RESEARCH HIGHLIGHTS

BOOSTING RESILIENCE OF INTERDEPENDENT CRITICAL INFRASTRUCTURE NETWORKS UNDER UNCERTAINTY

Basem Alkhaleel, Haitao Liao and Kelly Sullivan

Critical infrastructure networks (CINs) for electric power, water distribution, natural gas, transportation, and telecommunications are the backbone of modern societies. However, components in these networks are often vulnerable to disruptive events such as technical accidents, malevolent attacks and natural disasters. These disruptions can reduce overall performance and potentially prevent the networks from providing critical services to society. Therefore, it is of interest to improve the resilience of CINs both by identifying and addressing vulnerabilities before a disruption has occurred and restoring the required services as quickly as possible after the occurrence of a disruption.

Working with Basem Alkhaleel, 2021 doctoral graduate; Haitao Liao and Kelly Sullivan are researching optimization models for post-disruption restoration of CINs. These optimization models assign and schedule components for repair by available work crews to maximize a network resilience metric with the goal of quickly reestablishing the network’s ability to deliver services to users. The models expand upon prior research by incorporating uncertainty in the travel time of repair crews and the time required for repair. Alkahleel, Liao and Sullivan formulate both risk-averse and risk-neutral versions of the problem as stochastic optimization models and implement a wait-and-see solution methodology and a Benders decomposition methodology to solve these models. They validate these models by solving hypothetical test cases using network topology data from the French electrical power network. They demonstrate that the risk-averse model consistently outperforms available deterministic models with regard to resilience with high probability.

Since their initial work, Alkhaleel, Liao and Sullivan have also expanded the optimization model for the purposes of planning restoration of interdependent CINs in which one or more of the networks cannot function without the support of the others. They develop a two-stage mean-risk stochastic restoration model with the goal of minimizing the total cost associated with unsatisfied demands, repair tasks and flow of interdependent CINs. The team demonstrates the model’s capabilities using the water and power networks in Shelby County, TN, under two hypothetical earthquake scenarios. One of their important findings is that the mean-risk stochastic models significantly outperform the deterministic counterparts based on the positive mean-risk value of stochastic solution under all test cases.

This work was sponsored by the U.S. National Science Foundation under Grant No. CMMI-1745353 and OIA-2119691. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. 1

[1] The outcomes of this work are published in the European Journal on Operational Research and Computers & Operations Research.

Illustration of restoration model output for a test case using topology data from the French electrical power network. 1