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Intelligent modeling method based on genetic algorithm for partner selection in virtual organizations

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  • Simona, Dinu
  • Raluca, Pacuraru

Abstract

The goal of a Virtual Organization is to find the most appropriate partners in terms of expertise, cost wise, quick response, and environment. In this study we propose a model and a solution approach to a partner selection problem considering three main evaluation criteria: cost, time and risk. This multiobjective problem is solved by an improved GA that includes meiosis specific characteristics and step-size adaptation for the mutation operator. The algorithm performs strong exploration initially and exploitation in later generations. It has high global search ability and a fast convergence rate and also avoids premature convergence. On the basis of the numerical investigations, the incorporation of the proposed enhancements has been successfully proved.

Suggested Citation

  • Simona, Dinu & Raluca, Pacuraru, 2011. "Intelligent modeling method based on genetic algorithm for partner selection in virtual organizations," Business and Economic Horizons (BEH), Prague Development Center (PRADEC), vol. 5(2), pages 1-12, April.
  • Handle: RePEc:ags:pdcbeh:204193
    DOI: 10.22004/ag.econ.204193
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    References listed on IDEAS

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    1. Famuyiwa, Oluwafemi & Monplaisir, Leslie & Nepal, Bimal, 2008. "An integrated fuzzy-goal-programming-based framework for selecting suppliers in strategic alliance formation," International Journal of Production Economics, Elsevier, vol. 113(2), pages 862-875, June.
    2. Brucker, Peter & Drexl, Andreas & Mohring, Rolf & Neumann, Klaus & Pesch, Erwin, 1999. "Resource-constrained project scheduling: Notation, classification, models, and methods," European Journal of Operational Research, Elsevier, vol. 112(1), pages 3-41, January.
    3. Ip, W. H. & Yung, K. L. & Wang, Dingwei, 2004. "A branch and bound algorithm for sub-contractor selection in agile manufacturing environment," International Journal of Production Economics, Elsevier, vol. 87(2), pages 195-205, January.
    4. Manju K. Ahuja & Kathleen M. Carley, 1999. "Network Structure in Virtual Organizations," Organization Science, INFORMS, vol. 10(6), pages 741-757, December.
    5. Talluri, Srinivas & Baker, R. C. & Sarkis, Joseph, 1999. "A framework for designing efficient value chain networks," International Journal of Production Economics, Elsevier, vol. 62(1-2), pages 133-144, May.
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