Impact of heterogeneity on risk propagation in supply chain networks

IF 3.1 3区 物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY
Kaiyuan Liu , Yongxiang Xia , Chengyi Xia , Haicheng Tu
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引用次数: 0

Abstract

In the highly interconnected global economy, risk propagation in supply chain networks has garnered significant attention due to its profound impact. Based on complex network theory, this paper proposes a Degree-Dependent Heterogeneous Risk Propagation (DDHRP) model, in which risk is represented as a binary state within a susceptible–infected–susceptible (SIS) framework: a firm is either infected or susceptible. By use of mean-field equations, we derive the risk propagation threshold and the infection probability under varying basic propagation rates λ0, and analyze how different heterogeneity parameters affect the distribution of infection probability across degrees of nodes. Experimental simulations show that the monotonicity of a node’s infection probability with respect to node degree can be controlled by adjusting model parameters. Moreover, increasing heterogeneity in risk transmission reduces the average infection prevalence across nodes, whereas greater heterogeneity in risk recovery increases it. Our research not only offers a heterogeneous risk propagation model adapted for supply chain networks, but also provides a theoretical foundation for risk early warning and control.
异质性对供应链网络风险传播的影响
在高度互联的全球经济中,供应链网络中的风险传播因其深远的影响而引起了人们的广泛关注。基于复杂网络理论,提出了一种程度依赖的异质风险传播(DDHRP)模型,该模型将风险表示为易感-感染-易感(SIS)框架中的二元状态:企业要么被感染,要么易感。利用均场方程,导出了不同基本传播速率λ0下的风险传播阈值和感染概率,分析了不同异质性参数对节点感染概率分布的影响。实验仿真表明,通过调整模型参数可以控制节点感染概率对节点度的单调性。此外,风险传播异质性的增加降低了节点间的平均感染率,而风险恢复异质性的增加则增加了平均感染率。本研究不仅提供了一种适合供应链网络的异质性风险传播模型,而且为风险预警和控制提供了理论基础。
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来源期刊
CiteScore
7.20
自引率
9.10%
发文量
852
审稿时长
6.6 months
期刊介绍: Physica A: Statistical Mechanics and its Applications Recognized by the European Physical Society Physica A publishes research in the field of statistical mechanics and its applications. Statistical mechanics sets out to explain the behaviour of macroscopic systems by studying the statistical properties of their microscopic constituents. Applications of the techniques of statistical mechanics are widespread, and include: applications to physical systems such as solids, liquids and gases; applications to chemical and biological systems (colloids, interfaces, complex fluids, polymers and biopolymers, cell physics); and other interdisciplinary applications to for instance biological, economical and sociological systems.
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