IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v290y2020i1d10.1007_s10479-018-2902-3.html
   My bibliography  Save this article

Closed-loop supply chain network design and modelling under risks and demand uncertainty: an integrated robust optimization approach

Author

Listed:
  • Surya Prakash

    (BML Munjal University)

  • Sameer Kumar

    (University of St. Thomas)

  • Gunjan Soni

    (Malaviya National Institute of Technology)

  • Vipul Jain

    (Victoria University of Wellington)

  • Ajay Pal Singh Rathore

    (Malaviya National Institute of Technology)

Abstract

Closed loop supply chain network design (CL-SCND) is a critical economic and environmental activity. The closing of the loop to handle return, uncertainty in business environment, various supply chain risks, impact network design processes and performance of the firm in the long term. Thus, it is important to design robust and reliable supply chain structures and obtain network configurations which can always outperform the other configurations under the worst cases of risks and uncertainty. A generic closed-loop supply chain network based on mixed integer programming formulation is proposed with direct shipping to the customer from manufacturing plants as well as shipping through distribution centers under supply risks, transportation risk and uncertain demand using a robust optimization (RO) approach. A large number of numerical tests are carried out to test the performance of the model by considering a total of four levels of uncertainty for four different network structures types. The results of the tests confirm that the risk and uncertainty based integrated supply chain network models are more efficient (cost effective) than the other set of network configurations which treats the supply chain risks and uncertainty post-ante. To demonstrate the applicability of the proposed model, the case of an Indian e-commerce firm which wants to redesign its supply chain structure is presented. The results of case study show that the topology obtained from integrated treatment of risk and uncertainty called as RORU model, outperform other supply chain networks on various network performance indicators such as supply chain costs, the number of facilities open or close and the amount of products flowing through supply chain echelon. Thus, RO based mathematical modeling to address risks and its applicability for SCND for close loop supply chain is proposed, demonstrated and applied in practical cases.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:annopr:v:290:y:2020:i:1:d:10.1007_s10479-018-2902-3
    DOI: 10.1007/s10479-018-2902-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-018-2902-3
    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/s10479-018-2902-3?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. Ashayeri, J. & Ma, N. & Sotirov, R., 2014. "Supply chain downsizing under bankruptcy: A robust optimization approach," International Journal of Production Economics, Elsevier, vol. 154(C), pages 1-15.
    2. Tang, Christopher S., 2006. "Perspectives in supply chain risk management," International Journal of Production Economics, Elsevier, vol. 103(2), pages 451-488, October.
    3. Heckmann, Iris & Comes, Tina & Nickel, Stefan, 2015. "A critical review on supply chain risk – Definition, measure and modeling," Omega, Elsevier, vol. 52(C), pages 119-132.
    4. Dimitris Bertsimas & Melvyn Sim, 2004. "The Price of Robustness," Operations Research, INFORMS, vol. 52(1), pages 35-53, February.
    5. Zhu, Qinghua & Sarkis, Joseph & Lai, Kee-hung, 2008. "Green supply chain management implications for "closing the loop"," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 44(1), pages 1-18, January.
    6. B Ritchie & C Brindley, 2007. "An emergent framework for supply chain risk management and performance measurement," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(11), pages 1398-1411, November.
    7. Bimal Nepal & Om Prakash Yadav, 2015. "Bayesian belief network-based framework for sourcing risk analysis during supplier selection," International Journal of Production Research, Taylor & Francis Journals, vol. 53(20), pages 6114-6135, October.
    8. Peyman Taki & Farnaz Barzinpour & Ebrahim Teimoury, 2016. "Risk-pooling strategy, lead time, delivery reliability and inventory control decisions in a stochastic multi-objective supply chain network design," Annals of Operations Research, Springer, vol. 244(2), pages 619-646, September.
    9. Morteza Lalmazloumian & Kuan Yew Wong & Kannan Govindan & Devika Kannan, 2016. "A robust optimization model for agile and build-to-order supply chain planning under uncertainties," Annals of Operations Research, Springer, vol. 240(2), pages 435-470, May.
