Integrating Operational and Organizational Aspects in Interdependent Infrastructure Network Recovery
Integrating Operational and Organizational Aspects in Interdependent Infrastructure Network Recovery
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DOI：
10.1111/risa.13340
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发表时间：
2019-09-01
期刊：
影响因子：
3.8
通讯作者：
Bedoya-Motta, Claudia D.
中科院分区：
文献类型：
--
作者：
Gomez, Camilo;Gonzalez, Andres D.;Bedoya-Motta, Claudia D.
管理基础设施系统的风险意味着处理相互依存的物理网络及其与自然和社会环境的关系。计算工具通常用于支持旨在提高复原力的业务决策，而与经济学相关的工具往往用于解决基础设施管理中更广泛的社会和政策问题。我们提出了一个基于优化的基础设施弹性分析框架，该框架将组织和社会经济方面纳入运营问题，从而可以了解政策层面决策之间的关系（例如，法规）和技术水平（例如，优化基础设施建设）。我们专注于三个问题时出现的集成这样的水平。首先，由财政和业务因素驱动的最佳恢复战略与由社会经济和人道主义因素驱动的战略相比，其演变方式有所不同。第二，监管方面对复苏动态有重大影响（例如，在机构和监管薄弱的社会，有效恢复最具挑战性，因为个人利益可能损害社会福祉）。第三，决策空间（即，可用行动）在很大程度上取决于灾前决策（例如，资源分配）。建议的优化框架解决这些问题，通过使用：（1）参数分析，以测试优化结果的操作和社会经济因素的影响，（2）监管约束，以模拟和评估的成本和效益（各种演员）执行特定的政策相关的条件恢复过程中，和（3）敏感性分析，以捕捉灾前决策恢复的影响。我们说明了我们的方法与一个例子，在谢尔比县，田纳西州（美国），暴露于自然灾害的相互依赖的水，电，天然气网络的恢复。
Managing risk in infrastructure systems implies dealing with interdependent physical networks and their relationships with the natural and societal contexts. Computational tools are often used to support operational decisions aimed at improving resilience, whereas economics-related tools tend to be used to address broader societal and policy issues in infrastructure management. We propose an optimization-based framework for infrastructure resilience analysis that incorporates organizational and socioeconomic aspects into operational problems, allowing to understand relationships between decisions at the policy level (e.g., regulation) and the technical level (e.g., optimal infrastructure restoration). We focus on three issues that arise when integrating such levels. First, optimal restoration strategies driven by financial and operational factors evolve differently compared to those driven by socioeconomic and humanitarian factors. Second, regulatory aspects have a significant impact on recovery dynamics (e.g., effective recovery is most challenging in societies with weak institutions and regulation, where individual interests may compromise societal well-being). And third, the decision space (i.e., available actions) in postdisaster phases is strongly determined by predisaster decisions (e.g., resource allocation). The proposed optimization framework addresses these issues by using: (1) parametric analyses to test the influence of operational and socioeconomic factors on optimization outcomes, (2) regulatory constraints to model and assess the cost and benefit (for a variety of actors) of enforcing specific policy-related conditions for the recovery process, and (3) sensitivity analyses to capture the effect of predisaster decisions on recovery. We illustrate our methodology with an example regarding the recovery of interdependent water, power, and gas networks in Shelby County, TN (USA), with exposure to natural hazards.