Aiming at the optimal choice of sales channel structure in the closed-loop supply chain, considering the uncertainty of market demand and quality level of recycled products, four average gross profit models for the closed-loop supply chain system with four sales channel structures under the government differentially weighted subsidy were constructed with the objective of maximizing the gross profit. Firstly, Fuzzy Chance Constrained Programming (FCCP) method was used to transform the fuzzy constraints into clear corresponding expressions equivalently. Then, Particle Swarm Optimization (PSO) algorithm and Genetic Algorithm (GA) were used to solve numerical examples of the model comparatively. Finally, sensitivity analysis was performed on the parameters. The results show that the maximum difference ratio of the above two algorithms is 0.018%, indicating that both algorithms do not fall into the local optimal solution, which verifies the validity of the algorithms and the confidence of the models. Enterprises can formulate optimal recycling, production and sales strategies according to different confidence levels of the potential demands, choose the optimal channel structure and increase the gross profit gradually.

In order to solve the problems of high economic costs, large amount of carbon emissions and insufficient attention to social benefits in the closed-loop logistics network for fresh foods, a multi-objective closed-loop logistics network model for fresh foods under uncertain conditions was established by considering the uncertainty of return quantity and aiming at the minimum economic costs, the minimum carbon emissions and the maximum social benefits. Firstly, the improved Genetic Algorithm (GA) was used to solve the model. Then, the feasibility of the model was verified by combining the operation and management data of a fresh food enterprise in Shanghai. Finally, the results of improved GA was compared to the results of Particle Swarm Optimization (PSO) algorithm to verify the effectiveness of the algorithm， and to highlight the superiority of the improved GA in solving multi-objective complex constraint problems. The example results show that the satisfaction degree of multi-objective optimization is 0.92, which is higher than that of single-objective optimization, demonstrating the effectiveness of the proposed model.

Concerning the high frequency logistics distribution of fresh products due to the products' perishability and vulnerability, as well as the uncertainty of demand and return, a multi-period closed-loop logistics network for fresh products with fuzzy variables was constructed to achieve the multi-decision arrangement of minimum system cost, optimal facility location and optimal delivery route. In order to solve the Fuzzy Mixed Integer Linear Programming (FMILP) model corresponding to the system, firstly, the amounts of demand and return were defined as triangular fuzzy parameters; secondly, the fuzzy constraints were transformed into crisp formula by using fuzzy chance constrained programming method; finally, the optimal solution of case was obtained by using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm. The experimental results show that multi-period closed-loop system performs better than single-period system in the aspect of multi-decision programming, meanwhile, the confidence levels of triangular fuzzy parameters have significant influence on the optimal operation of enterprise, thus providing a reference for relevant decision makers.