Design of distributionally robust closed-loop supply chain network based on data-driven under disruption risks

References

• Alamdar, S. F., Rabbani, M., & Heydari, J. (2018). Pricing, collection, and effort decisions with coordination contracts in a fuzzy, three-level closed-loop supply chain. Expert Systems with Applications, 104, 261–276. https://doi.org/10.1016/j.eswa.2018.03.029
   Web of Science ®Google Scholar
• Aldrighetti, R., Battini, D., Ivanov, D., & Zennaro, I. (2021). Costs of resilience and disruptions in supply chain network design models: A review and future research directions. International Journal of Production Economics, 235, Article 108103. https://doi.org/10.1016/j.ijpe.2021.108103
   PubMed Web of Science ®Google Scholar
• Al-Salem, M., Diabat, A., Dalalah, D., & Alrefaei, M. (2016). A closed-loop supply chain management problem: Reformulation and piecewise linearization. Journal of Manufacturing Systems, 40, 1–8. https://doi.org/10.1016/j.jmsy.2016.04.001
   Web of Science ®Google Scholar
• Arrow, K. J., Karlin, S., & Scarf, H. E. (1958). Studies in the mathematical theory of inventory and production. Stanford University Press.
   Google Scholar
• Azad, N., Saharidis, G. K., Davoudpour, H., Malekly, H., & Yektamaram, S. A. (2013). Strategies for protecting supply chain networks against facility and transportation disruptions: An improved Benders decomposition approach. Annals of Operations Research, 210(1), 125–163. https://doi.org/10.1007/s10479-012-1146-x
   Web of Science ®Google Scholar
• Beamon, B. M. (1999). Designing the green supply chain. Logistics Information Management, 12(4), 332–342. https://doi.org/10.1108/09576059910284159
   Google Scholar
• Ben-Tal, A., Den Hertog, D., De Waegenaere, A., Melenberg, B., & Rennen, G. (2013). Robust solutions of optimization problems affected by uncertain probabilities. Management Science, 59(2), 341–357. https://doi.org/10.1287/mnsc.1120.1641
   Web of Science ®Google Scholar
• Cao, H., & Ji, X. (2023). Optimal recycling price strategy of clothing enterprises based on closed-loop supply chain. Journal of Industrial and Management Optimization, 19(2), 1350–1366. https://doi.org/10.3934/jimo.2021232
   Web of Science ®Google Scholar
• Carbonara, N., & Pellegrino, R. (2017). How do supply chain risk management flexibility-driven strategies perform in mitigating supply disruption risks? International Journal of Integrated Supply Management, 11(4), 354–379. https://doi.org/10.1504/IJISM.2017.089852
   Google Scholar
• Cheng, C., Adulyasak, Y., & Rousseau, L. M. (2021). Robust facility location under demand uncertainty and facility disruptions. Omega, 103, 102429. https://doi.org/10.1016/j.omega.2021.102429
   Web of Science ®Google Scholar
• Chopra, S., & Sodhi, M. S. (2004). Managing risk to avoid supply-chain breakdown. MIT Sloan Management Review, 46(1), 53–61.
   Web of Science ®Google Scholar
• Delage, E., & Ye, Y. (2010). Distributionally robust optimization under moment uncertainty with application to data-driven problems. Operations Research, 58(3), 595–612. https://doi.org/10.1287/opre.1090.0741
   Web of Science ®Google Scholar
• Dolgui, A., Ivanov, D., & Sokolov, B. (2020). Reconfigurable supply chain: The X-network. International Journal of Production Research, 58(13), 4138–4163. https://doi.org/10.1080/00207543.2020.1774679
   Web of Science ®Google Scholar
• Erdoan, E., & Iyengar, G. (2006). Ambiguous chance constrained problems and robust optimization. Mathematical Programming, 107(1-2), 37–61. https://doi.org/10.1007/s10107-005-0678-0
   Web of Science ®Google Scholar
• Esfahani, P. M., & Kuhn, D. (2018). Data-driven distributionally robust optimization using the Wasserstein metric: Performance guarantees and tractable reformulations. Mathematical Programming, 171(1-2), 115–166. https://doi.org/10.1007/s10107-017-1172-1
   Web of Science ®Google Scholar
• Feng, P., Wu, F., Fung, R. Y., & Jia, T. (2018). Evaluation of two transshipment policies in a two-location decentralized inventory system under partial backordering. Transportation Research Part E: Logistics and Transportation Review, 118, 207–224. https://doi.org/10.1016/j.tre.2018.07.010
