Book Description

Critical infrastructure networks (CINs) are the backbone of modern societies, which depend on their continuous and proper functioning. Such infrastructure networks are subjected to different types of inevitable disruptive events which could affect their performance unpredictably and have direct socioeconomic consequences. Therefore, planning for disruptions to CINs has recently shifted from emphasizing pre-disruption phases of prevention and protection to post-disruption studies investigating the ability of critical infrastructures (CIs) to withstand disruptions and recover timely from them. However, post-disruption restoration planning often faces uncertainties associated with the required repair tasks and the accessibility of the underlying transportation network. Such challenges are often overlooked in the CIs resilience literature. Furthermore, CIs are not isolated from each other, but instead, most of them rely on one another for their proper functioning. Hence, the occurrence of a disruption in one CIN could affect other dependent CINs, leading to a more significant adverse impact on communities. Therefore, interdependencies among CINs increase the complexity associated with recovery planning after a disruptive event, making it a more challenging task for decision makers. Recognizing the inevitability of large-scale disruptions to CIs and their impacts on societies, the research objective of this work is to study the recovery of CINs following a disruptive event. Accordingly, the main contributions of the following two research components are to develop: (i) resilience-based post-disruption stochastic restoration optimization models that respect the spatial nature of CIs, (ii) a general framework for scenario-based stochastic models covering scenario generation, selection, and reduction for resilience applications, (iii) stochastic risk-related cost-based restoration modeling approaches to minimize restoration costs of a system of interdependent critical infrastructure networks (ICINs), (iv) flexible restoration strategies of ICINs under uncertainty, and (v) effective solution approaches to the proposed optimization models. The first research component considers developing two-stage risk-related stochastic programming models to schedule repair activities for a disrupted CIN to maximize the system resilience. The stochastic models are developed using a scenario-based optimization technique accounting for the uncertainties of the repair time and travel time spent on the underlying transportation network. To assess the risks associated with post-disruption scheduling plans, a conditional value-at-risk metric is incorporated into the optimization models through the scenario reduction algorithm. The proposed restoration framework is illustrated using the French RTE electric power network. The second research component studies the restoration problem for a system of ICINs following a disruptive event under uncertainty. A two-stage mean-risk stochastic restoration model is proposed to minimize the total cost associated with ICINs unsatisfied demands, repair tasks, and flow. The model assigns and schedules repair tasks to network-specific work crews with consideration of limited time and resources availability. Additionally, the model features flexible restoration strategies including a multicrew assignment for a single component and a multimodal repair setting along with the consideration of full and partial functioning and dependencies between the multi-network components. The proposed model is illustrated using the power and water networks in Shelby County, Tennessee, United States, under two hypothetical earthquakes. Finally, some other topics are discussed for possible future work.