计算机工程与应用 ›› 2024, Vol. 60 ›› Issue (8): 296-308.DOI: 10.3778/j.issn.1002-8331.2306-0327

• 工程与应用 • 上一篇    下一篇

突发公共事件下复杂供应链网络风险传播与干预

姜林,梁竞心   

  1. 重庆邮电大学 现代邮政学院,重庆 400065
  • 出版日期:2024-04-15 发布日期:2024-04-15

Risk Propagation and Intervention in Complex Supply Chain Networks During Unforeseen Public Events

JIANG Lin, LIANG Jingxin   

  1. School of Modern Posts, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
  • Online:2024-04-15 Published:2024-04-15

摘要: 突发公共事件给供应链网络的稳定运行带来极大冲击,如何提升供应链网络应对突发公共事件的能力,保持其坚韧性与稳定性,具有重要意义。结合突发公共事件的特性,基于改进的SEIR系统动力学传播模型,构建政府干预下复杂供应链网络的风险传播模型,并利用基本再生数的理论方法,分析风险传播的阈值以及平衡点问题,探究政府干预对阻断供应链风险传播的积极作用,从宏观层面分析供应链风险的传播机理以及干预措施,得出政府的风险管控方向。理论研究及仿真实验的结果均表明,政府通过控制基本再生数,能有效遏制风险进一步蔓延与扩散,达到控制风险的目标。

关键词: 突发公共事件, 供应链风险, 系统动力学传播模型, 基本再生数

Abstract: Unforeseen public events present substantial challenges to the stable operation of supply chain networks. It is of paramount importance to enhance the capacity of these networks to respond to unforeseen public events while preserving their resilience and stability. In consideration of the unique characteristics of such events, this paper formulates a risk propagation model for intricate supply chain networks under the influence of government intervention, utilizing an enhanced SEIR system dynamics transmission model. It employs the basic reproduction number theory to scrutinize the thresholds and equilibrium points in risk propagation, delves into the constructive role of government intervention in disrupting the proliferation of supply chain risks, and furnishes a macro-level analysis of the mechanisms governing risk propagation within supply chains and the accompanying intervention strategies, ultimately offering directives for government risk management. Both theoretical research and simulation experiments concur in affirming that, through the manipulation of the basic reproduction number, the government can effectively curtail the further expansion and diffusion of risks, thereby attaining the objective of risk containment.

Key words: unforeseen public events, supply chain risk, system dynamics propagation model, basic reproduction number