Risk and resilience-based optimal post-disruption restoration for critical infrastructures under uncertainty
Risk and resilience-based optimal post-disruption restoration for critical infrastructures under uncertainty
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DOI：
10.1016/j.ejor.2021.04.025
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发表时间：
2021-08-27
影响因子：
6.4
通讯作者：
Sullivan, Kelly M.
中科院分区：
文献类型：
--
作者：
Alkhaleel, Basem A.;Liao, Haitao;Sullivan, Kelly M.
关键基础设施(CI)的中断后恢复经常面临与所需维修任务和相关运输网络相关的不确定性。然而，在大多数关于提高CI韧性的研究中，这些挑战往往被忽视。本文提出了两阶段风险厌恶和风险中性的随机优化模型，以最大化系统弹性为目标，对中断的CI网络进行维修活动调度。这两个模型都是基于基于情景的优化技术开发的，该技术考虑了维修时间和在基础交通网络上花费的旅行时间的不确定性。考虑到与中断后恢复任务相关的大量不确定性实现，提出了一种基于观望解决方案的改进快进算法，以减少所选场景的数量，从而得到期望的概率性能指标。为了评估与中断后调度计划相关的风险，通过情景归约算法将条件风险值(CVAR)指标纳入优化模型。将所提出的恢复框架应用于具有直流潮流过程的法国RTE电网，结果表明了考虑与修复活动相关的行程时间的随机优化模型的附加值。在不确定的情况下，规避风险的决策在很大程度上影响最优计划和预期的弹性，特别是在最坏的情况下，这一点至关重要。(C)2021年爱思唯尔B.V.保留所有权利。
Post-disruption restoration of critical infrastructures (CIs) often faces uncertainties associated with the required repair tasks and the related transportation network. However, such challenges are often overlooked in most studies on the improvement of CI resilience. In this paper, two-stage risk-averse and risk-neutral stochastic optimization models are proposed to schedule repair activities for a disrupted CI network with the objective of maximizing system resilience. Both models are developed based on a scenario-based optimization technique that accounts for the uncertainties of the repair time and the travel time spent on the underlying transportation network. Given the large number of uncertainty realizations associated with post-disruption restoration tasks, an improved fast forward algorithm based on a wait-and-see solution methodology is provided to reduce the number of chosen scenarios, which results in the desired probabilistic performance metrics. To assess the risks associated with post-disruption scheduling plans, a conditional value-at-risk (CVaR) metric is incorporated into the optimization models through a scenario reduction algorithm. The proposed restoration framework is applied to the French RTE electric power network with a DC power flow procedure, and the results demonstrate the added value of using the stochastic optimization models incorporating the travel times related to repair activities. It is essential that risk-averse decision-making under uncertainty largely impacts the optimum schedule and the expected resilience, especially in the worst-case scenarios. (c) 2021 Elsevier B.V. All rights reserved.