Reliability evaluation in terms of flow data mining for multistate networks

Network reliability is famous for its problem solving ability in several real-life applications. However, due to its NP-hard nature (Ball in IEEE Trans Reliab 35(3):230–238, 1986), researchers are devoted to the improvement of computational efficiency in various approaches. Although flow in networks...

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Bibliographic Details
Published in:Annals of operations research Vol. 311; no. 1; pp. 225 - 237
Main Authors: Lin, Yi-Kuei, Chen, Shin-Guang
Format: Journal Article
Language:English
Published: New York Springer US 01.04.2022
Springer
Springer Nature B.V
Subjects:
Algorithms
Big Data
Business and Management
Combinatorics
Data mining
Efficiency
Mathematical analysis
Network reliability
Operations research
Operations Research/Decision Theory
Problem solving
Reliability (Engineering)
Reliability analysis
S.I.: Reliability Modeling with Applications Based on Big Data
System reliability
Theory of Computation
Taiwan
Lower boundary vectors
Exact enumeration
Minimum path
Flow data mining
Maximum flow
ISSN:0254-5330, 1572-9338
Online Access:Get full text
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Summary:Network reliability is famous for its problem solving ability in several real-life applications. However, due to its NP-hard nature (Ball in IEEE Trans Reliab 35(3):230–238, 1986), researchers are devoted to the improvement of computational efficiency in various approaches. Although flow in networks depicts its combination properties, only few of them are useful in the calculation of network reliability. In some point of views, we call it mining in flow data. This paper presents techniques of how to efficiently do the flow data mining tasks. A skill based on backtrack and maximal flow is illustrated with examples and benchmarks. The results show that the proposed approach is valuable in the calculation of network reliability.
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ISSN:0254-5330
1572-9338
DOI:10.1007/s10479-020-03774-7