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A risk-graded propagation and dynamic recovery algorithm for optimizing supply chain networks

Author

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  • Wu, Leilei
  • Ding, Xixi
  • Bai, Guanghan
  • Yu, Liyan
  • Xia, Yongxiang

Abstract

Supply chain networks are highly susceptible to systemic failures when faced with localized risk shocks due to their complexity and high interconnectivity. In this paper, a multi-state model for supply chain risk transmission and recovery is constructed. By introducing hierarchical infection probabilities (α(n,f)) and recovery probabilities (μ(n,f,R)), the node states are dynamically updated, taking into full account node attributes such as risk levels, node fitness, and recovery costs. Notably, a handling strategy for node failures is designed. When a node enters the failure state (the D state), the impacts on the network topology and the states of neighboring nodes are analyzed in detail. Corresponding measures are taken, including removing the failed node and its connections and updating the states of neighboring nodes based on their degrees and impact factors. At the same time, a reconnection mechanism for healthy nodes is designed to reduce network fragmentation and maintain network connectivity and stability. Through simulations and empirical data analysis, the model effectively limits the spread of risk, reduces the failure rate of high-risk nodes, and enhances the overall connectivity of the network. It performs well in different network topologies, providing valuable insights and practical tools for optimizing risk management in complex supply chains.

Suggested Citation

  • Wu, Leilei & Ding, Xixi & Bai, Guanghan & Yu, Liyan & Xia, Yongxiang, 2025. "A risk-graded propagation and dynamic recovery algorithm for optimizing supply chain networks," Chaos, Solitons & Fractals, Elsevier, vol. 197(C).
    • Handle: RePEc:eee:chsofr:v:197:y:2025:i:c:s0960077925004904
    DOI: 10.1016/j.chaos.2025.116477
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    References listed on IDEAS

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