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An efficient algorithm for computing exact system and survival signatures of K-terminal network reliability
Resilience Engineering Research Group, Faculty of Engineering, University of Nottingham, Nottingham, UK.ORCID iD: 0000-0002-5698-6740
Örebro University, School of Science and Technology.ORCID iD: 0000-0002-2014-1308
Resilience Engineering Research Group, Faculty of Engineering, University of Nottingham, Nottingham, UK.
2019 (English)In: Reliability Engineering & System Safety, ISSN 0951-8320, E-ISSN 1879-0836, Vol. 185, p. 429-439Article in journal (Refereed) Published
Abstract [en]

An efficient algorithm is presented for computing exact system and survival signatures of K-terminal reliability in undirected networks with unreliable edges. K-terminal reliability is defined as the probability that a subset K of the network nodes can communicate with each other. Signatures have several advantages over direct reliability calculation such as enabling certain stochastic comparisons of reliability between competing network topology designs, extremely fast repeat computation of network reliability for different edge reliabilities and computation of network reliability when failures of edges are exchangeable but not independent. Existing methods for computation of signatures for K-terminal network reliability require derivation of cut-sets or path-sets which is only feasible for small networks due to the computational expense. The new algorithm utilises binary decision diagrams, boundary set partition sets and simple array operations to efficiently compute signatures through a factorisation of the network edges. The performance and advantages of the algorithm are demonstrated through application to a set of benchmark networks and a sensor network from an underground mine.

Place, publisher, year, edition, pages
Elsevier, 2019. Vol. 185, p. 429-439
National Category
Other Mechanical Engineering
Research subject
Mechanical Engineering
Identifiers
URN: urn:nbn:se:oru:diva-71504DOI: 10.1016/j.ress.2019.01.011ISI: 000459949900035Scopus ID: 2-s2.0-85060149804OAI: oai:DiVA.org:oru-71504DiVA, id: diva2:1279359
Funder
Knowledge FoundationAvailable from: 2019-01-16 Created: 2019-01-16 Last updated: 2022-04-19Bibliographically approved

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Reed, SeanLöfstrand, Magnus

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