A multi-objective environmental and economic design of closed-loop supply chain and production planning under a circcular economy perspective

The transition toward circular production systems requires integrated environmental decision-support frameworks capable of capturing the interactions between manufacturing, logistics, and resource recovery processes. Despite extensive research on closed-loop supply chains (CLSCs), limited studies have developed unified multi-period models that simultaneously integrate production planning, recycling operations, and environmental performance within a system-level optimization structure. This study proposes a multi-product, multi-period mixed-integer linear programming (MILP) model for the integrated design and planning of a closed-loop supply chain under a circular economy perspective. The framework links forward production flows with reverse logistics and recycling activities while incorporating flexible capacity structures (regular time, overtime, and outsourcing) at both manufacturing plants and recycling centers. A bi-objective formulation minimizes total operational cost and total CO 2 emissions generated from production and transportation activities. The e-constraint method is employed to generate Pareto-efficient solutions and analyze trade-offs between economic and environmental objectives. A numerical case study demonstrates the applicability of the model. Results reveal that by accepting a modest 4.29% increase in total costs, decision-makers can implement a highly sustainable operational plan with only a 6.17% deviation from the theoretical minimum emissions. Furthermore, sensitivity analyses indicate that logistics operations play a primary role in the decarbonization process; specifically, a 10% reduction in transportation emissions improves the overall environmental footprint by 9.12%, whereas a similar improvement in production yields only a 1.67% effect. Additionally, capacity configuration decisions significantly affect the cost–emission balance. The proposed framework contributes to environmental systems modeling by integrating aggregate production planning and closed-loop supply chain decisions into a unified optimization structure, providing quantitative support for sustainable resource management and greenhouse gas mitigation in circular production networks.