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<p>As global supply chains become increasingly complex, the propagation of risks within supply chain networks has garnered growing attention. Drawing on stochastic process theory, this study addresses the dynamic characteristics of operational state changes in supply chain firms during risk propagation. A dynamic simulation framework is constructed by integrating cellular automata with epidemiological models, and optimal control strategies are derived using Pontryagin’s Maximum Principle. The results show that: (1) When the hesitancy and infection rates among susceptible firms decline, or when the recovery and immunization rates among infected firms increase within certain thresholds, risks in the supply chain network will gradually dissipate. (2) In the absence of control strategies, vertical-type supply chain networks experience faster risk diffusion. Among the evaluated strategies, the full joint control strategy proves most effective, followed by the propagation path regulation strategy, while the semi joint control strategy shows limited effectiveness. Under resource constraints, priority should be given to the propagation path regulation strategy. Dense-type supply chain networks can inhibit the spread of risk by increasing the infection buffering rate, whereas vertical-type networks are more effective in doing so by enhancing the infection recovery rate. (3) Spatial simulations indicate that dense-type networks can mitigate risk by reducing the clustering of hesitant nodes. In contrast, vertical-type networks require precise early control of infected firms due to their weaker recovery capacity. These findings offer both theoretical insights and practical strategies for enhancing supply chain resilience in complex risk scenarios.</p>

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Nonlinear dynamics of risk propagation in supply chains: a stochastic SEIR-cellular automaton approach

  • 期刊论文

  • 发表日期：

    2025年7月16日

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  • Jinrong Ma,
  • Qiaoming Hou

摘要

As global supply chains become increasingly complex, the propagation of risks within supply chain networks has garnered growing attention. Drawing on stochastic process theory, this study addresses the dynamic characteristics of operational state changes in supply chain firms during risk propagation. A dynamic simulation framework is constructed by integrating cellular automata with epidemiological models, and optimal control strategies are derived using Pontryagin’s Maximum Principle. The results show that: (1) When the hesitancy and infection rates among susceptible firms decline, or when the recovery and immunization rates among infected firms increase within certain thresholds, risks in the supply chain network will gradually dissipate. (2) In the absence of control strategies, vertical-type supply chain networks experience faster risk diffusion. Among the evaluated strategies, the full joint control strategy proves most effective, followed by the propagation path regulation strategy, while the semi joint control strategy shows limited effectiveness. Under resource constraints, priority should be given to the propagation path regulation strategy. Dense-type supply chain networks can inhibit the spread of risk by increasing the infection buffering rate, whereas vertical-type networks are more effective in doing so by enhancing the infection recovery rate. (3) Spatial simulations indicate that dense-type networks can mitigate risk by reducing the clustering of hesitant nodes. In contrast, vertical-type networks require precise early control of infected firms due to their weaker recovery capacity. These findings offer both theoretical insights and practical strategies for enhancing supply chain resilience in complex risk scenarios.