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A multi-objective closed-loop supply chain network design problem under parameter uncertainty: comparison of exact methods

Author

Listed:
  • Omid Abdolazimi

    (Kharazmi University)

  • Farzad Bahrami

    (Arak University)

  • Davood Shishebori

    (Yazd University)

  • Majid Alimohammadi Ardakani

    (Ardakan University)

Abstract

Forward and reverse supply chains are one of the most important issues in supply chain management. These kinds of supply chain networks include a direct and reverse supply chain. In this paper, a multi-objective closed-loop supply chain network consisting of multi-level, multi-period, and multi-products is proposed under the set of parameter uncertainties. We formulate the problem as a mixed-integer linear programming model. The model assumes a shortage and a remaining inventory at the end of each period. The first objective function is to minimize the total costs of the network. The second one is to maximize the on-time delivery of the products purchased from suppliers to factories. The third objective is to maximize the quality according to the quality of the products produced in the forward supply chain and those that can be recovered in the reverse supply chain. Another point worth noting in this manuscript is selecting the best supplier. Because choosing the best supplier is one of the most critical decisions that purchasing managers have to make in a supply chain. It is based on different criteria, such as price, quality, customer service, and delivery, discussed in this article. Uncertainty is also considered in the model, and a scenario-based robust optimization approach is used to cope with it. Due to the problem’s multi-objective nature, four exact methods, namely LP-metric, sequential linear goal programming (SLGP), TH approach, and simple additive weighting are used to solve the objective functions. Finally, the most effective method for solving various numerical examples is selected as the best method by the least deviations compared to the other methods; in this paper, the SLGP method is chosen. To illustrate the response to a problem in more detail, some of the SLGP method outputs are presented. The results show the efficiency of the proposed model. Thus, it can be used in a variety of industries whose products are recycled and where the quality of products and the choice of appropriate suppliers are of great importance.

Suggested Citation

  • Omid Abdolazimi & Farzad Bahrami & Davood Shishebori & Majid Alimohammadi Ardakani, 2022. "A multi-objective closed-loop supply chain network design problem under parameter uncertainty: comparison of exact methods," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(9), pages 10768-10802, September.
    • Handle: RePEc:spr:endesu:v:24:y:2022:i:9:d:10.1007_s10668-021-01883-2
    DOI: 10.1007/s10668-021-01883-2
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    as
    1. Azizi, Vahid & Hu, Guiping, 2020. "Multi-product pickup and delivery supply chain design with location-routing and direct shipment," International Journal of Production Economics, Elsevier, vol. 226(C).
    2. Zhang, Min & Hu, Haiju & Zhao, Xiande, 2020. "Developing product recall capability through supply chain quality management," International Journal of Production Economics, Elsevier, vol. 229(C).
    3. Jayaraman, Vaidyanathan & Ross, Anthony, 2003. "A simulated annealing methodology to distribution network design and management," European Journal of Operational Research, Elsevier, vol. 144(3), pages 629-645, February.
    4. Nagurney, Anna & Saberi, Sara & Shukla, Shivani & Floden, Jonas, 2015. "Supply chain network competition in price and quality with multiple manufacturers and freight service providers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 77(C), pages 248-267.
    5. Hosseini-Motlagh, Seyyed-Mahdi & Samani, Mohammad Reza Ghatreh & Shahbazbegian, Vahid, 2020. "Innovative strategy to design a mixed resilient-sustainable electricity supply chain network under uncertainty," Applied Energy, Elsevier, vol. 280(C).
    6. Perron, Sylvain & Hansen, Pierre & Le Digabel, Sébastien & Mladenovic, Nenad, 2010. "Exact and heuristic solutions of the global supply chain problem with transfer pricing," European Journal of Operational Research, Elsevier, vol. 202(3), pages 864-879, May.
    7. Robinson, Carol J. & Malhotra, Manoj K., 2005. "Defining the concept of supply chain quality management and its relevance to academic and industrial practice," International Journal of Production Economics, Elsevier, vol. 96(3), pages 315-337, June.
    8. Aghajani, Mojtaba & Torabi, S. Ali & Heydari, Jafar, 2020. "A novel option contract integrated with supplier selection and inventory prepositioning for humanitarian relief supply chains," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).
    9. George B. Dantzig, 1955. "Linear Programming under Uncertainty," Management Science, INFORMS, vol. 1(3-4), pages 197-206, 04-07.
    10. Vahdani, Behnam & Tavakkoli-Moghaddam, Reza & Modarres, Mohammad & Baboli, Armand, 2012. "Reliable design of a forward/reverse logistics network under uncertainty: A robust-M/M/c queuing model," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(6), pages 1152-1168.
    11. 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.
    12. Shen, Jiayu, 2020. "An environmental supply chain network under uncertainty," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
    13. Li, Hongyan & Hendry, Linda & Teunter, Ruud, 2009. "A strategic capacity allocation model for a complex supply chain: Formulation and solution approach comparison," International Journal of Production Economics, Elsevier, vol. 121(2), pages 505-518, October.
    14. Sebatjane, Makoena & Adetunji, Olufemi, 2020. "A three-echelon supply chain for economic growing quantity model with price- and freshness-dependent demand: Pricing, ordering and shipment decisions," Operations Research Perspectives, Elsevier, vol. 7(C).
    15. Snyder, Lawrence V. & Daskin, Mark S. & Teo, Chung-Piaw, 2007. "The stochastic location model with risk pooling," European Journal of Operational Research, Elsevier, vol. 179(3), pages 1221-1238, June.
    16. Lian Qi & Zuo‐Jun Max Shen, 2007. "A supply chain design model with unreliable supply," Naval Research Logistics (NRL), John Wiley & Sons, vol. 54(8), pages 829-844, December.
    17. Zhou, Honggeng & Li, Ling, 2020. "The impact of supply chain practices and quality management on firm performance: Evidence from China's small and medium manufacturing enterprises," International Journal of Production Economics, Elsevier, vol. 230(C).
    18. Drezner, Zvi & Wesolowsky, George O., 2003. "Network design: selection and design of links and facility location," Transportation Research Part A: Policy and Practice, Elsevier, vol. 37(3), pages 241-256, March.
    19. Ozdemir, Deniz & Yucesan, Enver & Herer, Yale T., 2006. "Multi-location transshipment problem with capacitated transportation," European Journal of Operational Research, Elsevier, vol. 175(1), pages 602-621, November.
    20. John M. Mulvey & Robert J. Vanderbei & Stavros A. Zenios, 1995. "Robust Optimization of Large-Scale Systems," Operations Research, INFORMS, vol. 43(2), pages 264-281, April.
    21. Ambrosino, Daniela & Grazia Scutella, Maria, 2005. "Distribution network design: New problems and related models," European Journal of Operational Research, Elsevier, vol. 165(3), pages 610-624, September.
    22. Mota, Bruna & Gomes, Maria Isabel & Carvalho, Ana & Barbosa-Povoa, Ana Paula, 2018. "Sustainable supply chains: An integrated modeling approach under uncertainty," Omega, Elsevier, vol. 77(C), pages 32-57.
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