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Computing Network Reliability

Name: Computing Network Reliability
Creator: Michael O. Ball
Keywords: stochastic network, Reliability and life testing, Reliability, availability, maintenance, inspection in operations research, failure of nodes and links, 0211 other engineering and technologies, network reliability, computation of reliability measures, 02 engineering and technology, Programming involving graphs or networks, 0101 mathematics

Computing network reliability

descriptionPublicationkeyboard_double_arrow_right Article 01 Aug 1979Publisher:Institute for Operations Research and the Management Sciences (INFORMS)Journal:Operations Research, volume 27, pages 823-838 (issn: 0030-364X, eissn: 1526-5463,

Authors: Michael O. Ball;

doi: 10.1287/opre.27.4.823

Computing Network Reliability

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Abstract

This paper presents an algorithm to compute reliability measures on a stochastic network in which both nodes and links can fail. The measures considered are the probability that nodes s and t can communicate for all node pairs s and t, the probability that all operative node pairs can communicate, and the expected number of node pairs communicating. It also computes the latter two measures when all communication must proceed through a root node. A specialized version of the algorithm is given for networks in which only nodes can fail.

Related Organizations

University of Maryland, College Park
United States

Keywords

stochastic network, Reliability and life testing, Reliability, availability, maintenance, inspection in operations research, failure of nodes and links, network reliability, computation of reliability measures, Programming involving graphs or networks

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	129
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 10%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 1%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 10%