Measuring and optimizing system reliability: a stochastic programming approach

We propose a computational framework to quantify (measure) and to optimize the reliability of complex systems. The approach uses a graph representation of the system that is subject to random failures of its components (nodes and edges). Under this setting, reliability is defined as the probability...

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Vydáno v:TOP Ročník 28; číslo 3; s. 626 - 645
Hlavní autoři: Pulsipher, Joshua L., Zavala, Victor M.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2020
Springer Nature B.V
Témata:
Business and Management
Complex systems
Component reliability
Economic Theory/Quantitative Economics/Mathematical Methods
Economics
Failure
Finance
Graph representations
Graphical representations
Industrial and Production Engineering
Insurance
Management
Mixed integer
Nodes
Operations Research/Decision Theory
Optimization
Original Paper
Probability theory
Statistics for Business
Stochastic programming
System reliability
Design
90B15
Network
90C15
Topology
Reliability
68M15
68R10
ISSN:1134-5764, 1863-8279
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Popis
Shrnutí:We propose a computational framework to quantify (measure) and to optimize the reliability of complex systems. The approach uses a graph representation of the system that is subject to random failures of its components (nodes and edges). Under this setting, reliability is defined as the probability of finding a path between sources and sink nodes under random component failures and we show that this measure can be computed by solving a stochastic mixed-integer program. The stochastic programming setting allows us to account for system constraints and general probability distributions to characterize failures and allows us to derive optimization formulations that identify designs of maximum reliability. We also propose a strategy to approximately solve these problems in a scalable manner by using purely continuous formulations.
Bibliografie:ObjectType-Article-1
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ISSN:1134-5764
1863-8279
DOI:10.1007/s11750-020-00550-5