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Robust design of a closed-loop supply chain under uncertainty conditions integrating financial criteria

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Listed:
  • Polo, Andrés
  • Peña, Numar
  • Muñoz, Dairo
  • Cañón, Adrián
  • Escobar, John Willmer

Abstract

This paper proposes the formulation of a Mixed Integer Non-Linear Programming (MINLP) model that integrates financial risks measures in the robust design of a closed-loop supply chain, considering demand uncertainty of final products. In light of the advances in the reprocessing of goods to improve financial performance, the analysis of a closed-loop supply chain becomes crucial for the competitiveness of companies. We propose a multi-period model to solve the supply chain design problem in which several items must be produced through different levels after the production process, considering the flow of reverse of some products, which can be reprocessed or discarded. In this paper, we studied the design of a supply chain that includes several plants, distribution centers, collection centers, demand zones, and products; it consists of both products forward and reverses in the supply chain. Indeed, the perturbation parameters, robustness requirements, and the performance characteristics were identified qualitatively and quantitatively by determining their impact on the formulation and methodology. A variety of configurations are produced in the closed-loop supply chain, considering the variations of the uncertainty of the demand as a perturbation parameter. The objective is to maximize the economic value-added (EVA™); therefore, the most robust configuration is identified through robustness- EVA™ characterization and used to design the closed-loop chain. Finally, we present a numerical example using real information of the electronics industry in Bogotá to test the applied methodology and show that it is suitable for this type of problems.

Suggested Citation

  • Polo, Andrés & Peña, Numar & Muñoz, Dairo & Cañón, Adrián & Escobar, John Willmer, 2019. "Robust design of a closed-loop supply chain under uncertainty conditions integrating financial criteria," Omega, Elsevier, vol. 88(C), pages 110-132.
    • Handle: RePEc:eee:jomega:v:88:y:2019:i:c:p:110-132
    DOI: 10.1016/j.omega.2018.09.003
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    Cited by:

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    3. Borumand, Ali & Marandi, Ahmadreza & Nookabadi, Ali S. & Atan, Zümbül, 2024. "An oracle-based algorithm for robust planning of production routing problems in closed-loop supply chains of beverage glass bottles," Omega, Elsevier, vol. 122(C).
    4. César Flores-Fonseca & Rodrigo Linfati & John Willmer Escobar, 2022. "Exact algorithms for production planning in mining considering the use of stockpiles and sequencing of power shovels in open-pit mines," Operational Research, Springer, vol. 22(3), pages 2529-2553, July.
    5. Clavijo-Buritica, Nicolás & Triana-Sanchez, Laura & Escobar, John Willmer, 2023. "A hybrid modeling approach for resilient agri-supply network design in emerging countries: Colombian coffee supply chain," Socio-Economic Planning Sciences, Elsevier, vol. 85(C).
    6. Aijun Liu & Yan Zhang & Senhao Luo & Jie Miao, 2020. "Dual-Channel Global Closed-Loop Supply Chain Network Optimization Based on Random Demand and Recovery Rate," IJERPH, MDPI, vol. 17(23), pages 1-32, November.
    7. Li, Yi & Shou, Biying, 2021. "Managing supply risk: Robust procurement strategy for capacity improvement," Omega, Elsevier, vol. 102(C).
    8. Schultz, Michael & Soolaki, Majid & Salari, Mostafa & Bakhshian, Elnaz, 2023. "A combined optimization–simulation approach for modified outside-in boarding under COVID-19 regulations including limited baggage compartment capacities," Journal of Air Transport Management, Elsevier, vol. 106(C).

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