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Determining the optimal double-component assignment for a stochastic computer network

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  • Lin, Yi-Kuei
  • Yeh, Cheng-Ta

Abstract

This study determines the optimal double-component assignment based on the system reliability criterion for a computer system, in which the computer system is represented as a network with a set of links and a set of vertices. The double-component assignment is to assign a set of transmission lines (resp. facilities) to the links (resp. vertices) of the network, in which each transmission line (resp. facility) has multiple states due to maintenance or failure. Thus, the computer system according to any double-component assignment is called a stochastic computer network. The system reliability is the probability that the specific units of data are successfully transmitted through the stochastic computer network. An optimization algorithm which integrates the genetic algorithm, minimal paths, and Recursive Sum of Disjoint Products is utilized to find the optimal double-component assignment with maximal system reliability. Several computer networks are utilized to demonstrate the efficiency of the proposed algorithm compared with other algorithms. By solving this problem, data can be more reliably transmitted and thus the organization operation is executed more smoothly.

Suggested Citation

  • Lin, Yi-Kuei & Yeh, Cheng-Ta, 2012. "Determining the optimal double-component assignment for a stochastic computer network," Omega, Elsevier, vol. 40(1), pages 120-130, January.
    • Handle: RePEc:eee:jomega:v:40:y:2012:i:1:p:120-130
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    References listed on IDEAS

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    Cited by:

    1. Qiu, Siqi & Ming, Xinguo & Sallak, Mohamed & Lu, Jialiang, 2022. "A Birnbaum importance-based two-stage approach for two-type component assignment problems," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    2. Cheng-Ta Yeh, 2020. "Binary-state line assignment optimization to maximize the reliability of an information network under time and budget constraints," Annals of Operations Research, Springer, vol. 287(1), pages 439-463, April.

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