Abstract: This study presents a novel supplier selection methodology that integrates the Analytic Hierarchy Process (AHP) with a Convolutional Recurrent Neural Network (CRNN) to address the complexities of decision-making in dynamic industrial environments. The AHP component provides a systematic and transparent framework for evaluating many factors, ensuring consistency and minimizing subjective biases in supplier assessment. The Analytic Hierarchy Process (AHP) effectively combines expert knowledge with individual preferences, therefore embodying the human element of decision-making. The CRNN concurrently leverages its ability to process large sequential data, uncover hidden patterns, and assess supplier performance over time. This expertise enhances decision-making by transcending the limitations of traditional analytical methods in managing intricate, multidimensional data. The integration of AHP and CRNN offers a comprehensive evaluation framework, including both objective and subjective factors to enhance effective supplier selection decisions. This approach enhances the long-term sustainability of manufacturing operations by fostering reliable supplier relationships and ensuring access to high-performing suppliers. Experimental validations affirm the efficacy of the suggested approach in promoting sustainable manufacturing systems, highlighting its practical use. The findings demonstrate that the AHP-CRNN framework improves supplier selection criteria and offers prospects for future development and adaptation to address emerging challenges in complex manufacturing environments.

Karim Haricha, Azeddine Khiat, Yassine Issaoui, Ayoub Bahnasse and Hassan Ouajji, “Strategic Supplier Selection in Advanced Automotive Production: Harnessing AHP and CRNN for Optimal Decision-Making” International Journal of Advanced Computer Science and Applications(IJACSA), 16(1), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160151

@article{Haricha2025,
title = {Strategic Supplier Selection in Advanced Automotive Production: Harnessing AHP and CRNN for Optimal Decision-Making},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160151},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160151},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {1},
author = {Karim Haricha and Azeddine Khiat and Yassine Issaoui and Ayoub Bahnasse and Hassan Ouajji}
}