• CN: 11-2187/TH
  • ISSN: 0577-6686

Journal of Mechanical Engineering ›› 2026, Vol. 62 ›› Issue (11): 162-170.doi: 10.3901/JME.260440

Previous Articles    

Resilience Assessment and Design Method of 3C Product Manufacturing Networks for SME Collaboration

ZHANG Ding1, LIN Zexiong1, LIU Yu2, LI Xing3, JI Xiaojun4, LAI Peiyuan5   

  1. 1. National Key Laboratory of Precision Electronic Manufacturing, Guangdong University of Technology, Guangzhou 510016;
    2. School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641;
    3. School of Telecommunication Engineering and Intelligence, Dongguan University of Technology, Dongguan 523808;
    4. Kingdee Software (China) Co., Ltd., Shenzhen 518057;
    5. South China Technology Commercialization Center, Guangzhou 511458
  • Received:2025-07-01 Revised:2026-01-10 Published:2026-07-29

Abstract: The rapid iteration of 3C products, coupled with frequent disruptions from technological sanctions and tariff policies, has significantly increased the variability of manufacturing network systems. Manufacturing resilience, which reflects a system’s ability to withstand abnormal disruptive events, has garnered growing attention in recent years. Following a path of resilience modeling, evaluation, and design for product manufacturing networks, a state-space representation method based on max-plus algebra is proposed within the framework of discrete event dynamic systems. This approach enables dynamic modeling of manufacturing networks with material kitting constraints. System resilience is quantified based on capacity losses caused by disruptive events. Considering resilience as one of the objectives, along with manufacturing efficiency and configuration cost, a genetic algorithm is employed to optimize the configuration of the manufacturing network. The proposed theory and method are applied and validated in a smartphone manufacturing case. This approach could provide theoretical guidance and technical support for configuring manufacturing networks in regional small and medium-sized enterprise clusters.

Key words: collaborative manufacturing, manufacturing network resilience, dynamic modeling, resilience evaluation, max-plus algebra, configuration design

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