   Web of Science ®Google Scholar
• Fournier, N., & Guillin, A. (2015). On the rate of convergence in Wasserstein distance of the empirical measure. Probability Theory and Related Fields, 162(3-4), 707–738. https://doi.org/10.1007/s00440-014-0583-7
   Web of Science ®Google Scholar
• Gao, R., & Kleywegt, A. (2022). Distributionally robust stochastic optimization with Wasserstein distance. Mathematics of Operations Research, 48(2), 603–655. https://doi.org/10.1287/moor.2022.1275
   Web of Science ®Google Scholar
• Ge, C., Zhang, L., & Yuan, Z. (2022). Distributionally robust optimization for the closed–loop supply chain design under uncertainty. AIChE Journal, 68(12), Article e17909. https://doi.org/10.1002/aic.v68.12
   Web of Science ®Google Scholar
• Goli, A. (2023). Integration of blockchain-enabled closed-loop supply chain and robust product portfolio design. Computers and Industrial Engineering, 179, Article 109211. https://doi.org/10.1016/j.cie.2023.109211
   PubMed Web of Science ®Google Scholar
• Goli, A., Ala, A., & Mirjalili, S. (2022). A robust possibilistic programming framework for designing an organ transplant supply chain under uncertainty. Annals of Operations Research, 328(1), 493–530. https://doi.org/10.1007/s10479-022-04829-7
   Web of Science ®Google Scholar
• Goli, A., & Tirkolaee, E. B. (2023). Designing a portfolio-based closed-loop supply chain network for dairy products with a financial approach: Accelerated benders decomposition algorithm. Computers and Operations Research, 155, 106244. https://doi.org/10.1016/j.cor.2023.106244
   Web of Science ®Google Scholar
• Hashemi-Amiri, O., Ghorbani, F., & Ji, R. (2023). Integrated supplier selection, scheduling, and routing problem for perishable product supply chain: A distributionally robust approach. Computers and Industrial Engineering, 175, Article 108845. https://doi.org/10.1016/j.cie.2022.108845
   Web of Science ®Google Scholar
• Hosseini, M. R., Rameezdeen, R., Chileshe, N., & Lehmann, S. (2015). Reverse logistics in the construction industry. Waste Management and Research, 33(6), 499–514. https://doi.org/10.1177/0734242X15584842
   PubMed Web of Science ®Google Scholar
• Hou, J., Zeng, A. Z., & Zhao, L. (2010). Coordination with a backup supplier through buy-back contract under supply disruption. Transportation Research Part E: Logistics and Transportation Review, 46(6), 881–895. https://doi.org/10.1016/j.tre.2010.03.004
   Web of Science ®Google Scholar
• Huang, R., Qu, S., Yang, X., & Liu, Z. (2020). Multi-stage distributionally robust optimization with risk aversion. Journal of Industrial and Management Optimization, 17(1), 233–259. https://doi.org/10.3934/jimo.2019109
   Web of Science ®Google Scholar
• Ivanov, D., & Dolgui, A. (2019). Low-Certainty-Need (LCN) supply chains: A new perspective in managing disruption risks and resilience. International Journal of Production Research, 57(15-16), 5119–5136. https://doi.org/10.1080/00207543.2018.1521025
   Web of Science ®Google Scholar
• Ivanov, D., & Rozhkov, M. (2020). Coordination of production and ordering policies under capacity disruption and product write-off risk: An analytical study with real-data based simulations of a fast moving consumer goods company. Annals of Operations Research, 291(1-2), 387–407. https://doi.org/10.1007/s10479-017-2643-8
   Web of Science ®Google Scholar
• Jabbarzadeh, A., Fahimnia, B., & Sabouhi, F. (2018). Resilient and sustainable supply chain design: Sustainability analysis under disruption risks. International Journal of Production Research, 56(17), 5945–5968. https://doi.org/10.1080/00207543.2018.1461950
   Web of Science ®Google Scholar
• Kannan, G., Sasikumar, P., & Devika, K. (2010). A genetic algorithm approach for solving a closed loop supply chain model: A case of battery recycling. Applied Mathematical Modelling, 34(3), 655–670. https://doi.org/10.1016/j.apm.2009.06.021
   Web of Science ®Google Scholar
• Li, Y., Chen, K., Collignon, S., & Ivanov, D. (2021). Ripple effect in the supply chain network: Forward and backward disruption propagation, network health and firm vulnerability. European Journal of Operational Research, 291(3), 1117–1131. https://doi.org/10.1016/j.ejor.2020.09.053
