Net Present Value based Supplier Selection Problem under Carbon Emission Constraints: A Hybrid Immune-genetic Algorithm Approach

Supplier selection is critical to supply chain management, influencing operational efficiency, sustainability, and financial performance. This study presents a multi-criteria-based non-linear integer programming model designed to maximize discounted cash flows' net present value (NPV) while meeting carbon emissions constraints. A hybrid immune-genetic algorithm (IGA) is developed, integrating the local search diversity of immune algorithms (IA) with the fast convergence capabilities of genetic algorithms (GA). This combination enhances solution quality by mitigating premature convergence and improving search efficiency. The proposed model and IGA are tested on various problem scales, ranging from small (5 suppliers, 5 products) to large (100 suppliers, 100 products). Comparative results indicate that the IGA consistently outperforms standalone IA and GA in solution accuracy and computational efficiency. This research contributes to advancing meta-heuristic optimization techniques. It offers an effective decision-support tool for businesses to address complex supplier selection challenges while integrating sustainability considerations into supply chain practices.