Network reliability maximization for stochastic-flow network subject to correlated failures using genetic algorithm and tabu search

Network reliability is an important performance index for many real-life systems, such as electric power systems, computer systems and transportation systems. These systems can be modelled as stochastic-flow networks (SFNs) composed of arcs and nodes. Most system supervisors respect the network reli...

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Veröffentlicht in:Engineering optimization Jg. 50; H. 7; S. 1212 - 1231
Hauptverfasser: Yeh, Cheng-Ta, Lin, Yi-Kuei, Yang, Jo-Yun
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Abingdon Taylor & Francis Ltd 03.07.2018
Schlagworte:
Binomial distribution
Correlation
Electric arcs
Electric power systems
Failure
Genetic algorithms
Mathematical models
Maximization
Network reliability
Optimization
Performance indices
Production scheduling
Soft computing
Supervisors
System reliability
Tabu search
Transportation systems
ISSN:0305-215X, 1029-0273
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Zusammenfassung:Network reliability is an important performance index for many real-life systems, such as electric power systems, computer systems and transportation systems. These systems can be modelled as stochastic-flow networks (SFNs) composed of arcs and nodes. Most system supervisors respect the network reliability maximization by finding the optimal multi-state resource assignment, which is one resource to each arc. However, a disaster may cause correlated failures for the assigned resources, affecting the network reliability. This article focuses on determining the optimal resource assignment with maximal network reliability for SFNs. To solve the problem, this study proposes a hybrid algorithm integrating the genetic algorithm and tabu search to determine the optimal assignment, called the hybrid GA-TS algorithm (HGTA), and integrates minimal paths, recursive sum of disjoint products and the correlated binomial distribution to calculate network reliability. Several practical numerical experiments are adopted to demonstrate that HGTA has better computational quality than several popular soft computing algorithms.
Bibliographie:ObjectType-Article-1
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ISSN:0305-215X
1029-0273
DOI:10.1080/0305215X.2017.1353089