Adaptive algorithm for dependent infrastructure network restoration in an imperfect information sharing environment

Alireza Rangrazjeddi, Andrés D González and Kash Barker

PLOS ONE, 2022, vol. 17, issue 8, 1-24

Abstract: Critical infrastructure networks are vital for a functioning society and their failure can have widespread consequences. Decision-making for critical infrastructure resilience can suffer based on several characteristics exhibited by these networks, including (i) that there exist interdependencies with other networks, (ii) that several decision-makers represent potentially competing interests among the interdependent networks, and (iii) that information about other decision-makers’ actions are uncertain and potentially unknown. To address these concerns, we propose an adaptive algorithm using machine learning to integrate predictions about other decision-makers’ behavior into an interdependent network restoration planning problem considering an imperfect information sharing environment. We examined our algorithm against the optimal solution for various types, sizes, and dependencies of networks, resulting in insignificant differences. To assess the proposed algorithm’s efficiency, we compared its results with a proposed heuristic method that prioritizes, and schedules components restoration based on centrality-based importance measures. The proposed algorithm provides a solution sufficiently close to the optimal solution showing the algorithm performs well in situations where the information sharing environment is incomplete.

Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0270407 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 70407&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0270407

DOI: 10.1371/journal.pone.0270407

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().