Optimization of Manufacturing-Remanufacturing Model in Circular Supply Chain Considering Warehouse Capacity Constraints by Using Chinese Pangolin Optimizer Algorithm

This research developed an optimization model within a circular supply chain framework incorporating factors such as carbon emissions, social sustainability, and warehouse capacity limitations. The model adopted a modified Economic Order Quantity (EOQ) approach, with a comprehensive cost assessment that included production cost, remanufacturing cost, storage cost, disposal cost, and penalty cost for emissions, all formulated within a Mixed Integer Nonlinear Programming (MINLP) structure. To address the complex nonlinear problem, the metaheuristic Chinese Pangolin Optimizer (CPO) algorithm was applied, as it effectively balanced solution exploration and exploitation. The simulation results indicated the optimal combination of production lot size, remanufacturing, and the share of reusable goods, achieving the minimum total system cost. The sensitivity analysis showed the significant influence of production and remanufacturing costs, emissions, and the rate of product returns on system efficiency. Overall, this research demonstrated more credible, cost-efficient, and sustainable inventory control approaches in a circular supply chain by considering warehouse constraints and applying the CPO.