Optimization-Driven Vendor Selection with Tabu Search and Neural Networks

Vendor selection in supply chain management is a complex but extremely important process that requires the evaluation of multiple factors such as cost, quality, delivery time, and responsiveness. This study presents a new hybrid methodology that makes the vendor selection process more efficient by combining tabu search optimization and neural network analysis. The proposed model operates in two stages. In the first stage, tabu search algorithms systematically analyze the solution space and identify optimal vendor combinations based on predefined criteria. In the second stage, these selected combinations are evaluated through a neural network. This trained network has the ability to recognize patterns in performance indicators such as cost efficiency, delivery time, and operational wastage. Tests conducted on simulated data clearly demonstrated the superior performance of this hybrid model compared to traditional single-method approaches. The model significantly improved key parameters such as Mean Absolute Error and R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> score. Moreover, it also proved to be adaptable to changing conditions. This framework not only addresses challenges such as scalability and integration of qualitative-quantitative factors in vendor selection, but also highlights the effectiveness of combining optimization algorithms and machine learning techniques for complex supply chain decisions. This model not only serves as a practical decision-support tool for procurement managers but also lays a strong foundation for the development of hybrid AI techniques in the field of operational optimization.