    10. Van-Nam Huynh, 2017. "Recent advances of uncertainty management in knowledge modelling and decision making," Annals of Operations Research, Springer, vol. 256(2), pages 199-202, September.
    11. Ezutah Udoncy Olugu & Kuan Yew Wong & Awaludin Mohamed Shaharoun, 2010. "A Comprehensive Approach in Assessing the Performance of an Automobile Closed-Loop Supply Chain," Sustainability, MDPI, vol. 2(4), pages 1-19, March.
    12. ,, 2000. "Problems And Solutions," Econometric Theory, Cambridge University Press, vol. 16(2), pages 287-299, April.
    13. Shabani, Nazanin & Sowlati, Taraneh & Ouhimmou, Mustapha & Rönnqvist, Mikael, 2014. "Tactical supply chain planning for a forest biomass power plant under supply uncertainty," Energy, Elsevier, vol. 78(C), pages 346-355.
    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. Gabrel, Virginie & Murat, Cécile & Thiele, Aurélie, 2014. "Recent advances in robust optimization: An overview," European Journal of Operational Research, Elsevier, vol. 235(3), pages 471-483.
    16. Ensafian, Hamidreza & Yaghoubi, Saeed, 2017. "Robust optimization model for integrated procurement, production and distribution in platelet supply chain," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 103(C), pages 32-55.
    17. Hamed Soleimani & Mirmehdi Seyyed-Esfahani & Mohsen Akbarpour Shirazi, 2016. "A new multi-criteria scenario-based solution approach for stochastic forward/reverse supply chain network design," Annals of Operations Research, Springer, vol. 242(2), pages 399-421, July.
    18. 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.
    19. Mirzapour Al-e-hashem, S.M.J. & Malekly, H. & Aryanezhad, M.B., 2011. "A multi-objective robust optimization model for multi-product multi-site aggregate production planning in a supply chain under uncertainty," International Journal of Production Economics, Elsevier, vol. 134(1), pages 28-42, November.
    20. Ravi Shankar Kumar & Alok Choudhary & Soudagar A. K. Irfan Babu & Sri Krishna Kumar & A. Goswami & M. K. Tiwari, 2017. "Designing multi-period supply chain network considering risk and emission: a multi-objective approach," Annals of Operations Research, Springer, vol. 250(2), pages 427-461, March.
    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. M. J. Hermoso-Orzáez & J. Garzón-Moreno, 2022. "Risk management methodology in the supply chain: a case study applied," Annals of Operations Research, Springer, vol. 313(2), pages 1051-1075, June.
    2. Wang, Xue-Chao & Jiang, Peng & Yang, Lan & Fan, Yee Van & Klemeš, Jiří Jaromír & Wang, Yutao, 2021. "Extended water-energy nexus contribution to environmentally-related sustainable development goals," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    3. Zhou, Yongyi & Zhang, Yulin & Wahab, M.I.M. & Goh, Mark, 2023. "Channel leadership and performance for a closed-loop supply chain considering competition," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 175(C).
    4. 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.
    5. Mingqiang Yin & Min Huang & Xiaohu Qian & Dazhi Wang & Xingwei Wang & Loo Hay Lee, 2023. "Fourth-party logistics network design with service time constraint under stochastic demand," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1203-1227, 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. Yang Hu, 2023. "Perspectives in closed-loop supply chains network design considering risk and uncertainty factors," Papers 2306.04819, arXiv.org.
    8. Pin, Lantos A. & Pennink, Bartjan J.W. & Balsters, Herman & Sianipar, Corinthias P.M., 2021. "Technological appropriateness of biomass production in rural settings: Addressing water hyacinths (E. crassipes) problem in Lake Tondano, Indonesia," Technology in Society, Elsevier, vol. 66(C).
    9. Simonetto, Marco & Sgarbossa, Fabio & Battini, Daria & Govindan, Kannan, 2022. "Closed loop supply chains 4.0: From risks to benefits through advanced technologies. A literature review and research agenda," International Journal of Production Economics, Elsevier, vol. 253(C).
    10. Jose Alejandro Cano & Abraham Londoño-Pineda & Maria Fanny Castro & Hugo Bécquer Paz & Carolina Rodas & Tatiana Arias, 2022. "A Bibliometric Analysis and Systematic Review on E-Marketplaces, Open Innovation, and Sustainability," Sustainability, MDPI, vol. 14(9), pages 1-42, May.
    11. Rimalini Gadekar & Bijan Sarkar & Ashish Gadekar, 2022. "Key performance indicator based dynamic decision-making framework for sustainable Industry 4.0 implementation risks evaluation: reference to the Indian manufacturing industries," Annals of Operations Research, Springer, vol. 318(1), pages 189-249, November.

