IDEAS home Printed from https://ideas.repec.org/a/spr/endesu/v24y2022i9d10.1007_s10668-021-01883-2.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10668-021-01883-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10668-021-01883-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    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. 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.
    3. 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).
    4. 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.
    5. 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).
    6. 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.
    7. 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.
    8. 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.
    9. 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).
    10. 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.
    11. 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.
    12. 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).
    13. 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.
    14. 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.
    15. 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).
    16. George B. Dantzig, 1955. "Linear Programming under Uncertainty," Management Science, INFORMS, vol. 1(3-4), pages 197-206, 04-07.
    17. 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.
    18. 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.
    19. 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.
    20. 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.
    21. 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.
    22. Shen, Jiayu, 2020. "An environmental supply chain network under uncertainty," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Essam Kaoud & Mohammad A. M. Abdel-Aal & Tatsuhiko Sakaguchi & Naoki Uchiyama, 2022. "Robust Optimization for a Bi-Objective Green Closed-Loop Supply Chain with Heterogeneous Transportation System and Presorting Consideration," Sustainability, MDPI, vol. 14(16), pages 1-23, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hasani, Aliakbar & Khosrojerdi, Amirhossein, 2016. "Robust global supply chain network design under disruption and uncertainty considering resilience strategies: A parallel memetic algorithm for a real-life case study," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 87(C), pages 20-52.
    2. Jahani, Hamed & Abbasi, Babak & Sheu, Jiuh-Biing & Klibi, Walid, 2024. "Supply chain network design with financial considerations: A comprehensive review," European Journal of Operational Research, Elsevier, vol. 312(3), pages 799-839.
    3. Ouhimmou, Mustapha & Nourelfath, Mustapha & Bouchard, Mathieu & Bricha, Naji, 2019. "Design of robust distribution network under demand uncertainty: A case study in the pulp and paper," International Journal of Production Economics, Elsevier, vol. 218(C), pages 96-105.
    4. Melo, M.T. & Nickel, S. & Saldanha-da-Gama, F., 2009. "Facility location and supply chain management - A review," European Journal of Operational Research, Elsevier, vol. 196(2), pages 401-412, July.
    5. Vahid Nazari-Ghanbarloo & Ali Ghodratnama, 2021. "Optimizing a robust tri-objective multi-period reliable supply chain network considering queuing system and operational and disruption risks," Operational Research, Springer, vol. 21(3), pages 1963-2020, September.
    6. Hashem Omrani & Farzane Adabi & Narges Adabi, 2017. "Designing an efficient supply chain network with uncertain data: a robust optimization—data envelopment analysis approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(7), pages 816-828, July.
    7. Xuejie Bai & Yankui Liu, 2016. "Robust optimization of supply chain network design in fuzzy decision system," Journal of Intelligent Manufacturing, Springer, vol. 27(6), pages 1131-1149, December.
    8. Berger, Niklas & Schulze-Schwering, Stefan & Long, Elisa & Spinler, Stefan, 2023. "Risk management of supply chain disruptions: An epidemic modeling approach," European Journal of Operational Research, Elsevier, vol. 304(3), pages 1036-1051.
    9. Jabbarzadeh, Armin & Fahimnia, Behnam & Sheu, Jiuh-Biing & Moghadam, Hani Shahmoradi, 2016. "Designing a supply chain resilient to major disruptions and supply/demand interruptions," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 121-149.
    10. Berman, Oded & Krass, Dmitry & Menezes, Mozart B.C., 2016. "Directed assignment vs. customer choice in location inventory models," International Journal of Production Economics, Elsevier, vol. 179(C), pages 179-191.
    11. João Flávio de Freitas Almeida & Samuel Vieira Conceição & Luiz Ricardo Pinto & Ricardo Saraiva de Camargo & Gilberto de Miranda Júnior, 2018. "Flexibility evaluation of multiechelon supply chains," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-27, March.
    12. Azadeh, Ali & Vafa Arani, Hamed, 2016. "Biodiesel supply chain optimization via a hybrid system dynamics-mathematical programming approach," Renewable Energy, Elsevier, vol. 93(C), pages 383-403.
    13. Bastian, Nathaniel D. & Lunday, Brian J. & Fisher, Christopher B. & Hall, Andrew O., 2020. "Models and methods for workforce planning under uncertainty: Optimizing U.S. Army cyber branch readiness and manning," Omega, Elsevier, vol. 92(C).
    14. Shuangyan Li & Xialian Li & Dezhi Zhang & Lingyun Zhou, 2017. "Joint Optimization of Distribution Network Design and Two-Echelon Inventory Control with Stochastic Demand and CO2 Emission Tax Charges," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-22, January.
    15. Mohammadi, M. & Dehbari, S. & Vahdani, Behnam, 2014. "Design of a bi-objective reliable healthcare network with finite capacity queue under service covering uncertainty," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 72(C), pages 15-41.
    16. Messina, E. & Mitra, G., 1997. "Modelling and analysis of multistage stochastic programming problems: A software environment," European Journal of Operational Research, Elsevier, vol. 101(2), pages 343-359, September.
    17. Javid Jouzdani & Mohammad Fathian & Ahmad Makui & Mehdi Heydari, 2020. "Robust design and planning for a multi-mode multi-product supply network: a dairy industry case study," Operational Research, Springer, vol. 20(3), pages 1811-1840, September.
    18. Fathi, Mahdi & Khakifirooz, Marzieh & Diabat, Ali & Chen, Huangen, 2021. "An integrated queuing-stochastic optimization hybrid Genetic Algorithm for a location-inventory supply chain network," International Journal of Production Economics, Elsevier, vol. 237(C).
    19. Alysson Costa & Lana Santos & Douglas Alem & Ricardo Santos, 2014. "Sustainable vegetable crop supply problem with perishable stocks," Annals of Operations Research, Springer, vol. 219(1), pages 265-283, August.
    20. An, Kun & Ouyang, Yanfeng, 2016. "Robust grain supply chain design considering post-harvest loss and harvest timing equilibrium," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 88(C), pages 110-128.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:endesu:v:24:y:2022:i:9:d:10.1007_s10668-021-01883-2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.