In the context of supply chain globalization, this paper applies complex network theory to construct automotive supply chain networks. The fundamental characteristics of the automotive supply chain are examined, and an improved artificial fish swarm algorithm-based key node identification method is proposed to address the limitations of existing approaches for complex networks. Following optimization and enhancement, the identification results are tested using the vandalism resistance of the automotive supply chain network as a metric. The analysis of the results reveals that the key nodes with the most significant impact on risk propagation in the automotive supply chain network are primarily the core automotive manufacturing enterprises, with certain auto parts enterprises also playing a crucial role. The proposed key node identification method offers a more intuitive means of identifying potential key risk control nodes within the automotive supply chain network, with valuable practical applications for preventing risk propagation in the automotive supply chain.


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    Titel :

    Key Node Identification of the Automotive Supply Chain Network Based on Improved AFSA


    Beteiligte:
    Ou, Chengyang (Autor:in) / Pan, Fubin (Autor:in) / Chen, Minwei (Autor:in) / Lin, Shuangjiao (Autor:in)


    Erscheinungsdatum :

    04.11.2023


    Format / Umfang :

    512199 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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