A Two-Stage Restoration Resource Allocation Model for Enhancing the Resilience of Interdependent Infrastructure Systems

Abstract

Infrastructure systems play a critical role in delivering essential services that are important to the economy and welfare of society. To enhance the resilience of infrastructure systems after a large-scale disruptive event, determining where and when to invest restoration resources is a challenge for decision makers. Comprehensively considering the recovery time of infrastructure systems and the overall losses resulting from a disaster, this study proposes a two-stage restoration resource allocation model for enhancing the resilience of interdependent infrastructure systems. First, to evaluate the effect of resource allocation during the recovery process, dynamic resilience is selected as the criterion for the recovery of infrastructure systems. Second, taking into consideration the decision makers’ point of view, a two-stage resource allocation model is proposed. The objective of the first stage is to quickly recover the infrastructure systems’ dynamic resilience to meet the basic needs of the users. The second stage is aimed at minimizing the overall losses in the following recovery process. The effects of infrastructure interdependencies on resource allocation are incorporated in the model using the dynamic inoperability input–output model. Through a case study, the proposed approach is compared with other resource allocation strategies. The results show that: (1) the restoration resource allocation strategy obtained from the proposed approach balances the recovery time and the overall losses to infrastructure systems; and (2) the value of the usage cost of the unit restoration resource has a significant impact on the recovery time and the overall losses under different strategies. The proposed model is both effective and efficient in solving the post-disaster resource allocation problem and can provide decision makers with scientific decision support.

Decentralized resource allocation for interdependent infrastructures resilience: a cooperative game approach

“…A rich stream of the literature for resource allocation optimization problems in interdependent infrastructure networks relies on assumptions of centralized decision making (Cavdaroglu et al, 2013;Sharkey et al, 2015;Rong et al, 2018;Zhang et al, 2018;Kong et al, 2019). For instance, Cavdaroglu et al (2013) proposed mathematical models and efficient optimization algorithms that integrate the restoration and planning decisions for interdependent systems.…”

“…One approach to maximize resilience is to simply make the resilience metric the focus of the optimization model (Zhang et al, 2018). More complex models can be conceptualized and capture different aspects of the problem, such as developing a two-stage optimization model that first restores the minimum level of service before minimizing the losses in the network (Kong et al, 2019). Although this setting is more realistic, the associated complexity increases the amount of required information for the decision maker to plan.…”

A resource allocation framework for predisaster resilience management of interdependent infrastructure networks

“…There are studies related to allocate resources for infrastructure resilience optimization with the consideration of the infrastructure interdependencies (Kong et al, 2019;Pumpuni-Lenss et al, 2017;Sun and Zhang, 2020;Zhang et al, 2018). All these studies focus on allocating resources after an extreme event to minimize the restoration cost or expedite the recovery speed.…”

Resilience Assessment of Interdependent Infrastructure Systems: A Case Study Based on Different Response Strategies

“…(ii) Street-power grid: Nodes of street and power supply networks located in the same geographic area have geographical dependence and use of Equation (5). Power plants need fuel (such as coal) from outside, which means node-path dependence pattern and use of Equation (4).…”

“…(vi) Water supply-information infrastructure network: Nodes of water supply and information networks are located in the same geographic area, which means geographical dependence and use of Equation (5).…”