Resource allocation frameworks for resilience management of interdependent infrastructure network
Throughout their lifetime, critical infrastructure networks are exposed to risks from external hazards and threats, such as natural hazards, terrorist attacks, and cyber-attacks. Since modern urban infrastructure systems are highly interdependent on each other, the risks of infrastructure failures due to extreme events are far more extensive in geographic scale and intensity. Given that traditional resource allocation methods are based primarily on the performance of individual infrastructure facilities without considering interdependencies among them, the resulting resilience of the infrastructure network is not always optimal. In this study, methodological frameworks are proposed to allocate resources (budget or personnel) to improve the resilience of the infrastructure network considering interdependencies among infrastructure systems. Taking advantage of Agent-Based Modeling (ABM) technique, the proposed methodology frameworks are able to assess the interdependent effects of extreme events and resource allocation strategies. By simulating the interdependent effects with ABM and using the results as inputs to the optimization process, the optimization process for maximizing the infrastructure network resilience is improved. Specifically, the objectives of this study include: 1) maximizing the robustness of the infrastructure network before an extreme event; 2) optimizing the repair crew allocation after an extreme event; and 3) optimizing the long-term infrastructure network resilience when the infrastructure network is potentially subject to multiple occurrences of extreme events. The proposed methodology frameworks are applied to simplified networks consisting of different critical infrastructure systems in order to understand the applicability of the frameworks. The results show that the proposed frameworks are effective in quantifying the interdependent effects of the infrastructure network and improving its resilience.