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Solving a Supply Chain Management Problem to Near Optimality Using Ant Colony Optimization, in an International Context

Listed author(s):
  • Luminiţa Nicolescu


    (Bucharest Academy of Economic Studies, Romania)

  • Cristina Galalae

    (Bucharest Academy of Economic Studies, Romania)

  • Alexandru Voicu

    (Bucharest Academy of Economic Studies, Romania)

The importance of achieving optimality or near optimality in supply routing is on the rise as globalization leads to scenarios in which multiple, heterogeneous and highly spatially distributed demands have to be satisfied under stringent constraints. However, there is no consensus concerning what constitutes an all-encompassing objective function for the supply planner, who faces what can easily constitute a problem requiring Non-deterministic Polynomial time for determining the solution even in its simplest formulations. The work presented in this article proposes a mathematically grounded approach that uses Ant Colony Optimisation to yield near optimal results across a large set of problem formulations and objective functions. The latter are designed to capture real-world goals such as cost reduction, optimal transportation management, flexibility and minimal lead-time. This study adds a new dimension to topics traditionally encountered in the literature, namely that of the cultural differences between partners engaged in international trade relations. Furthermore, the impact of the lag between determining and implementing the quasioptimal strategy is forecast for an array of objective functions tailored to represent approaches encountered in international companies dealing with supply challenges in fields such as Information Technology. Finally, the framework thus established is employed to analyse the indirect relationship between Asian “white box” suppliers and a Romanian firm operating in Mobile Integrated Device space.

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Article provided by Academy of Economic Studies - Bucharest, Romania in its journal The AMFITEATRU ECONOMIC journal.

Volume (Year): 15 (2013)
Issue (Month): 33 (February)
Pages: 8-26

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Handle: RePEc:aes:amfeco:v:15:y:2013:i:33:p:8-26
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  1. Gheidar Kheljani, J. & Ghodsypour, S.H. & O'Brien, C., 2009. "Optimizing whole supply chain benefit versus buyer's benefit through supplier selection," International Journal of Production Economics, Elsevier, vol. 121(2), pages 482-493, October.
  2. Kenneth E. Boulding, 1956. "General Systems Theory--The Skeleton of Science," Management Science, INFORMS, vol. 2(3), pages 197-208, April.
  3. Weber, Charles A. & Current, John R. & Benton, W. C., 1991. "Vendor selection criteria and methods," European Journal of Operational Research, Elsevier, vol. 50(1), pages 2-18, January.
  4. Lap Mui Ann Chan & Ana Muriel & Zuo-Jun Max Shen & David Simchi-Levi & Chung-Piaw Teo, 2002. "Effective Zero-Inventory-Ordering Policies for the Single-Warehouse Multiretailer Problem with Piecewise Linear Cost Structures," Management Science, INFORMS, vol. 48(11), pages 1446-1460, November.
  5. Emil Crişan & Liviu Ilieş & Irina Salanţă, 2010. "Management Best Practices Used in Romanian Logistics Customer Service Planning," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 12(27), pages 215-227, February.
  6. Basarab Gogoneata, 2008. "An analysis of explanatory factors of logistics performance of a country," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 10(24), pages 143-156, June.
  7. Yue, Jinfeng & Xia, Yu & Tran, Thuhang, 2010. "Selecting sourcing partners for a make-to-order supply chain," Omega, Elsevier, vol. 38(3-4), pages 136-144, June.
  8. Rust, John, 1996. "Numerical dynamic programming in economics," Handbook of Computational Economics,in: H. M. Amman & D. A. Kendrick & J. Rust (ed.), Handbook of Computational Economics, edition 1, volume 1, chapter 14, pages 619-729 Elsevier.
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