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Synchronization Optimization Model Based on Enhanced Connectivity of New Energy Vehicle Supply Chain Network

Author

Listed:
  • Haiwei Gao

    (School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Xiaomin Zhu

    (School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Binghui Guo

    (LMIB and NLSDE, School of Artificial Intelligence, Beihang University, Beijing 100191, China
    Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beijing 100191, China
    Zhongguancun Laboratory, Beijing 100191, China
    Peng Cheng Laboratory, Shenzhen 518055, China)

  • Yifan Cao

    (School of Artificial Intelligence, Beihang University, Beijing 100191, China)

  • Haotian Wang

    (School of Software, Beihang University, Beijing 100191, China)

  • Xiaohan Yu

    (School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Xiaobo Yang

    (School of Food and Health, Beijing Technology and Business University, Beijing 100048, China
    National Engineering Research Centre for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China)

Abstract

The synchronization of the new energy vehicle (NEV) supply chain network is crucial for enhancing industrial integration, building intelligent supply chain systems, and promoting sustainable development. This study proposes a novel synchronization model for the NEV supply chain network, incorporating a technical method for measuring synchronization intervals. The research makes three key contributions: (1) development of a dynamic synchronization model capturing the complex interactions within NEV supply chains; (2) introduction of a quantitative method for assessing synchronization intervals; and (3) identification of critical parameters influencing network synchronization. Methodologically, we employ a combination of complex network theory and nonlinear dynamic systems to construct the synchronization model. The study utilizes real-world data from two major NEV companies (X and T) to validate the model’s effectiveness. Through network topology analysis and parameter optimization, we demonstrate significant improvements in supply chain efficiency and resilience. The practical application of this research lies in its ability to provide actionable insights for supply chain management. By optimizing network structure, coupling strength, and information delay, companies can enhance synchronization, reduce the bullwhip effect, and improve overall supply chain performance. The findings offer valuable guidance for NEV manufacturers and policymakers in building more resilient and efficient supply chain networks in the rapidly evolving automotive industry.

Suggested Citation

  • Haiwei Gao & Xiaomin Zhu & Binghui Guo & Yifan Cao & Haotian Wang & Xiaohan Yu & Xiaobo Yang, 2025. "Synchronization Optimization Model Based on Enhanced Connectivity of New Energy Vehicle Supply Chain Network," Mathematics, MDPI, vol. 13(4), pages 1-46, February.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:4:p:632-:d:1591540
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    References listed on IDEAS

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    1. Li, Yuhong & Zobel, Christopher W., 2020. "Exploring supply chain network resilience in the presence of the ripple effect," International Journal of Production Economics, Elsevier, vol. 228(C).
    2. Mi Zhou & Xiangdong Li & Yangyan Shi, 2024. "Study on the Resilience Measurement of the New Energy Vehicle Industry Chain," Sustainability, MDPI, vol. 16(12), pages 1-28, June.
    3. Liu, Jun & Chang, Huihong & Forrest, Jeffrey Yi-Lin & Yang, Baohua, 2020. "Influence of artificial intelligence on technological innovation: Evidence from the panel data of china's manufacturing sectors," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    4. Edi Lukin & Aleksandra Krajnović & Jurica Bosna, 2022. "Sustainability Strategies and Achieving SDGs: A Comparative Analysis of Leading Companies in the Automotive Industry," Sustainability, MDPI, vol. 14(7), pages 1-15, March.
    5. Sergey V. Buldyrev & Roni Parshani & Gerald Paul & H. Eugene Stanley & Shlomo Havlin, 2010. "Catastrophic cascade of failures in interdependent networks," Nature, Nature, vol. 464(7291), pages 1025-1028, April.
    6. Alexandre Dolgui & Dmitry Ivanov, 2021. "Ripple effect and supply chain disruption management: new trends and research directions," International Journal of Production Research, Taylor & Francis Journals, vol. 59(1), pages 102-109, January.
    7. Liu, Wei & Li, Xin & Liu, Chunyan & Wang, Minxi & Liu, Litao, 2023. "Resilience assessment of the cobalt supply chain in China under the impact of electric vehicles and geopolitical supply risks," Resources Policy, Elsevier, vol. 80(C).
    8. Wu, Yunna & Jia, Weibing & Li, Lingwenying & Song, Zixin & Xu, Chuanbo & Liu, Fangtong, 2019. "Risk assessment of electric vehicle supply chain based on fuzzy synthetic evaluation," Energy, Elsevier, vol. 182(C), pages 397-411.
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