<![CDATA[This study proposes a hybrid decision-making model that integrates the Best-Worst Method (BWM) with the Analytic Hierarchy Process (AHP) to optimize supplier selection. The primary objective is to address limitations in traditional Multi-Criteria Decision-Making (MCDM) methods, such as inconsistency, subjectivity, and cognitive overload when handling complex criteria. The proposed model leverages AHP's hierarchical structuring and BWM’s efficiency in reducing comparison load, aiming for a more accurate and consistent evaluation framework. The research design involves developing a hybrid AHP-BWM model and applying it to a dataset from the Vietnamese Textile and Apparel (T&A) sector. The methodology includes two stages: determining the weight of each criterion using a Hesitant-AHP approach, followed by evaluating supplier alternatives with BWM. The performance of the model is assessed using classification metrics, namely accuracy, precision, recall, and F1-score. The results show that the proposed model outperforms conventional methods such as TOPSIS, ELECTRE, VIKOR, and SWARA. It achieves an accuracy of 92%, precision of 87%, recall of 86%, and an F1-score of 86%. These outcomes confirm the model’s superior ability to consistently classify supplier suitability. Furthermore, the model identifies Quality Assurance as the most critical criterion, followed by Assistance, Capacity, Charge, and Shipment. In conclusion, the hybrid AHP-BWM model offers a robust, scalable, and data-driven approach for supplier selection. Its strength lies in balancing systematic evaluation with reduced cognitive effort, making it suitable for complex real-world decision-making environments. Future research may explore its application in other domains and enhance its scalability for larger datasets.]]>
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