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Supplier selection and order allocation in closed-loop supply chain systems using hybrid Monte Carlo simulation and goal programming

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  • Kamran S. Moghaddam

Abstract

Supplier selection is an important strategic design decision in closed-loop supply chain systems. In addition, and after identifying the candidate suppliers, optimal order allocations are also considered as crucial tactical decisions. This research presents a multi-objective optimisation model to select the best suppliers and configure manufacturing and refurbishing facilities with the optimal number of parts and products in a closed-loop supply chain network. The objective functions in this research are formulated as total profit, total defective parts, total late delivered parts and economic risk factors of the candidate suppliers. The proposed multi-objective model is solved by hybrid Monte Carlo simulation integrated with three different variants of goal programming method. The effectiveness of the mathematical model and the proposed solution algorithms in obtaining Pareto-optimal solutions is demonstrated in a numerical example adopted from a real case study.

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  • Kamran S. Moghaddam, 2015. "Supplier selection and order allocation in closed-loop supply chain systems using hybrid Monte Carlo simulation and goal programming," International Journal of Production Research, Taylor & Francis Journals, vol. 53(20), pages 6320-6338, October.
    • Handle: RePEc:taf:tprsxx:v:53:y:2015:i:20:p:6320-6338
    DOI: 10.1080/00207543.2015.1054452
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    Cited by:

    1. Leonid Mylnikov & Martin Kuetz, 2017. "The Risk Assessment Method in Prognostic Models of Production Systems Management with Account of the Time Factor," European Research Studies Journal, European Research Studies Journal, vol. 0(3A), pages 291-310.
    2. Van Engeland, Jens & Beliën, Jeroen & De Boeck, Liesje & De Jaeger, Simon, 2020. "Literature review: Strategic network optimization models in waste reverse supply chains," Omega, Elsevier, vol. 91(C).
    3. Leonid Mylnikov & Rustam Fayzrakhmanov, 2018. "Production Planning with Parameters on the Basis of Dynamic Predictive Models: Interconnection and the Inertness of their Interaction," European Research Studies Journal, European Research Studies Journal, vol. 0(2), pages 265-281.
    4. Diaz, Rafael & Behr, Joshua G. & Acero, Beatriz, 2022. "Coastal housing recovery in a postdisaster environment: A supply chain perspective," International Journal of Production Economics, Elsevier, vol. 247(C).
    5. Marcus V. C. Fagundes & Bernd Hellingrath & Francisco G. M. Freires, 2021. "Supplier Selection Risk: A New Computer-Based Decision-Making System with Fuzzy Extended AHP," Logistics, MDPI, vol. 5(1), pages 1-17, March.
    6. Luttiely Santos Oliveira & Ricardo Luiz Machado, 2021. "Application of optimization methods in the closed-loop supply chain: a literature review," Journal of Combinatorial Optimization, Springer, vol. 41(2), pages 357-400, February.
    7. Mariagrazia Dotoli & Nicola Epicoco & Marco Falagario, 2017. "A fuzzy technique for supply chain network design with quantity discounts," International Journal of Production Research, Taylor & Francis Journals, vol. 55(7), pages 1862-1884, April.
    8. Shekarian, Mansoor & Reza Nooraie, Seyed Vahid & Parast, Mahour Mellat, 2020. "An examination of the impact of flexibility and agility on mitigating supply chain disruptions," International Journal of Production Economics, Elsevier, vol. 220(C).

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