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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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    References listed on IDEAS

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    1. Tordecilla-Madera, Rafael & Polo, Andrés & Muñoz, Dairo & González-Rodríguez, Leonardo, 2017. "A robust design for a Colombian dairy cooperative's milk storage and refrigeration logistics system using binary programming," International Journal of Production Economics, Elsevier, vol. 183(PC), pages 710-720.
    2. Bandaly, Dia & Satir, Ahmet & Shanker, Latha, 2016. "Impact of lead time variability in supply chain risk management," International Journal of Production Economics, Elsevier, vol. 180(C), pages 88-100.
    3. Bogataj, D. & Aver, B. & Bogataj, M., 2016. "Supply chain risk at simultaneous robust perturbations," International Journal of Production Economics, Elsevier, vol. 181(PA), pages 68-78.
    4. Baghalian, Atefeh & Rezapour, Shabnam & Farahani, Reza Zanjirani, 2013. "Robust supply chain network design with service level against disruptions and demand uncertainties: A real-life case," European Journal of Operational Research, Elsevier, vol. 227(1), pages 199-215.
    5. G J Hahn & H Kuhn, 2011. "Optimising a value-based performance indicator in mid-term sales and operations planning," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(3), pages 515-525, March.
    6. Tang, Ou & Nurmaya Musa, S., 2011. "Identifying risk issues and research advancements in supply chain risk management," International Journal of Production Economics, Elsevier, vol. 133(1), pages 25-34, September.
    7. Xiao, Shuang & Sethi, Suresh P. & Liu, Mengqi & Ma, Shihua, 2017. "Coordinating contracts for a financially constrained supply chain," Omega, Elsevier, vol. 72(C), pages 71-86.
    8. Holweg, Matthias & Helo, Petri, 2014. "Defining value chain architectures: Linking strategic value creation to operational supply chain design," International Journal of Production Economics, Elsevier, vol. 147(PB), pages 230-238.
    9. Klibi, Walid & Martel, Alain & Guitouni, Adel, 2010. "The design of robust value-creating supply chain networks: A critical review," European Journal of Operational Research, Elsevier, vol. 203(2), pages 283-293, June.
    10. Vahid Nooraie, S. & Parast, Mahour Mellat, 2016. "Mitigating supply chain disruptions through the assessment of trade-offs among risks, costs and investments in capabilities," International Journal of Production Economics, Elsevier, vol. 171(P1), pages 8-21.
    11. Klibi, Walid & Martel, Alain, 2012. "Scenario-based Supply Chain Network risk modeling," European Journal of Operational Research, Elsevier, vol. 223(3), pages 644-658.
    12. Özceylan, Eren & Paksoy, Turan & Bektaş, Tolga, 2014. "Modeling and optimizing the integrated problem of closed-loop supply chain network design and disassembly line balancing," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 61(C), pages 142-164.
    13. Tsao, Yu-Chung & Lu, Jye-Chyi, 2012. "A supply chain network design considering transportation cost discounts," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(2), pages 401-414.
    14. Farahani, Reza Zanjirani & Rezapour, Shabnam & Drezner, Tammy & Fallah, Samira, 2014. "Competitive supply chain network design: An overview of classifications, models, solution techniques and applications," Omega, Elsevier, vol. 45(C), pages 92-118.
    15. Nagurney, Anna, 2010. "Optimal supply chain network design and redesign at minimal total cost and with demand satisfaction," International Journal of Production Economics, Elsevier, vol. 128(1), pages 200-208, November.
    16. Vlajic, Jelena V. & van der Vorst, Jack G.A.J. & Haijema, René, 2012. "A framework for designing robust food supply chains," International Journal of Production Economics, Elsevier, vol. 137(1), pages 176-189.
    17. G. Bennett Stewart, 1994. "Eva™: Fast And Fantasy," Journal of Applied Corporate Finance, Morgan Stanley, vol. 7(2), pages 71-84, June.
    18. Comelli, Mickael & Féniès, Pierre & Tchernev, Nikolay, 2008. "A combined financial and physical flows evaluation for logistic process and tactical production planning: Application in a company supply chain," International Journal of Production Economics, Elsevier, vol. 112(1), pages 77-95, March.
    19. Paksoy, Turan & Bektas, Tolga & Özceylan, Eren, 2011. "Operational and environmental performance measures in a multi-product closed-loop supply chain," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 47(4), pages 532-546, July.
    20. Klibi, Walid & Martel, Alain & Guitouni, Adel, 2016. "The impact of operations anticipations on the quality of stochastic location-allocation models," Omega, Elsevier, vol. 62(C), pages 19-33.
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    Cited by:

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    2. 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.
    3. Li, Yi & Shou, Biying, 2021. "Managing supply risk: Robust procurement strategy for capacity improvement," Omega, Elsevier, vol. 102(C).
    4. 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).
    5. Yang Hu, 2023. "Perspectives in closed-loop supply chains network design considering risk and uncertainty factors," Papers 2306.04819, arXiv.org.
    6. 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.
    7. 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).

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