In the process of globalization, customer demand is usually difficult to predict, and product recycling is generally difficult to achieve accurately. It is also urgent to deal with increased inventory while avoiding shortages, with the purpose of reducing supply chain risks. This study analyzes the integrated supply chain decision-making problem in the random product demand and return environment. It proposes a multi-objective optimization model, which is an effective tool to solve the design and planning problems of the global closed-loop supply chain. It consists of a multi-period, single-product and multi-objective mixed integer linear programming model, which can solve some strategic decision problems, including the network structure, entity capacities, flow of products and components, and collection levels, as well as the inventory levels. From the perspective of economic, environmental and social benefits, three objective functions are defined, including maximizing the net present value (NPV) of the system, minimizing the total $C{O}_{2e}$ emissions of supply chain activities, and maximizing social sustainability indicators. Finally, a numerical example is provided to verify the advantages of this model, and sensitivity analysis results are provided. The results show that changes in product demand and return rate will have a great impact on economic and social performance.

Supplier selection for aquatic products E-commerce has aroused widespread concern due to the characteristics of fresh products and the popularity of the network. This paper proposes a novel fuzzy group decision method, which not only integrates Quality Function Deployment (QFD) and improved Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS), but also combines the qualitative analysis and quantitative analysis. Moreover, this method contains two stages, in the first stage, QFD is applied to determine the weights of customer requirements (CRs) and company strategies (CSs) by analyzing the relationship between CRs and CSs, the subjective importance weights of criteria can be derived, in addition, the House of Quality (HOQ) is used to characterize the above process. To address the uncertainty in the selection process, intuitionistic fuzzy numbers (IFNs) are provided to describe the imprecise information, which take membership degree, non-membership degree and hesitation degree into consideration; in the second stage, a novel ranking method is proposed based on the improved TOPSIS, the objective importance weights of criteria are obtained, then the integrated importance criteria weights are calculated and a new ranking index is provided to rank the suppliers. What’s more, a case study illustrates the effectiveness and applicability of the proposed method. Finally, comparative analysis and sensitivity analysis are carried to demonstrate the robustness and effectiveness of the proposed method. The result shows that the proposed TOPSIS-based method can help companies monitor supplier performance and provide customers with quality products and services.