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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Veröffentlicht in:arXiv.org
Hauptverfasser: Pulsipher, Joshua L, Zavala, Victor M
Format: Paper
Sprache:Englisch
Veröffentlicht: Ithaca Cornell University Library, arXiv.org 24.06.2021
Schlagworte:
Complex systems
Component reliability
Failure
Graph representations
Graphical representations
Mixed integer
Nodes
Optimization
Probability theory
Stochastic programming
System reliability
ISSN:2331-8422
Online-Zugang:Volltext
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Zusammenfassung: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.
Bibliographie:SourceType-Working Papers-1
ObjectType-Working Paper/Pre-Print-1
content type line 50
ISSN:2331-8422
DOI:10.48550/arxiv.2106.12712