   Web of Science ®Google Scholar
• Ling, A., Sun, J., Xiu, N., & Yang, X. (2017). Robust two-stage stochastic linear optimization with risk aversion. European Journal of Operational Research, 256(1), 215–229. https://doi.org/10.1016/j.ejor.2016.06.017
   Web of Science ®Google Scholar
• Namdar, J., Li, X., Sawhney, R., & Pradhan, N. (2018). Supply chain resilience for single and multiple sourcing in the presence of disruption risks. International Journal of Production Research, 56(6), 2339–2360. https://doi.org/10.1080/00207543.2017.1370149
   Web of Science ®Google Scholar
• Paydar, M. M., Babaveisi, V., & Safaei, A. S. (2017). An engine oil closed-loop supply chain design considering collection risk. Computers and Chemical Engineering, 104, 38–55. https://doi.org/10.1016/j.compchemeng.2017.04.005
   Web of Science ®Google Scholar
• Poursoltan, L., Seyed-Hosseini, S. M., & Jabbarzadeh, A. (2021). Green closed-loop supply chain network under the covid-19 pandemic. Sustainability, 13(16), 9407 https://doi.org/10.3390/su13169407
   Web of Science ®Google Scholar
• Qzcelik, G., Faruk, Y. Q., & Betul, Y. F. (2021). Robust optimisation for ripple effect on reverse supply chain: An industrial case study. International Journal of Production Research, 59(1), 245–264. https://doi.org/10.1080/00207543.2020.1740348
   Web of Science ®Google Scholar
• Rachev, S. T., & Rschendorf, L. (1998). Mass transportation problems: Volume I: theory (Vol. 1). Springer Science and Business Media.
   Google Scholar
• Roberto, R. C. (2023). Adaptive production control of two-product closed-loop supply chain dynamics under disruptions. Journal of Industrial and Production Engineering, 40(8), 638–660. https://doi.org/10.1080/21681015.2023.2256962
   Web of Science ®Google Scholar
• Sarykalin, S., Serraino, G., & Uryasev, S. (2008). Value-at-risk vs. conditional value-at-risk in risk management and optimization. INFORMS 2008.
   Google Scholar
• Seydanlou, P., Jolai, F., Tavakkoli-Moghaddam, R., & Fathollahi-Fard, A. M. (2022). A multi-objective optimization framework for a sustainable closed-loop supply chain network in the olive industry: Hybrid meta-heuristic algorithms. Expert Systems with Applications, 203, Article 117566. https://doi.org/10.1016/j.eswa.2022.117566
   PubMed Web of Science ®Google Scholar
• Soleimani, H., Chhetri, P., Fathollahi-Fard, A. M., Al-e-Hashem, S. M. J. M., & Shahparvari, S. (2022). Sustainable closed-loop supply chain with energy efficiency: Lagrangian relaxation, reformulations and heuristics. Annals of Operations Research, 318(1), 531–556. https://doi.org/10.1007/s10479-022-04661-z
   Web of Science ®Google Scholar
• Tsao, Y. C., Linh, V. T., & Lu, J. C. (2016). Closed-loop supply chain network designs considering RFID adoption. Computers and Industrial Engineering, 113, 716–726. https://doi.org/10.1016/j.cie.2016.09.016
   Web of Science ®Google Scholar
• Van Wassenhove, L. N., & Guide, V. D. R. (2009). The evolution of closed-loop supply chain research. Operations Research, 57(1), 10–18. https://doi.org/10.1287/opre.1080.0628
   Web of Science ®Google Scholar
• Wiesemann, W., Kuhn, D., & Sim, M. (2014). Distributionally robust convex optimization. Operations Research, 62(6), 1358–1376. https://doi.org/10.1287/opre.2014.1314
   Web of Science ®Google Scholar
• Yuan, Y.,Song, Q., Zhou, B. (2023). A multi-period emergency medical service location problem based on Wasserstein-metric approach using generalised benders decomposition method. International Journal of Systems Science, 54(6), 1173–1185. https://doi.org/10.1080/00207721.2023.2168144
   Web of Science ®Google Scholar
• Zackova, J. (1966). On minimax solutions of stochastic linear programming problems. Časopis pro pěstování matematiky, 91(4), 423–430.
   Google Scholar
• Zhang, Z., Jing, H., & Kao, C. (2023). High-dimensional distributionally robust mean-variance efficient portfolio selection. Mathematics, 11(5), 1272. https://doi.org/10.3390/math11051272
   Web of Science ®Google Scholar