Infrastructure networks such as power, communication, gas, water, and transportation rely on one another for their proper functioning. Such infrastructure networks are subject to diverse disruptive events, including random failures, malevolent attacks, and natural disasters, which could significantly affect their performance and adversely impact economic productivity. Moreover, the proliferation of interdependencies among infrastructure networks has increased the complexity associated with recovery planning after a disruptive event. Consequently, providing solution approaches to restore interdependent networks following the occurrence of a disruptive event has attracted many researchers in the last decade. The goal of this paper is to help decision makers plan for recovery following the occurrence of a disruptive event, to procure strategies that center not only on recovering the system promptly, but also such that the weighted average performance of the system is maximized during the recovery process (i.e., enhancing its resilience). Accordingly, this paper studies the interdependent network restoration problem (INRP) and proposes a resilience-driven multi-objective optimization model to solve it. The proposed model aims to: (i) prioritize the restoration of the disrupted components for each infrastructure network, and (ii) assign and schedule the prioritized networks components to the available work crews, such that the resilience of the system of interdependent infrastructure networks is enhanced considering the physical interdependency among them. The proposed model is formulated using mixed-integer programming (MIP) with the objectives of: (i) enhancing the resilience of the system of interdependent infrastructure networks, and (ii) minimizing the total costs associated with the restoration process (i.e., flow, restoration, and disruption costs). Moreover, the proposed model considers partial disruptions and recovery of the disrupted network components, and partial dependence between nodes in different networks. The proposed model is illustrated through a system of interdependent infrastructure networks after multiple hypothetical earthquakes in Shelby County, TN, United States.
Bibliographical noteFunding Information:
This work was supported in part by the National Science Foundation through award 1541165.
© 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature.
- Interdependent networks
ASJC Scopus subject areas
- Computer Networks and Communications
- Artificial Intelligence