IDEAS home Printed from https://ideas.repec.org/a/gam/jlogis/v5y2021i2p20-d531771.html
   My bibliography  Save this article

Designing a Sustainable Green Closed-Loop Supply Chain under Uncertainty and Various Capacity Levels

Author

Listed:
  • Mohsen Tehrani

    (Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA 02115, USA)

  • Surendra M. Gupta

    (Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA 02115, USA)

Abstract

The ever-increasing concerns of the growth in the volume of waste tires and new strict government legislations to reduce the environmental impact of the end-of-life (EOL) tires have increased interest among companies to design a sustainable and efficient closed-loop supply-chain (CLSC) network. In the real world, the CLSC network design is subject to a variety of uncertainties, such as random and fuzzy (epistemic) uncertainties. Designing a reliable and environmentally cautious CLSC with consideration of risks and the uncertainty of the parameters in the network is necessary for a successful supply-chain network. This study proposes a sustainable and environmentally cautious closed-loop supply-chain network for the tire industry, by considering several recovery options, including retreading, recycling, and energy recovery. This study aims to design and develop a robust multi-objective, multi-product, multi-echelon, multi-cycle, multi-capacity, green closed-loop supply-chain network under hybrid uncertainty. There are two types of uncertainties associated with the parameters in the network. There is an uncertainty associated with the demand, which is expressed in some future scenarios according to the probability of their occurrences, as well as fuzzy-based uncertainty associated with return rates, retreading rates, recycling rates, procurement, and production costs, which are expressed with possibilistic distributions. In order to deal with this hybrid uncertainty, a robust fuzzy stochastic programming approach has been proposed, and the proposed mixed integer programming model is applied to a case study in the tire industry to validate the model. The result indicates the applicability of the proposed model and its efficiency to control the hybrid uncertainties and the risk level in the network.

Suggested Citation

  • Mohsen Tehrani & Surendra M. Gupta, 2021. "Designing a Sustainable Green Closed-Loop Supply Chain under Uncertainty and Various Capacity Levels," Logistics, MDPI, vol. 5(2), pages 1-31, April.
    • Handle: RePEc:gam:jlogis:v:5:y:2021:i:2:p:20-:d:531771
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2305-6290/5/2/20/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2305-6290/5/2/20/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Surendra M. Gupta & Aşkıner Güngör & Kannan Govindan & Eren Özceylan & Can Berk Kalaycı & Rajesh Piplani, 2020. "Responsible & sustainable manufacturing," International Journal of Production Research, Taylor & Francis Journals, vol. 58(23), pages 7181-7182, December.
    2. Salehi Sadghiani, N. & Torabi, S.A. & Sahebjamnia, N., 2015. "Retail supply chain network design under operational and disruption risks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 75(C), pages 95-114.
    3. Anil Jindal & Kuldip Singh Sangwan, 2017. "Multi-objective fuzzy mathematical modelling of closed-loop supply chain considering economical and environmental factors," Annals of Operations Research, Springer, vol. 257(1), pages 95-120, October.
    4. Lin, Chitsan & Huang, Chun-Lan & Shern, Chien-Chuan, 2008. "Recycling waste tire powder for the recovery of oil spills," Resources, Conservation & Recycling, Elsevier, vol. 52(10), pages 1162-1166.
    5. Liangchuan Zhou & Surendra M. Gupta, 2020. "Value depreciation factors for new and remanufactured high-technology products: a case study on iPhones and iPads," International Journal of Production Research, Taylor & Francis Journals, vol. 58(23), pages 7218-7249, December.
    6. Vahab Vahdat & Mohammad Ali Vahdatzad, 2017. "Accelerated Benders’ Decomposition for Integrated Forward/Reverse Logistics Network Design under Uncertainty," Logistics, MDPI, vol. 1(2), pages 1-21, December.
    7. Mehmet Talha Dulman & Surendra M. Gupta, 2018. "Evaluation of Maintenance and EOL Operation Performance of Sensor-Embedded Laptops," Logistics, MDPI, vol. 2(1), pages 1-22, January.
    8. Surya Prakash & Sameer Kumar & Gunjan Soni & Vipul Jain & Ajay Pal Singh Rathore, 2020. "Closed-loop supply chain network design and modelling under risks and demand uncertainty: an integrated robust optimization approach," Annals of Operations Research, Springer, vol. 290(1), pages 837-864, July.
    9. 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.
    10. Govindan, K. & Jafarian, A. & Khodaverdi, R. & Devika, K., 2014. "Two-echelon multiple-vehicle location–routing problem with time windows for optimization of sustainable supply chain network of perishable food," International Journal of Production Economics, Elsevier, vol. 152(C), pages 9-28.
    11. Yu, Chian-Son & Li, Han-Lin, 2000. "A robust optimization model for stochastic logistic problems," International Journal of Production Economics, Elsevier, vol. 64(1-3), pages 385-397, March.
    12. Ozden Tozanli & Gazi Murat Duman & Elif Kongar & Surendra M. Gupta, 2017. "Environmentally Concerned Logistics Operations in Fuzzy Environment: A Literature Survey," Logistics, MDPI, vol. 1(1), pages 1-42, June.
    13. D. G. Mogale & Sri Krishna Kumar & Manoj Kumar Tiwari, 2020. "Green food supply chain design considering risk and post-harvest losses: a case study," Annals of Operations Research, Springer, vol. 295(1), pages 257-284, December.
    14. 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.
    15. Patricia Guarnieri & Lucio Camara e Silva & Bárbara de Oliveira Vieira, 2020. "How to Assess Reverse Logistics of e-Waste Considering a Multicriteria Perspective? A Model Proposition," Logistics, MDPI, vol. 4(4), pages 1-31, October.
    Full references (including those not matched with items on IDEAS)

