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Unearthing vulnerability of supply provision in logistics networks to the black swan events: Applications of entropy theory and network analysis

机译：物流网络向黑色天鹅事件提供供应规定的脆弱性：熵理论和网络分析的应用

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摘要

Over the past two decades, the fragility of modern Logistics Networks (LNs) has been exposed by unexpected events with extreme impacts outside the realm of regular expectations, which are known as the black swan events. As a response to the growing dysfunctions of the global LNs due to the detrimental effects of the black swan events, this article develops a quantitative vulnerability assessment method by paying simultaneous attention to various structural properties of these networks. It adopts three prototypical examples of centrality indices known as betweenness, closeness, and eigenvector centrality. This work also develops a vulnerability index from the joint entropy of centrality values, which can be interpreted as a certain extent of risk to disruptions in supply provision in LNs. The proposed entropy-based vulnerability index enables a far more accurate analysis of vulnerability by measuring the degree of homogeneity and heterogeneity of centrality values. The suggested index is considered as an objective function that must be minimized to reduce the deleterious effects of the black swan events. To illustrate the proposed vulnerability assessment method, this paper analyzes a real-world global maritime network, concluding that the juxtaposition of centrality measures provides a richer insight into the vulnerability analysis of LNs. The results also favor the fact that increasing the homogeneity of a network through decentralization yields lower vulnerability and builds extra robustness into the networks.
机译：在过去的二十年中，现代物流网络（LNS）的脆弱性已经受到常规期望领域外面的极端影响的意外事件，称为黑天鹅事件。由于对全球LNS不断增长的功能障碍导致的黑色天鹅事件的不利影响，本文通过对这些网络的各种结构特性进行了同时关注这些网络的各种结构性能来发展定量漏洞评估方法。它采用三个被称为性，近，特征向量中心的中心形指数的三种原型示例。这项工作还开发了来自中心价值的联合熵的脆弱性指数，这可以被解释为LNS中供应规定中断的一定程度。所提出的基于熵的漏洞指数通过测量中心值的均匀性和异质性来实现对脆弱性的更准确分析。建议的指数被认为是必须最小化的目标函数，以减少黑天鹅事件的有害影响。为了说明拟议的脆弱性评估方法，本文分析了一个真实世界的全球海上网络，得出结论，中心性措施的并置对LNS的脆弱性分析提供了更丰富的洞察力。结果还支持通过分散化增加网络的同质性产生更低的脆弱性，并将额外的稳健性建立在网络中。

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