At this stage, the clothing logistics industry still has some shortcomings in terms of rapid response performance and warehouse integration, and the operation of reverse logistics network planning is also in the primary development stage. Therefore, on the basis of considering the forward garment logistics, the return and replacement process of garments should be taken into account to achieve a win-win situation for economic, environmental and social benefits. In addition, the impact of the existence of uncertain factors on the network planning is considered to establish a scientific and reasonable clothing closed-loop supply chain network system. Based on the distribution characteristics of clothing enterprises and supporting facilities, the location model was proposed to determine the optimal location and quantity of the corresponding facilities. At the same time, taking into account the carbon emissions in the construction, manufacturing and transportation process and the social responsibility of the enterprise, the environmental and social risk assessment targets are added on the basis of maximizing the profit of the enterprise, and a multi-objective planning model of transportation vehicle path optimization is established. According to the characteristics of the model, a two-stage algorithm is designed. The first stage obtains the optimal initial solution through the greedy algorithm, and the second stage solves the bi-level programming model by the particle swarm optimization algorithm. Finally, based on the data of a certain city, the parameter assignment of the model is carried out, and the problem is solved by CPLEX optimization software. The feasibility and correctness of the model and algorithm are verified by several numerical examples and sensitivity analysis of model parameters.

Yixin Xu. A robust multi-objective optimization model for sustainable closed-loop supply chain network design under demand uncertainty. Academic Journal of Business & Management (2021) Vol. 3, Issue 1: 27-43. https://doi.org/10.25236/AJBM.2021.030104.

[1] United Nations. Transforming Our World: the 2030 Agenda for Sustainable Development. New York, 2015–10–21, https: //www.un.org/ga/search/view doc.asp?symbol=A/RES/70/1&Lang=E., 2015.
[2] Mota B, Gomes M I, Carvalho A, et al. Sustainable supply chains: An integrated modeling approach under uncertainty [J]. Omega, 2017, 77 (JUN.): 32-57.
[3] Hawks, K. What is reverse logistics. Reverse Logist. Mag, 2006, 1 (1).
[4] Perez-Fortes M, Lainez-Aguirre J M, Arranz-Piera P, et al. Design of regional and sustainable bio-based networks for electricity generation using a multi-objective MILP approach [J]. Energy, 2012, 44 (1): 79-95.
[5] Varsei M, Polyakovskiy S. Sustainable supply chain network design: A case of the wine industry in Australia [J]. Omega, 2017.
[6] Chaabane A, Ramudhin A, Paquet M. Design of sustainable supply chains under the emission trading scheme [J]. International Journal of Production Economics, 2012, 135 (1): 37-49.
[7] SAHEBJAMNIA N, FARD A M F, HAJIAGHAEI-KESHTELI M. Sustainable tire closed-loop supply chain network design: Hybrid metaheuristic algorithms for large-scale networks. Journal of Cleaner Production, 2018, 196 (20): 273 – 296.
[8] Lee D H, Dong M. Dynamic network design for reverse logistics operations under uncertainty [J]. Transportation Research Part E Logistics & Transportation Review, 2009, 45 (1): 61-71.
[9] Pishvaee M S, Jolai F, Razmi J. A stochastic optimization model for integrated forward/reverse logistics network design [J]. Journal of Manufacturing Systems, 2009, 28 (4): 107-114.
[10] Hasani A, Zegordi S H, Nikbakhsh E. Robust closed-loop supply chain network design for perishable goods in agile manufacturing under uncertainty [J]. International Journal of Production Research, 2012, 50 (14-16): 4649-4669.
[11] Kim H, Yang J, Lee K D. Vehicle routing in reverse logistics for recycling end-of-life consumer electronic goods in South Korea [J]. Transportation Research Part D Transport & Environment, 2009, 14 (5): 291-299.
[12] Diabat A, Kannan D, Kaliyan M, et al. A optimization model for product returns using genetic algorithms and artificial immune system [J]. Resources Conservation and Recycling, 2013, 74 (2013): 156-169.