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


  • Lin, Yi-Kuei
  • Yeh, Cheng-Ta


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

    1. Pentico, David W., 2007. "Assignment problems: A golden anniversary survey," European Journal of Operational Research, Elsevier, vol. 176(2), pages 774-793, January.
    2. Lin, Yi-Kuei, 2007. "On a multicommodity stochastic-flow network with unreliable nodes subject to budget constraint," European Journal of Operational Research, Elsevier, vol. 176(1), pages 347-360, January.
    3. Yeh, Wei-Chang, 2004. "Multistate network reliability evaluation under the maintenance cost constraint," International Journal of Production Economics, Elsevier, vol. 88(1), pages 73-83, March.
    4. Vallada, Eva & Ruiz, Rubén, 2010. "Genetic algorithms with path relinking for the minimum tardiness permutation flowshop problem," Omega, Elsevier, vol. 38(1-2), pages 57-67, February.
    5. Yao, Ming-Jong & Chu, Weng-Ming, 2008. "A genetic algorithm for determining optimal replenishment cycles to minimize maximum warehouse space requirements," Omega, Elsevier, vol. 36(4), pages 619-631, August.
    6. Martin, Clarence H, 2009. "A hybrid genetic algorithm/mathematical programming approach to the multi-family flowshop scheduling problem with lot streaming," Omega, Elsevier, vol. 37(1), pages 126-137, February.
    7. Udhayakumar, A. & Charles, V. & Kumar, Mukesh, 2011. "Stochastic simulation based genetic algorithm for chance constrained data envelopment analysis problems," Omega, Elsevier, vol. 39(4), pages 387-397, August.
    8. repec:eee:reensy:v:94:y:2009:i:5:p:913-921 is not listed on IDEAS
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