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

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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.

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References

  • Al-e-hashem, S. M. J. M., 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,134(1), 28–42.

    Google Scholar 

  • Ashayeri, J., Ma, N., & Sotirov, R. (2014). Supply chain downsizing under bankruptcy: A robust optimization approach. International Journal of Production Economics,154, 1–15.

    Google Scholar 

  • Baghalian, A., Rezapour, S., & Farahani, R. Z. (2013). Robust supply chain network design with service level against disruptions and demand uncertainties: A real-life case. European Journal of Operational Research,227(1), 199–215.

    Google Scholar 

  • Ballou, R. H. (2003). Business logistics: Supply chain management (5th ed.). Upper Saddle River: Prentice Hall.

    Google Scholar 

  • Ben-Tal, A., Ghaoui, L El, & Nemirovski, A. (2009). Robust optimization. Princeton: Princeton University Press.

    Google Scholar 

  • Ben-Tal, A., Goryashko, A., Guslitzer, E., & Nemirovski, A. (2004). Adjustable robust solutions of uncertain linear programs. Mathematical Programming,99(2), 351–376.

    Google Scholar 

  • Ben-Tal, A., & Nemirovski, A. (1999). Robust solutions of uncertain linear programs. Operations Research Letters,25(1), 1–13.

    Google Scholar 

  • Ben-Tal, A., & Nemirovski, A. (2000). Robust solutions of linear programming problems contaminated with uncertain data. Mathematical Programming,88(3), 411–424.

    Google Scholar 

  • Ben-Tal, A., & Nemirovski, A. (2002). Robust optimization? Methodology and applications. Mathematical Programming,92(3), 453–480.

    Google Scholar 

  • Bertsimas, D., & Sim, M. (2003). Robust discrete optimization and network flows. Mathematical Programming,98(1–3), 49–71.

    Google Scholar 

  • Bertsimas, D., & Sim, M. (2004). The price of robustness. Operations Research,52(1), 35–53.

    Google Scholar 

  • Carlsson, D., Flisberg, P., & Rönnqvist, M. (2014). Using robust optimization for distribution and inventory planning for a large pulp producer. Computers & Operations Research,44, 214–225.

    Google Scholar 

  • Christopher, M., & Peck, H. (2004). Building the resilient supply chain. International Journal of Logistics Management,15(2), 1–14.

    Google Scholar 

  • Combe, C. (2006). Introduction to e-business: Management and strategy: Management and strategy (1st ed.). Oxford: Elsevier.

    Google Scholar 

  • Dai, Z., & Zheng, X. (2015). Design of close-loop supply chain network under uncertainty using hybrid genetic algorithm: A fuzzy and chance-constrained programming model. Computers & Industrial Engineering,88, 444–457.

    Google Scholar 

  • Ensafian, H., & Yaghoubi, S. (2017). Robust optimization model for integrated procurement, production and distribution in platelet supply chain. Transportation Research Part E: Logistics and Transportation Review,103(1), 32–55.

    Google Scholar 

  • Eslamipoor, R., Fakhrzad, M. B., & ZareMehrjerdi, Y. (2014). A new robust optimization model under uncertainty for new and remanufactured products. International Journal of Management Science and Engineering Management,10(2), 137–143.

    Google Scholar 

  • Fernandes, L. J., Relvas, S., Alem, D., & Barbosa-Póvoa, A. P. (2016). Robust optimization for petroleum supply chain collaborative design and planning. Computer Aided Chemical Engineering,38, 1569–1574.

    Google Scholar 

  • Gabrel, V., Murat, C., & Thiele, A. (2014). Recent advances in robust optimization: An overview. European Journal of Operational Research,235(3), 471–483.

    Google Scholar 

  • Hasani, A., Zegordi, S. H., & Nikbakhsh, E. (2014). Robust closed-loop global supply chain network design under uncertainty: The case of the medical device industry. International Journal of Production Research,53(5), 1596–1624.

    Google Scholar 

  • Hatefi, S. M. M., & Jolai, F. (2014). Robust and reliable forward-reverse logistics network design under demand uncertainty and facility disruptions. Applied Mathematical Modelling,38(9), 2630–2647.

    Google Scholar 

  • Heckmann, I., Comes, T., & Nickel, S. (2015). A critical review on supply chain risk—definition, measure and modeling. Omega,52, 119–132.

    Google Scholar 

  • Hetland, P. W. (2003). Uncertainty management in appraisal. In N. J. Smith (Ed.), Risk and uncertainty. London: Thomas Telford.

    Google Scholar 

  • Huynh, V. N. (2017). Recent advances of uncertainty management in knowledge modelling and decision making. Annals of Operations Research,256, 199–202.

    Google Scholar 

  • Kannan, G., Fattahi, M., & Keyvanshokooh, E. (2017). Supply chain network design under uncertainty: A comprehensive review and future research directions. European Journal of Operational Research,263(1), 108–141.

