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Restoration of services in disrupted infrastructure systems: A network science approach

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  • Aybike Ulusan
  • Ozlem Ergun

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.

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  • Aybike Ulusan & Ozlem Ergun, 2018. "Restoration of services in disrupted infrastructure systems: A network science approach," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-28, February.
    • Handle: RePEc:plo:pone00:0192272
    DOI: 10.1371/journal.pone.0192272
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    References listed on IDEAS

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    Cited by:

    1. Zou, Qiling & Chen, Suren, 2021. "Resilience-based Recovery Scheduling of Transportation Network in Mixed Traffic Environment: A Deep-Ensemble-Assisted Active Learning Approach," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    2. Ulusan, Aybike & Ergun, Özlem, 2021. "Approximate dynamic programming for network recovery problems with stochastic demand," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 151(C).
    3. Giada Feletti & Mariachiara Piraina & Boris Petrenj & Paolo Trucco, 2022. "Collaborative capability building for critical infrastructure resilience: assessment and selection of good practices," Environment Systems and Decisions, Springer, vol. 42(2), pages 207-233, June.
    4. Poulin, Craig & Kane, Michael B., 2021. "Infrastructure resilience curves: Performance measures and summary metrics," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    5. Garay-Sianca, Aniela & Nurre Pinkley, Sarah G., 2021. "Interdependent integrated network design and scheduling problems with movement of machines," European Journal of Operational Research, Elsevier, vol. 289(1), pages 297-327.

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