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Methodology for the reliability evaluation of the novel learning-effect multi-state flow network

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  • Wei-Chang Yeh

Abstract

In the traditional multi-state flow networks (MFNs), it is assumed that the flow is fixed in each arc. However, the flow may experience gain after transmission via arcs in many real-life applications; e.g., the infected population size is increased from time to time for a certain period during outbreaks of disease, the number of bit errors is amplified in digital transmission, etc. Hence, a novel network model called the learning-effect MFN (MFNle) is proposed to meet real-world problems. A straightforward and simple algorithm based on minimal path (MP) set is presented here to evaluate MFNle reliability, which is defined as the probability that at least d units of data can be sent from the source node and dout (≥d) units of data exists from the sink node through a single MP in the MFNle. The computational complexity of the proposed algorithm is also analyzed. Finally, an example is given to illustrate how the MFNle reliability is calculated using the proposed algorithm.

Suggested Citation

  • Wei-Chang Yeh, 2017. "Methodology for the reliability evaluation of the novel learning-effect multi-state flow network," IISE Transactions, Taylor & Francis Journals, vol. 49(11), pages 1078-1085, November.
    • Handle: RePEc:taf:uiiexx:v:49:y:2017:i:11:p:1078-1085
    DOI: 10.1080/24725854.2017.1351044
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    References listed on IDEAS

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    1. Gregory Levitin, 2005. "The Universal Generating Function in Reliability Analysis and Optimization," Springer Series in Reliability Engineering, Springer, number 978-1-84628-245-4, February.
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    Cited by:

    1. Yeh, Wei-Chang, 2020. "A new method for verifying d-MC candidates," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    2. Chang, Ping-Chen & Lin, Yi-Kuei & Chiang, Yu-Min, 2019. "System reliability estimation and sensitivity analysis for multi-state manufacturing network with joint buffers––A simulation approach," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 103-109.
    3. Hao, Zhifeng & Yeh, Wei-Chang & Zuo, Ming & Wang, Jing, 2020. "Multi-distribution multi-commodity multistate flow network model and its reliability evaluation algorithm," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    4. Hao, Zhifeng & Yeh, Wei-Chang & Liu, Zhenyao & Forghani-elahabad, Majid, 2020. "General multi-state rework network and reliability algorithm," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    5. Yeh, Wei-Chang & Hao, Zhifeng & Forghani-elahabad, Majid & Wang, Gai-Ge & Lin, Yih-Lon, 2021. "Novel Binary-Addition Tree Algorithm for Reliability Evaluation of Acyclic Multistate Information Networks," Reliability Engineering and System Safety, Elsevier, vol. 210(C).

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