers. Through the introduction of blockchain technology and information sharing among enterprises, we improve the effectiveness of our model to realize the optimal allocation of long-term capacity planning. Finally, we reformulate the model into a tractable MINLP, construct numerical examples to verify the solvability, and carry out sensitivity analysis.

Suggested Citation

  • Jian Yang & Jichang Dong & Suixiang Gao & Guoqing Wang, 2023. "Blockchain-Based Long-Term Capacity Planning for Semiconductor Supply Chain Manufacturers," Sustainability, MDPI, vol. 15(6), pages 1-20, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:6:p:4748-:d:1090264
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    References listed on IDEAS

    as
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