Today, with the daily increase in population, the demand for agricultural and/or, in general, food products continuously raises and so does the natural resources consumption which induces challenges to the supply chain management of important food products. In the present study, drivers as well as barriers of the agriculture sustainable supply chain are identified and ranked with the help of multicriteria decision-making (MCDM) techniques. We identify six drivers and seven barriers with the help of experts’ opinions in the field and apply ranking methods including TOPSIS-AHP, AHP, and COPRAS-AHP as well as Borda rule and Copeland method to merge the ratings. The results highlight that the economic dimension of sustainability is more important than the environmental and social dimension. Distrust of consumers, lack of understanding and awareness of managers, and performance appraisal problems were found to be the most important barriers. Furthermore, informing the community by the media, entering the global market, exporting products, and producing sustainable products as a competitive advantage were identified to be the most important drivers. The study also shows that public awareness and demands can push the food supply chains toward sustainability goals with the cooperation of governments and suppliers.

In this study, we present a multiobjective mixed-integer nonlinear programming (MINLP) model to design a closed-loop supply chain (CLSC) from production stage to distribution as well as recycling for reproduction. The given network includes production centers, potential points for establishing of distribution centers, retrieval centers, collecting and recycling centers, and the demand points. The presented model seeks to find optimal locations for distribution centers, second-hand product collection centers, and recycling centers under the uncertainty situation alongside the factorys fixed points. The purpose of the presented model is to minimize overall network costs including processing, establishing, and transportation of products and return flows as well as environmental impacts while maximizing social scales and network flexibility according to the presence of uncertainty parameters in the problem. To solve the proposed model with fuzzy uncertainty, first, the improved epsilon (ε)-constraints approach is used to transform a multiobjective to a single-objective problem. Afterward, the Lagrangian relaxation approach is applied to effectively solve the problem. A real-world case study is used to evaluate the performance of the proposed model. Finally, sensitivity analysis is performed to study the effects of important parameters on the optimal solution.