A Mathematical Framework to Optimize Critical Infrastructure Resilience against Intentional Attacks

Abstract

This article establishes a tri-level decisionmaking model supporting critical infrastructure (CI) resilience optimization against intentional attacks. A novel decomposition algorithm is proposed to exactly identify the best pre-event defense strategy (protecting vulnerable components and building new lines), the worst-case attack scenario, and the optimal postevent repair sequence of damaged components. As different types of CIs have different flow models, this article mainly considers the direct current power flow model and the maximal flow model for illustrative purposes. The proposed framework is illustrated by a simple but representative case system with nine nodes, and main results include: (1) the marginal value of extra defense investment under low defense budget is more considerable for mitigating system resilience loss, especially under large intentional attacks; (2) no defense strategy is always the best under different attack budgets; (3) increasing amount of repair resources can dramatically enhance CI resilience, but makes the pre-event defense strategy less effective; (4) the use of maximal flow model can provide a lower bound estimation of the resilience loss from the power flow model; (5) the optimum defense strategy and the worst-case attack identified by minimizing CI resilience loss largely differ from those by minimizing CI vulnerability, where the latter does not consider the re- * To whom correspondence should be addressed. E-mail: fangy@ ethz.ch. covery actions. Finally, the algorithm complexity is analyzed by comparing with the enumeration method and by testing two larger electric power systems. C 2017 Computer-Aided Civil and Infrastructure Engineering.

Influence of overloading on the reliability and critical components of networked critical infrastructures

“…The multiobjective evolutionary algorithm, MO‐PSDA, was implemented to deal with the complexity of the proposed problem. The obtained solutions can help in identifying a network's vulnerability and guide the decision maker to conduct further optimization of critical infrastructures, such as reliability redundancy allocation and inspection, while considering the possibility of overloading and its influence.…”

“…The multiobjective evolutionary algorithm, MO-PSDA, was implemented to deal with the complexity of the proposed problem. The obtained solutions can help in identifying a network's vulnerability and guide the decision maker to conduct further optimization of critical infrastructures 43,44 , such as reliability redundancy allocation and inspection, while considering the possibility of overloading and its influence. As a first step towards more practically studying overloading influence on networked infrastructures, this research has assumed that there is no restriction for the survived components in sharing the load of the failed components.…”

Alternative Resilience Indices for City Ecosystems Subjected to Natural Hazards

“…For instance, in the proposed case study the urban connectivity between residential buildings is considered and named as building-to-network efficiency. This is because of the proposed network model being connectivity-based (Ouyang and Fang, 2017;Cavalieri et al, 2014;Cavallaro et al, 2014;Franchin and Cavalieri, 2015;Bettencourt and West, 2010). Hence, social agents are not engaging dynamically with the physical network as assumed in recent agent-based approaches (Reilly et al, 2015;Sharkey et al, 2015;Gómez et al, 2014;Nejat and Damnjanovic, 2012;Zhang et al, 2005).…”

A Boolean Networks Approach to Modeling and Resilience Analysis of Interdependent Critical Infrastructures

“…It interrelates with the absorptive, adaptive, and restorative capacity indicators studied in Vugrin et al (2013). Moreover, a number of novel contributions are addressing aspects such as the development of resilience indexes and optimization techniques, see, for instance, Bozza et al (2017), Ouyang (2017), Ouyang and Fang (2017), and Wang et al (2017). Resilience analysis and enhancement also display interesting relationships with sustainability and sustainable design (Redman, 2014;Wang and Adeli, 2014;Xu et al, 2015;Rafiei and Adeli, 2016;Wang et al, 2017).…”

“…Moreover, a number of novel contributions are addressing aspects such as the development of resilience indexes and optimization techniques, see, for instance, Bozza et al. (), Ouyang (), Ouyang and Fang (), and Wang et al. ().…”