A robust multi-objective optimization model for sustainable closed-loop supply chain network design under demand uncertainty

Abstract

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.