Designing Green and Resilient Dual-Channel Closed-Loop Supply Chain Network Under Disruption Risk Considering Network Flexibility

Document Type : Research Paper

Authors

1 Student, Department of Industrial Engineering, Faculty of Engineering, University of Science and Culture, Tehran, Iran

2 Associate Professor, Department of Industrial Engineering, Faculty of Engineering, University of Science and Culture, Tehran, Iran

10.22084/ier.2025.30588.2197

Abstract

With the increasing use of the Internet and the further development of e-commerce, the number of customers who intend to shop online is increasing every day. In today's business environments that face many uncertainties, supply chains are much more exposed to disruption risks than before. This research presents a two-objective stochastic robust optimization model (economic objective and environmental objective) to design a dual-channel closed-loop supply chain network (online retailer and offline retailer) that uses resilience strategies to deal with disruptions. The innovation of this research is the design of a dual-channel closed-loop supply chain network that is subject to disruption and operational risks, and for the first time, two resilience strategies of backup suppliers and lateral transshipment are considered to deal with them. In addition, network flexibility is also modeled for the first time in such a chain, meaning that customer demands can also be met in other ways such as outsourcing, temporary employment of employees, etc. Due to the complexity of the problem, the Lagrangian relaxation approach has been used to solve it on a large scale, and its proper performance is confirmed by the calculations. The calculations showed that the use of resilience strategies can reduce costs by 3.7% and reduce environmental impacts by 25.5%. In addition, the use of network flexibility has contributed to a 26% reduction in costs and a 79% reduction in negative environmental impacts. The tire manufacturing industry is an example of the application of this model in the real world.

