Reliability evaluation and big data analytics architecture for a stochastic flow network with time attribute

A network with multi-state (stochastic) elements (arcs or nodes) is commonly called a stochastic flow network. It is important to measure the system reliability of a stochastic flow network from the perspective of operations management. In the real world, the system reliability of a stochastic flow...

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Bibliographic Details
Published in:Annals of operations research Vol. 311; no. 1; pp. 3 - 18
Main Author: Chang, Ping-Chen
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
Component reliability
Data mining
Methods
Network reliability
Operations management
Operations research
Operations Research/Decision Theory
Probability distribution
Probability theory
Production management
Reliability (Engineering)
Reliability analysis
S.I.: Reliability Modeling with Applications Based on Big Data
Statistical analysis
System reliability
Theory of Computation
Time dependence
Weibull distribution
Taiwan
Stochastic flow network
Reliability evaluation
Weibull distribution
Time attribute
Minimal component vectors
ISSN:0254-5330, 1572-9338
Online Access:Get full text
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Summary:A network with multi-state (stochastic) elements (arcs or nodes) is commonly called a stochastic flow network. It is important to measure the system reliability of a stochastic flow network from the perspective of operations management. In the real world, the system reliability of a stochastic flow network can vary over time. Hence, a critical issue emerges—characterizing the time attribute in a stochastic flow network. To solve this issue, this study bridges (classical) reliability theory and the reliability of a stochastic flow network. This study utilizes Weibull distribution as a possible reliability function to quantify the time attribute in a stochastic flow network. For more general cases, the proposed model and algorithm can apply any reliability function and is not limited to Weibull distribution. First, the reliability of every single component is modeled by Weibull distribution to consider the time attribute, where such components comprise a multi-state element. Once the time constraint is given, the capacity probability distribution of elements can be derived. Second, an algorithm to generate minimal component vectors for given demand is provided. Finally, the system reliability can be calculated in terms of the derived capacity probability distribution and the generated minimal component vectors. In addition, a big data architecture is proposed for the model to collect and estimate the parameters of the reliability function. For future research in which very large volumes of data may be collected, the proposed model and architecture can be applied to time-dependent monitoring.
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ISSN:0254-5330
1572-9338
DOI:10.1007/s10479-019-03427-4