    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. Behzadi, Golnar & O’Sullivan, Michael Justin & Olsen, Tava Lennon & Zhang, Abraham, 2018. "Agribusiness supply chain risk management: A review of quantitative decision models," Omega, Elsevier, vol. 79(C), pages 21-42.
    2. Shiva Zokaee & Armin Jabbarzadeh & Behnam Fahimnia & Seyed Jafar Sadjadi, 2017. "Robust supply chain network design: an optimization model with real world application," Annals of Operations Research, Springer, vol. 257(1), pages 15-44, October.
    3. Almaraj, Ismail I. & Trafalis, Theodore B., 2019. "An integrated multi-echelon robust closed- loop supply chain under imperfect quality production," International Journal of Production Economics, Elsevier, vol. 218(C), pages 212-227.
    4. Mohammaddust, Faeghe & Rezapour, Shabnam & Farahani, Reza Zanjirani & Mofidfar, Mohammad & Hill, Alex, 2017. "Developing lean and responsive supply chains: A robust model for alternative risk mitigation strategies in supply chain designs," International Journal of Production Economics, Elsevier, vol. 183(PC), pages 632-653.
    5. Hêris Golpîra, 2017. "Robust bi-level optimization for an opportunistic supply chain network design problem in an uncertain and risky environment," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 27(1), pages 21-41.
    6. Jabbarzadeh, Armin & Haughton, Michael & Pourmehdi, Fahime, 2019. "A robust optimization model for efficient and green supply chain planning with postponement strategy," International Journal of Production Economics, Elsevier, vol. 214(C), pages 266-283.
    7. 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.
    8. 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.
    9. Hanks, Robert W. & Weir, Jeffery D. & Lunday, Brian J., 2017. "Robust goal programming using different robustness echelons via norm-based and ellipsoidal uncertainty sets," European Journal of Operational Research, Elsevier, vol. 262(2), pages 636-646.
    10. 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.
    11. Mavrotas, George & Figueira, José Rui & Siskos, Eleftherios, 2015. "Robustness analysis methodology for multi-objective combinatorial optimization problems and application to project selection," Omega, Elsevier, vol. 52(C), pages 142-155.
    12. Cleber D. Rocco & Reinaldo Morabito, 2016. "Robust optimisation approach applied to the analysis of production / logistics and crop planning in the tomato processing industry," International Journal of Production Research, Taylor & Francis Journals, vol. 54(19), pages 5842-5861, October.
    13. 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.
    14. Henao, César Augusto & Ferrer, Juan Carlos & Muñoz, Juan Carlos & Vera, Jorge, 2016. "Multiskilling with closed chains in a service industry: A robust optimization approach," International Journal of Production Economics, Elsevier, vol. 179(C), pages 166-178.
    15. Marla, Lavanya & Rikun, Alexander & Stauffer, Gautier & Pratsini, Eleni, 2020. "Robust modeling and planning: Insights from three industrial applications," Operations Research Perspectives, Elsevier, vol. 7(C).
    16. 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.
    17. Govindan, Kannan & Fattahi, Mohammad, 2017. "Investigating risk and robustness measures for supply chain network design under demand uncertainty: A case study of glass supply chain," International Journal of Production Economics, Elsevier, vol. 183(PC), pages 680-699.
    18. 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.
    19. Shuihua Han & Yue Jiang & Ling Zhao & Stephen C. H. Leung & Zongwei Luo, 2020. "Weight reduction technology and supply chain network design under carbon emission restriction," Annals of Operations Research, Springer, vol. 290(1), pages 567-590, July.
    20. 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.

    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:annopr:v:290:y:2020:i:1:d:10.1007_s10479-018-2902-3. 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.