Flow Generation for Stochastic-flow Networks with Demands as Real Number

Chin Lung Huang, Cheng Fu Huang*, Ding Hsiang Huang, Yi Kuei Lin

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Network reliability is concerned with the probability that the predetermined demand from multiple sources can be transmitted through the stochastic-flow network (SFN) successfully. The minimal capacity vectors (MCVs) which are the minimally required capacity for each arc are used to network reliability calculation. Every MCV is transformed from the flow vectors satisfying predetermined demands based on minimal paths (MPs). However, the demands are set as integers for flow vector generation in the previous studies. In fact, demand might be the positive real number in the practical transmission. In this paper, a concept of the minimal transmission unit is proposed to search the flow vectors for demands as the positive real number. It can reduce the range of the flow search. Then an algorithm for flow vector generation is developed to efficiently deal with demands as the positive real number. A simple case shows that the proposed algorithm is reasonable.

Original languageEnglish
Title of host publicationConference Proceedings - 27th ISSAT International Conference on Reliability and Quality in Design
EditorsHoang Pham
PublisherInternational Society of Science and Applied Technologies
Pages184-188
Number of pages5
ISBN (Electronic)9798986576107
StatePublished - 2022
Event27th ISSAT International Conference on Reliability and Quality in Design - Virtual, Online
Duration: 4 Aug 20226 Aug 2022

Publication series

NameConference Proceedings - 27th ISSAT International Conference on Reliability and Quality in Design

Conference

Conference27th ISSAT International Conference on Reliability and Quality in Design
CityVirtual, Online
Period4/08/226/08/22

Keywords

  • Flow vector
  • Minimal capacity vector
  • Minimal transmission unit
  • Real number
  • Stochastic-flow network (SFN)

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