Artificial Intelligence-Assisted Multi-Criteria Decision-Making Methodology: From Research Trends to the Future Roadmap

Abstract

Bibliometric analysis is a popular methodology in recent years that provides valuable insights for literature and researchers by visualizing interesting trends, relationship patterns, and information flow in research areas. This study aims to evaluate the publication trends, author contributions, institutional collaborations, and citation dynamics of this field by examining the integration of Multi-Criteria Decision Making (MCDM) and Artificial Intelligence (AI) with bibliometric analysis methods. This integration optimizes complex decision-making processes and provides faster, consistent, and effective solutions. The analysis was performed using performance analysis and science mapping techniques. Data were collected from the WoS database and 993 articles covering the period from 1992 to 2024 were analyzed. Co-citation, keyword co- occurrence, and co-authorship analyses were visualized with VOSviewer software. Accordingly, India, China and Iran stand out as the countries with the most publications, while the Indian Institute of Technology has the highest contribution. ‘Annals of Operations Research’ and ‘Expert Systems with Applications’ were among the most frequently cited journals. University of Technology Sydney and King Abdulaziz University stood out in institutional collaboration. This study is a pioneering study that conducts bibliometric analysis for AI-MCDM methods, especially in terms of the subject, scope and some of the findings obtained, and has produced valuable insights through data analytics.