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Planning of a decentralized distribution network using bilevel optimization

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  • Calvete, Herminia I.
  • Galé, Carmen
  • Iranzo, José A.

Abstract

This paper proposes a bilevel optimization problem to model the planning of a distribution network that allows us to take into account how decisions made at the distribution stage of the supply chain can affect and be affected by decisions made at the manufacturing stage. Usually, the distribution network design problem decides on the opening of depots and the distribution from the depots to customers only and pays no attention to the manufacturing process itself. By way of example, the paper discusses the implications of formulating a bilevel model to integrate distribution and manufacturing, maintaining the hierarchy existing in the decision process. The resulting model is a bilevel mixed integer optimization problem. Hence, only small instances can be optimally solved in an acceptable computing time. In order to be able to solve the optimization model for realistic large systems, a metaheuristic approach based on evolutionary algorithms is developed. The algorithm combines the use of an evolutionary algorithm to control the supply of depots with optimization techniques to determine the delivery from depots to customers and the supply from manufacturing plants to depots. A computational experiment is carried out to assess the efficiency and robustness of the algorithm.

Suggested Citation

  • Calvete, Herminia I. & Galé, Carmen & Iranzo, José A., 2014. "Planning of a decentralized distribution network using bilevel optimization," Omega, Elsevier, vol. 49(C), pages 30-41.
    • Handle: RePEc:eee:jomega:v:49:y:2014:i:c:p:30-41
    DOI: 10.1016/j.omega.2014.05.004
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    References listed on IDEAS

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    Cited by:

    1. Herminia I. Calvete & Carmen Galé & José A. Iranzo, 2016. "An improved evolutionary algorithm for the two-stage transportation problem with fixed charge at depots," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 38(1), pages 189-206, January.
    2. Nishizaki, Ichiro & Hayashida, Tomohiro & Sekizaki, Shinya & Okabe, Junya, 2022. "Data envelopment analysis approaches for two-level production and distribution planning problems," European Journal of Operational Research, Elsevier, vol. 300(1), pages 255-268.
    3. Wang, Delu & Liu, Yifei & Wang, Yadong & Shi, Xunpeng & Song, Xuefeng, 2020. "Allocation of coal de-capacity quota among provinces in China: A bi-level multi-objective combinatorial optimization approach," Energy Economics, Elsevier, vol. 87(C).
    4. Sun, X.T. & Chung, S.H. & Chan, Felix T.S., 2015. "Integrated scheduling of a multi-product multi-factory manufacturing system with maritime transport limits," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 79(C), pages 110-127.
    5. Abdul Sattar Safaei & Saba Farsad & Mohammad Mahdi Paydar, 2020. "Emergency logistics planning under supply risk and demand uncertainty," Operational Research, Springer, vol. 20(3), pages 1437-1460, September.
    6. Zhou, Xiaoyang & Luo, Rui & Tu, Yan & Lev, Benjamin & Pedrycz, Witold, 2018. "Data envelopment analysis for bi-level systems with multiple followers," Omega, Elsevier, vol. 77(C), pages 180-188.
    7. Marjia Haque & Sanjoy Kumar Paul & Ruhul Sarker & Daryl Essam, 2022. "A combined approach for modeling multi-echelon multi-period decentralized supply chain," Annals of Operations Research, Springer, vol. 315(2), pages 1665-1702, August.

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