Managing Physical and Economic Risk for Systems with Multidirectional Network Interdependencies

Managing Physical and Economic Risk for Systems with Multidirectional Network Interdependencies
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管理具有多向网络相互依赖性的系统的物理和经济风险

DOI:
10.1111/risa.13824
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发表时间:
2022
期刊:
影响因子:
3.8
通讯作者:
Thekdi, Shital A.
Thekdi, Shital A.
中科院分区:
医学3区
文献类型:
--
作者:
Tatar, Unal;Santos, Joost R.;Thekdi, Shital A.

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运输和供应链等关键基础设施网络正变得越来越相互依赖。由于网络节点的可操作性依赖于所连接节点的可操作性,因此网络中断有可能蔓延到整个网络,从而对物理网络性能和经济绩效产生灾难性后果。虽然文献中已经对风险知情的物理网络模型和经济模型进行了充分研究,但对于网络性能的物理特征如何与特定部门的经济性能相互作用的研究有限,特别是当这些物理网络从不同持续时间的中断中恢复时。在本文中,我们创建了一个集成功能依赖网络分析 (FDNA) 和动态不可操作输入输出模型 (DIIM) 的通用框架,以评估关键基础设施的中断在一段时间内可能降低其功能的程度。我们使用美国弗吉尼亚州关键交通网络的破坏性场景演示了该框架。我们考虑的场景涉及: (a) 相对较频繁的轻度病例,例如反复出现的交通状况; (b) 涉及延误数小时的事件的中度案例,以及 (c) 相对较少发生的严重案例,例如大飓风后的疏散。研究结果对于寻求投资于网络功能和经济活动风险缓解的网络管理者、政策制定者和利益相关者来说将非常有用。
Critical infrastructure networks, such as transportation and supply chains, are becoming increasingly interdependent. As the operability of network nodes relies on the operability of connected nodes, network disruptions have the potential to spread across entire networks, having catastrophic consequences in the realms of physical network performance and also economic performance. While risk‐informed physical network models and economic models have been well‐studied in the literature, there is limited study of how physical features of network performance interact with sector‐specific economic performance, particularly as these physical networks recover from disruptions of varying durations. In this article, we create a generalizable framework for integrating Functional Dependency Network Analysis (FDNA) and Dynamic Inoperability Input–Output Models (DIIM), to assess the extent to which disruptions to critical infrastructure could degrade its functionality over a period of time. We demonstrate the framework using disruptive scenarios for a critical transportation network in Virginia, USA. We consider scenarios involving: (a) mild case that is relatively more frequent such as recurring traffic conditions; (b) moderate case involving an incident with a multihour delay, and (c) severe case that is relatively less frequent such as evacuation after a major hurricane. The results will be useful for network managers, policymakers, and stakeholders who are seeking to invest in risk mitigation for network functionality and economic activity.
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