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

Cheng Ta Yeh, Yi-Kuei Lin*, Jo Yun Yang

*Corresponding author for this work

Research output: Contribution to journalArticle

7 Scopus citations

Abstract

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. 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 propose. 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.

Original languageEnglish
Pages (from-to)1212-1231
Number of pages20
JournalEngineering Optimization
Volume50
Issue number7
DOIs
StatePublished - 3 Jul 2018

Keywords

  • Network reliability maximization
  • correlated failure
  • genetic algorithm
  • multi-state resource assignment
  • stochastic-flow network (SFN)
  • tabu search

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