The environmental changes caused by industrial activities have spurred a significant interest in designing supply chain networks by considering environmental issues such as CO2 emission. The pivotal role of taking uncertainty and risk into account in closed-loop supply chain networks has induced numerous researchers and practitioners to develop appropriate decision making tools to cope with these issues in such networks. To design a supply chain regarding environmental impacts under uncertainty of the input data and to cope with the operational risks, this paper proposes a multi objective possibilistic optimization model. The proposed model minimizes traditional costs such as cost of products shipment, purchasing machines and so on, as well as minimizing the environmental impact, and as a results strikes a balance between the two objective functions. Furthermore, in order to solve the proposed multi objective fuzzy mathematical programming model, an interactive fuzzy solution approach is applied. Numerical experiments are used to prove the applicability and feasibility of the developed possibilistic programming model and the usefulness of the applied hybrid solution approach.

Nowadays, the advance and enhance in competitive area, convert the supply chain management into one of the most important issues for industries, organization, and firms. Increasing the quality of products, decreasing the costs, and representing the satisfying service are the primary objectives of organization and managers. Apart from that, the amount of CNGs (such as CO2) has been raised by industrial activities. Therefore, the concern of air pollution motivates managers and researchers to consider this issue in the process. This paper represents a multi objective supply chain network fuzzy programming, which is multi product, multi period, multi-layer, and has reverse product network. Operational risks are considered as deficiency in suppliers’ units and production center. The model’s duty is to choose the optimal suppliers based on different factors such as selling price, the average of deficiency and transportation costs. In order to solve the model, the Jimenez and TH approach are used and for large-scale problems, the paper uses the NSGA-II algorithm.