In response to the growing adoption of circular economy (CE) principles and heightened environmental concerns, businesses are increasingly integrating green practices into supply chain management to achieve both economic and environmental benefits. Despite this shift, a significant research gap remains in the optimization of green dual-channel closed-loop supply chain (G-DCCLSC) networks, especially when considering multi-product, multi-period scenarios with uncertain demand. This study addresses this gap by formulating a multi-objective optimization model aimed at reducing total costs and environmental impacts, specifically CO2 and particulate matter (PM) emissions. The research employs a multi-objective mixed integer linear programming (MO-MILP) model in conjunction with the non-dominated sorting genetic algorithm II (NSGA-II) to meet these dual objectives. Results indicate that the proposed model significantly enhances the balance between economic and environmental objectives compared to existing models that typically focus on single-product, single-period scenarios. The solutions derived offer a range of Pareto-optimal choices, effectively illustrating the trade-offs between costs and emissions. The study underscores the necessity of incorporating both forward and reverse logistics in supply chain design to achieve true sustainability. Through a comprehensive case study of an electrics business in Vietnam, the practical applicability of the model is demonstrated, providing robust insights for optimizing G-DCCLSC networks under realistic conditions. This study adds to the subject of sustainable supply chain management by offering an improved optimization framework that incorporates economic and environmental objectives.