    Google Scholar 

  • Khan, O., & Burnes, B. (2007). Risk and supply chain management: Creating a research agenda. The International Journal of Logistics Management,18(2), 197–216.

    Google Scholar 

  • Kim, J. H., Kim, W. C., & Fabozzi, F. J. (2017). Recent advancements in robust optimization for investment management. Annals of Operations Research. https://doi.org/10.1007/s10479-017-2573-5.

    Article  Google Scholar 

  • Kumar, R. S., Choudhary, A., Babu, S. A. I., Kumar, S. K., Goswami, A., & Tiwari, M. K. (2017). Designing multi-period supply chain network considering risk and emission: A multi-objective approach. Annals of Operations Research,250(2), 427–461.

    Google Scholar 

  • Kumar, S. K., Tiwari, M. K., & Babiceanu, R. F. (2010). Minimisation of supply chain cost with embedded risk using computational intelligence approaches. International Journal of Production Research,48(13), 3717–3739.

    Google Scholar 

  • Lalmazloumian, M., Wong, K. Y., Govindan, K., & Kannan, D. (2016). A robust optimization model for agile and build-to-order supply chain planning under uncertainties. Annals of Operations Research,240(2), 435–470.

    Google Scholar 

  • Listeş, O. (2007). A generic stochastic model for supply-and-return network design. Computers & Operations Research,34(2), 417–442.

    Google Scholar 

  • Marzban, S., Mahootchi, M., & Khamseh, A. A. (2015). Developing a multi-period robust optimization model considering American style options. Annals of Operations Research,233(1), 305–320.

    Google Scholar 

  • Minoux, M. (2009). On robust maximum flow with polyhedral uncertainty sets. Optimization Letters,3(3), 367–376.

    Google Scholar 

  • Mulvey, J. M., Vanderbei, R. J., & Zenios, S. A. (1995). Robust optimization of large-scale systems. Operations Research,43(2), 264–281.

    Google Scholar 

  • Nepal, B., & Yadav, O. P. (2015). Bayesian belief network-based framework for sourcing risk analysis during supplier selection. International Journal of Production Research,53(20), 6114–6135.

    Google Scholar 

  • Oliveira, F., Grossmann, I. E., & Hamacher, S. (2014). Accelerating benders stochastic decomposition for the optimization under uncertainty of the petroleum product supply chain. Computers & Operations Research,49, 47–58.

    Google Scholar 

  • Olugu, E. U., & Wong, K. Y. (2012). An expert fuzzy rule based system for closed loop supply chain performance assessment in the automotive industry. Expert System with Application,39, 375–384.

    Google Scholar 

  • Olugu, E. U., Wong, K. Y., & Shaharoun, A. M. (2010). A comprehensive approach in assessing the performance of an automobile closed loop supply chain. Sustainability,2(4), 871–889.

    Google Scholar 

  • Peck, H. (2005). Drivers of supply chain vulnerability: An integrated framework. International Journal of Physical Distribution and Logistics Management,35(4), 210–232.

    Google Scholar 

  • Pishvaee, M. S., Jolai, F., & Razmi, J. (2009). A stochastic optimization model for integrated forward/reverse logistics network design. Journal of Manufacturing Systems,28(4), 107–114.

    Google Scholar 

  • Pishvaee, M. S., Rabbani, M., & Torabi, S. A. (2011). A robust optimization approach to closed-loop supply chain network design under uncertainty. Applied Mathematical Modelling,35(2), 637–649.

    Google Scholar 

  • Pishvaee, M. S., & Torabi, S. A. (2010). A possibilistic programming approach for closed-loop supply chain network design under uncertainty. Fuzzy Sets and Systems,161(20), 2668–2683.

    Google Scholar 

  • Prakash, S., Soni, G., & Rathore, A. P. S. (2016). Multi-echelon closed-loop supply chain network design and configuration under supply risks and logistics risks. International Journal of Logistics Systems and Management,28, 1–23.

    Google Scholar 

  • Prakash, S., Soni, G., & Rathore, A. P. S. (2017). Embedding risk in closed-loop supply chain network design: Case of a hospital furniture manufacturer. Journal of Modelling in Management,12(3), 551–574.

    Google Scholar 

  • PwC. (2015). eCommerce in India accelerating growth, India. PwC. http://www.pwc.in/assets/pdfs/publications/2015/ecommerce-in-india-accelerating-growth.pdf. Accessed April 19, 2016.

  • Ramezani, M., Kimiagari, A. M., Karimi, B., & Hejazi, T. H. (2014). Closed-loop supply chain network design under a fuzzy environment. Knowledge-Based Systems,59, 108–120.

    Google Scholar 

  • Ritchie, B., & Brindley, C. (2007). Supply chain risk management and performance: A guiding framework for future development. International Journal of Operations and Production Management,27(3), 303–322.

