The closed-loop supply chain network design (CLSCND) involves integrating decision-making for both forward and reverse logistics. The significance of this design has increased due to various factors such as maximizing economic performance, competitive pressures, environmental concerns, resource utilization, and sustainability. In this study, we focus on a novel CLSCND that spans multiple periods and includes regional products. This research presents a mathematical model with two objectives: minimizing costs and minimizing environmental issues while focusing on imperfect quality production. This research begins by developing a mixed integer linear programming model to design a closed-loop supply chain network under uncertainty. The proposed model is then formulated based on robust optimization approaches to protect the network against uncertainty. Demand forecasting, specifically for nondeterministic dynamic demand, is addressed using the vector autoregressive time series model. Finally, numerical examples and sensitivity analysis on key parameters are provided to examine the behavior of the network’s strength and validate the proposed model that offers valuable insights into effectively managing this problem. The results indicate that the unfulfilled demand significantly influences the overall cost of the supply chain.