    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. Zahra Homayouni & Mir Saman Pishvaee & Hamed Jahani & Dmitry Ivanov, 2023. "A robust-heuristic optimization approach to a green supply chain design with consideration of assorted vehicle types and carbon policies under uncertainty," Annals of Operations Research, Springer, vol. 324(1), pages 395-435, May.
    2. Vahab Vahdat & Mohammad Ali Vahdatzad, 2017. "Accelerated Benders’ Decomposition for Integrated Forward/Reverse Logistics Network Design under Uncertainty," Logistics, MDPI, vol. 1(2), pages 1-21, December.
    3. Dehghani, Ehsan & Jabalameli, Mohammad Saeed & Jabbarzadeh, Armin, 2018. "Robust design and optimization of solar photovoltaic supply chain in an uncertain environment," Energy, Elsevier, vol. 142(C), pages 139-156.
    4. Jafarian, Ahmad & Asgari, Nasrin & Mohri, Seyed Sina & Fatemi-Sadr, Elham & Farahani, Reza Zanjirani, 2019. "The inventory-routing problem subject to vehicle failure," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 126(C), pages 254-294.
    5. 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.
    6. Tsai, Jung-Fa, 2007. "An optimization approach for supply chain management models with quantity discount policy," European Journal of Operational Research, Elsevier, vol. 177(2), pages 982-994, March.
    7. Antonio G. Martín & Manuel Díaz-Madroñero & Josefa Mula, 2020. "Master production schedule using robust optimization approaches in an automobile second-tier supplier," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 28(1), pages 143-166, March.
    8. Jyoti Dhingra Darbari & Devika Kannan & Vernika Agarwal & P. C. Jha, 2019. "Fuzzy criteria programming approach for optimising the TBL performance of closed loop supply chain network design problem," Annals of Operations Research, Springer, vol. 273(1), pages 693-738, February.
    9. Seyed Babak Ebrahimi & Ehsan Bagheri, 2022. "A multi-objective formulation for the closed-loop plastic supply chain under uncertainty," Operational Research, Springer, vol. 22(5), pages 4725-4768, November.
    10. Lai, K.K. & Wang, Ming & Liang, L., 2007. "A stochastic approach to professional services firms' revenue optimization," European Journal of Operational Research, Elsevier, vol. 182(3), pages 971-982, November.
    11. Yaser Taghinezhad, 2019. "Optimisation model for a chain logistics problem involving chilled food under conditions of uncertainty," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 29(2), pages 103-116.
    12. 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.
    13. Golpîra, Hêriş & Khan, Syed Abdul Rehman, 2019. "A multi-objective risk-based robust optimization approach to energy management in smart residential buildings under combined demand and supply uncertainty," Energy, Elsevier, vol. 170(C), pages 1113-1129.
    14. Erfan Hassannayebi & Seyed Hessameddin Zegordi & Mohammad Reza Amin-Naseri & Masoud Yaghini, 2017. "Train timetabling at rapid rail transit lines: a robust multi-objective stochastic programming approach," Operational Research, Springer, vol. 17(2), pages 435-477, July.
    15. Xu, Y. & Huang, G.H. & Qin, X.S. & Cao, M.F., 2009. "SRCCP: A stochastic robust chance-constrained programming model for municipal solid waste management under uncertainty," Resources, Conservation & Recycling, Elsevier, vol. 53(6), pages 352-363.
    16. Azaron, A. & Brown, K.N. & Tarim, S.A. & Modarres, M., 2008. "A multi-objective stochastic programming approach for supply chain design considering risk," International Journal of Production Economics, Elsevier, vol. 116(1), pages 129-138, November.
    17. Xie, Y.L. & Huang, G.H. & Li, W. & Ji, L., 2014. "Carbon and air pollutants constrained energy planning for clean power generation with a robust optimization model—A case study of Jining City, China," Applied Energy, Elsevier, vol. 136(C), pages 150-167.
    18. Aalaei, Amin & Davoudpour, Hamid, 2017. "A robust optimization model for cellular manufacturing system into supply chain management," International Journal of Production Economics, Elsevier, vol. 183(PC), pages 667-679.
    19. Masoud Hekmatfar & M. R. M. Aliha & Mir Saman Pishvaee & Tomasz Sadowski, 2023. "A Robust Flexible Optimization Model for 3D-Layout of Interior Equipment in a Multi-Floor Satellite," Mathematics, MDPI, vol. 11(24), pages 1-41, December.
    20. Gilani Larimi, Niloofar & Yaghoubi, Saeed & Hosseini-Motlagh, Seyyed-Mahdi, 2019. "Itemized platelet supply chain with lateral transshipment under uncertainty evaluating inappropriate output in laboratories," Socio-Economic Planning Sciences, Elsevier, vol. 68(C).

    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:gam:jlogis:v:5:y:2021:i:2:p:20-:d:531771. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.