Enhancing Multi -Criteria Decision Making Models through Artificial Intelligence: A Framework Based on Interval Fuzzy Numbers

This article presents an innovative framework aimed at enhancing multi-criteria decision-making (MCDM) models through the integration of artificial intelligence and interval fuzzy numbers. By leveraging interval fuzzy numbers, our model effectively addresses the uncertainties and subjective judgments that frequently emerge during performance evaluations, particularly within the context of the Iranian automotive industry. We initiate the process by calculating initial fuzzy scores based on a comprehensive set of evaluation criteria, subsequently employing advanced deep learning techniques to predict supplier performance. To demonstrate the practical application of our model, we provide a detailed example that compares the predicted scores with actual fuzzy scores, offering valuable insights into each supplier’s strengths and weaknesses. The rankings generated from this comparative analysis empower decision-makers to confidently identify the most suitable partners that align with their specific requirements, thereby streamlining the supplier selection process. Ultimately, this cutting-edge framework not only enhances decision-making accuracy but also fosters strategic partnerships, equipping businesses to effectively navigate the complexities of today’s dynamic market landscape while promoting sustainable growth and competitive advantage.

کلیدواژه ها:

Multi -Criteria ، Decision Making ، Model ، Interval Fuzzy Number ، Artificial Intelligence