Keywords

Main Subjects

References

  • Gao, S. Lu, H. Cheng, and X. Liu, (2024). “Data-driven robust optimization of dual-channel closed-loop supply chain network design considering uncertain demand and carbon cap-and-trade policy,” Comput. Ind. Eng., 187: 109811 doi: 10.1016/j.cie.2023.109811.
  • Jabbarzadeh, M. Haughton, and A. Khosrojerdi, (2018). “Computers & Industrial Engineering Closed-loop supply chain network design under disruption risks : A robust approach with real world application,” Comput. Ind. Eng., 116: 178–191, doi: 10.1016/j.cie.2017.12.025.
  • Peng, L. V. Snyder, A. Lim, and Z. Liu, (2011). “Reliable logistics networks design with facility disruptions,” Transp. Res. Part B Methodol., 45 (8): 1190–1211. doi: 10.1016/j.trb.2011.05.022.
  • B. Hendricks, V. R. Singhal, and R. Zhang, (2009). “The effect of operational slack, diversification, and vertical relatedness on the stock market reaction to supply chain disruptions,” J. Oper. Manag., 27 (3): 233–246, doi: 10.1016/j.jom.2008.09.001.
  • Z. Farahani, S. Rezapour, T. Drezner, and S. Fallah, (2014). “Competitive supply chain network design: An overview of classifications, models, solution techniques and applications,” Omega, 45: 92–118. doi: 10.1016/j.omega.2013.08.006.
  • S. Pishvaee, M. Rabbani, and S. A. Torabi, (2011). “A robust optimization approach to closed-loop supply chain network design under uncertainty,” Appl. Math. Model., 35 (2): 637–649. doi: 10.1016/j.apm.2010.07.013.
  • Zhalechian, R. Tavakkoli-Moghaddam, B. Zahiri, and M. Mohammadi, (2016). “Sustainable design of a closed-loop location-routing-inventory supply chain network under mixed uncertainty,” Transp. Res. Part E Logist. Transp. Rev., 89: 182–214. doi: 10.1016/j.tre.2016.02.011.
  • Mota, M. I. Gomes, A. Carvalho, and A. P. Barbosa-Povoa, (2018). “Sustainable supply chains: An integrated modeling approach under uncertainty,” Omega, 77: 32–57, doi: 10.1016/j.omega.2017.05.006.
  • G. Mogale, A. De, A. Ghadge, and E. Aktas, (2022). “Multi-objective modelling of sustainable closed-loop supply chain network with price-sensitive demand and consumer’s incentives,” Comput. Ind. Eng., 168: 108105. doi: 10.1016/j.cie.2022.108105.
  • Ganguly, D. Chatterjee, and H. Rao, (2018 ). “The Role of Resiliency in Managing Supply Chains Disruptions,” in Supply Chain Risk Management, Singapore: Springer Singapore: 237–251. doi: 10.1007/978-981-10-4106-8_14.
  • Z. Mehrjerdi and M. Shafiee, (2021). “A resilient and sustainable closed-loop supply chain using multiple sourcing and information sharing strategies,” J. Clean. Prod., 289: 125141. doi: 10.1016/j.jclepro.2020.125141.
  • Akbari-Kasgari, H. Khademi-Zare, M. B. Fakhrzad, M. Hajiaghaei-Keshteli, and M. Honarvar, (2022). “Designing a resilient and sustainable closed-loop supply chain network in copper industry,” Clean Technol. Environ. Policy, 24 (5): 1553–1580, doi: 10.1007/s10098-021-02266-x.
  • Liu, J. Chen, C. Diallo, and U. Venkatadri, (2021). “Pricing and production decisions in a dual-channel closed-loop supply chain with (re)manufacturing,” Int. J. Prod. Econ., 232: 107935, doi: 10.1016/j.ijpe.2020.107935.
  • Zhang, S. Liu, and B. Niu, (2020). “Coordination mechanism of dual-channel closed-loop supply chains considering product quality and return,” J. Clean. Prod., 248: 119273. doi: 10.1016/j.jclepro.2019.119273.
  • M. Fathollahi-Fard, M. A. Dulebenets, M. Hajiaghaei–Keshteli, R. Tavakkoli-Moghaddam, M. Safaeian, and H. Mirzahosseinian, (2021). “Two hybrid meta-heuristic algorithms for a dual-channel closed-loop supply chain network design problem in the tire industry under uncertainty,” Adv. Eng. Informatics, 50: 101418. doi: 10.1016/j.aei.2021.101418.
  • Kaoud, M. A. M. Abdel-Aal, T. Sakaguchi, and N. Uchiyama, (2020 ). “Design and Optimization of the Dual-Channel Closed Loop Supply Chain with E-Commerce,” Sustainability, 12 (23): 10117. doi: 10.3390/su122310117.
  • Kazancoglu, D. Yuksel, M. D. Sezer, S. K. Mangla, and L. Hua, (2022). “A Green Dual-Channel Closed-Loop Supply Chain Network Design Model,” J. Clean. Prod., 332: 130062. doi: 10.1016/j.jclepro.2021.130062.
  • Keyvanshokooh, S. M. Ryan, and E. Kabir, (2016). “Hybrid robust and stochastic optimization for closed-loop supply chain network design using accelerated Benders decomposition,” Eur. J. Oper. Res., 249 (1): 76–92. doi: 10.1016/j.ejor.2015.08.028.
  • M. Paydar, V. Babaveisi, and A. S. Safaei, (2017). “An engine oil closed-loop supply chain design considering collection risk,” Comput. Chem. Eng., 104: 38–55. doi: 10.1016/j.compchemeng.2017.04.005.
  • M. Mulvey, R. J. Vanderbei, and S. A. Zenios, (1995). “Robust Optimization of Large-Scale Systems,” Oper. Res., vol. 43 (2): 264–281. doi: 10.1287/opre.43.2.264.
  • M. Hatefi and F. Jolai, (2014). “Robust and reliable forward–reverse logistics network design under demand uncertainty and facility disruptions,” Appl. Math. Model., 38, 9–10: 2630–2647. doi: 10.1016/j.apm.2013.11.002.
  • -H. Aghezzaf, C. Sitompul, and N. M. Najid, (2010). “Models for robust tactical planning in multi-stage production systems with uncertain demands,” Comput. Oper. Res., 37 (5): 880–889. doi: 10.1016/j.cor.2009.03.012.
  • Lotfi, Z. Sheikhi, M. Amra, M. AliBakhshi, and G.-W. Weber, (2021). “Robust optimization of risk-aware, resilient and sustainable closed-loop supply chain network design with Lagrange relaxation and fix-and-optimize,” Int. J. Logist. Res. Appl.: 1–41. doi: 10.1080/13675567.2021.2017418.
  • Yu and W. D. Solvang, (2020). “A fuzzy-stochastic multi-objective model for sustainable planning of a closed-loop supply chain considering mixed uncertainty and network flexibility,” J. Clean. Prod., 266: 121702, doi: 10.1016/j.jclepro.2020.121702.
  • M. Vali-siar and E. Roghanian, (2022). “Sustainable , resilient and responsive mixed supply chain network design under hybrid uncertainty with considering COVID-19 pandemic disruption,” Sustain. Prod. Consum., 30: 278–300, doi: 10.1016/j.spc.2021.12.003.
  • Fahimnia, A. Jabbarzadeh, A. Ghavamifar, and M. Bell, (2017). “Supply chain design for efficient and effective blood supply in disasters,” Int. J. Prod. Econ., 183: 700–709, doi: 10.1016/j.ijpe.2015.11.007.
  • Soleimani and G. Kannan, (2015). “A hybrid particle swarm optimization and genetic algorithm for closed-loop supply chain network design in large-scale networks,” Appl. Math. Model., 39 (14): 3990–4012. doi: 10.1016/j.apm.2014.12.016.
  • L. Fisher, (2004). “The Lagrangian Relaxation Method for Solving Integer Programming Problems,” Manage. Sci., 50 (12): 1861–1871. doi: 10.1287/mnsc.1040.0263.
Journal of Industrial Engineering Research in Production Systems
Volume 12, Issue 25
March 2025
Pages 99-119

Files

Share

How to cite

Statistics