    Google Scholar 

  • Shabani, N., Sowlati, T., Ouhimmou, M., & Rönnqvist, M. (2014). Tactical supply chain planning for a forest biomass power plant under supply uncertainty. Energy,78, 346–355.

    Google Scholar 

  • Shukla, A., Lalit, V. A., & Venkatasubramanian, V. (2011). Optimizing efficiency-robustness trade-offs in supply chain design under uncertainty due to disruptions. International Journal of Physical Distribution and Logistics Management,41(6), 623–646.

    Google Scholar 

  • Simangunsong, E. S., Hendry, L., & Stevenson, M. (2012). Supply chain uncertainty: A review and theoretical foundation for future research. International Journal of Production Research,50(16), 4493–4523.

    Google Scholar 

  • Singh, A. R., Mishra, P. K., Jain, R., & Khurana, M. K. (2012). Design of global supply chain network with operational risks. International Journal of Advanced Manufacturing Technology,60(1–4), 273–290.

    Google Scholar 

  • Soleimani, H., Seyyed-Esfahani, M., & Kannan, G. (2014). Incorporating risk measures in closed-loop supply chain network design. International Journal of Production Research,52(6), 1843–1867.

    Google Scholar 

  • Soleimani, H., Seyyed-Esfahani, M., & Shirazi, M. A. (2016). A new multi-criteria scenario-based solution approach for stochastic forward/reverse supply chain network design. Annals of Operations Research,242(2), 399–421.

    Google Scholar 

  • Solo, C. J. (2009). Multi-objective, integrated supply chain design and operation under uncertainty. The Pennsylvania State University. https://etda.libraries.psu.edu/catalog/9709. Accessed March 18, 2016.

  • Soyster, A. L. (1973). Technical note—Convex programming with set-inclusive constraints and applications to inexact linear programming. Operations Research,21(5), 1154–1157.

    Google Scholar 

  • Subulan, K., Baykasoğlu, A., Özsoydan, F. B., Taşan, A. S., & Selim, H. (2014). A case-oriented approach to a lead/acid battery closed-loop supply chain network design under risk and uncertainty. Journal of Manufacturing Systems. https://doi.org/10.1016/j.jmsy.

    Article  Google Scholar 

  • Taki, P., Barzinpour, F., & Teimoury, E. (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,244(2), 619–646.

    Google Scholar 

  • Talaei, M., Babak, F., Moghaddam, M. S., Pishvaee, A Bozorgi-Amiri, & Sepideh, G. (2016). A robust fuzzy optimization model for carbon-efficient closed-loop supply chain network design problem: A numerical illustration in electronics industry. Journal of Cleaner Production,113(1), 662–673.

    Google Scholar 

  • Tang, C. S. (2006). Perspectives in supply chain risk management. International Journal of Production Economics,103(2), 451–488.

    Google Scholar 

  • Wang, H.-F., & Huang, Y.-S. (2013). A two-stage robust programming approach to demand-driven disassembly planning for a closed-loop supply chain system. International Journal of Production Research,51(8), 2414–2432.

    Google Scholar 

  • Wells, P., & Seitz, M. (2006). Business models and closed loop supply chains: A typology. Supply Chain Management: An International Journal,10(4), 249–251.

    Google Scholar 

  • Zhang, Y., & Jiang, Y. (2017). Robust optimization on sustainable biodiesel supply chain produced from waste cooking oil under price uncertainty. Waste Management,60(1), 329–339.

    Google Scholar 

  • Zhu, Q., Sarkis, J., & Lai, K. (2008). Green supply chain management implications for closing the loop. Transportation Research Part E: Logistics and Transportation Review,44(1), 1–18.

    Google Scholar 

  • Zokaee, S., Jabbarzadeh, A., Fahimnia, B., & Sadjadi, S. J. (2014). Robust supply chain network design: An optimization model with real world application. Annals of Operations Research,257(1–2), 15–44.

    Google Scholar 

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Authors and Affiliations

  1. Department of Mechanical Engineering, School of Engineering and Technology, BML Munjal University, Gurgaon, Haryana, India

    Surya Prakash

  2. Opus College of Business, University of St. Thomas, 1000 LaSalle Avenue, Minneapolis, MN, 55403-2005, USA

    Sameer Kumar

  3. Department of Mechanical Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India

    Gunjan Soni & Ajay Pal Singh Rathore

  4. School of Management, Victoria Business School, Victoria University of Wellington, 23, Lambton Quay, Pipitea Campus, PO Box 600, Wellington, 6140, New Zealand

    Vipul Jain

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  1. Surya Prakash
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  2. Sameer Kumar
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  5. Ajay Pal Singh Rathore
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Correspondence to Sameer Kumar.

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Prakash, S., Kumar, S., Soni, G. et al. Closed-loop supply chain network design and modelling under risks and demand uncertainty: an integrated robust optimization approach. Ann Oper Res 290, 837–864 (2020). https://doi.org/10.1007/s10479-018-2902-3

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