Abstract
Due to the ubiquitous nature of disruptive extreme events, functionality of the critical infrastructure systems (CIS) is constantly at risk. In case of a disruption, in order to minimize the negative impact to the society, service networks operating on the CIS should be restored as quickly as possible. In this paper, we introduce a novel network science inspired measure to quantify the criticality of components within a disrupted service network and develop a restoration heuristic (Cent-Restore) that prioritizes restoration efforts based on this measure. As an illustrative case study, we consider a road network blocked by debris in the aftermath of a natural disaster. The debris obstructs the flow of relief aid and search-and-rescue teams between critical facilities and disaster sites, debilitating the emergency service network. In this context, the problem is defined as finding a schedule to clear the roads with the limited resources. First, we develop a mixed-integer programming model for the problem. Then we validate the efficiency and accuracy of the Cent-Restore heuristic on randomly generated instances by comparing it to the model. Furthermore, we use Cent-Restore to recommend real-time restoration plans for disrupted road networks of Boston and Manhattan and analyze the performance of the plans over time through resilience curves. We compare Cent-Restore to the current restoration guidelines proposed by FEMA and other strategies that prioritize the restoration efforts based on different measures. As a result we confirm the importance of including specific post-disruption attributes of the networks to create effective restoration strategies. Moreover, we explore the relationship between a service network’s resilience and its topological and operational characteristics under different disruption scenarios. The methods and insights provided in this work can be extended to other disrupted large-scale critical infrastructure systems in which the ultimate goal is to enable the functions of the overlaying service networks.
Highlights
Critical infrastructure systems (CIS) underpin almost every aspect of the modern society by providing the essential functions through overlaying service networks
There are two subsets in the node set N is divided into three subsets: NS indicates the set of supply nodes, critical facilities such as hospitals, distribution centers, shelters, ND indicates the set of demand nodes, disaster sites composed of the people in need for relief commodities and services, and the remaining nodes that are neither a supply nor a demand node
At each iteration CPLEX finds two values: (i) an upper bound (UB) on the optimal objective function value obtained by a relaxed solution, and (ii) a lower bound (LB) on the optimal objective function value obtained by the best feasible solution
Summary
Critical infrastructure systems (CIS) underpin almost every aspect of the modern society by providing the essential functions through overlaying service networks. In this study, the focus is to select a subset of disrupted roads and schedule them for recovery such that the functionality of the emergency service network would return to a stable level, where all the service demanding locations (disaster sites) are served, in the shortest possible time. A set of papers integrate network design and scheduling decisions to achieve an effective restoration planning [11, 22]. Bhatia et al propose recovery strategies for large-scale CIS through network centrality measures [34] They do not include the service component into the problem. When the Restoration of services in disrupted infrastructures goal is set as re-establishing the functionality of the service network rather than the complete recovery of CIS, generic network science measures fall short in explaining the dynamics of the networks. The contribution of this paper is threefold: (i) proposing a new network science measure for assessing the criticality of disrupted service network components; (ii) developing a network science based restoration heuristic for service networks; (iii) exploring the relationship between service networks’ different characteristics and their resilience under various disruption scenarios to derive insights on resilience
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
