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, which provides valuable insights, is a pioneering study that performs bibliometric analysis for AI-MCDM methods, especially in terms of title emphasis and some of the findings obtained.

Keywords

• Bibliometric analysis
• Web of Science
• Machine Learning
• Artificial Intelligence
• Multi-Criteria Decision-Making

Yapay Zekâ Destekli Çok Kriterli Karar Verme Metodolojisi: Araştırma Eğilimlerinden Gelecek Yol Haritasına

Abstract

Bibliyometrik analiz, son yıllarda araştırma alanlarında ilginç eğilimleri, ilişki desenlerini ve bilgi akışını görselleştirerek literatür ve araştırmacılar için değerli bilgiler sağlayan popüler bir yöntemdir. Bu çalışma, Çok Kriterli Karar Verme (ÇKKV) ve Yapay Zekâ (AI) entegrasyonunun bibliyometrik analiz yöntemleriyle incelenerek, bu alanın yayın eğilimlerini, yazar katkılarını, kurumsal işbirliklerini ve atıf dinamiklerini değerlendirmeyi amaçlamaktadır. Bu entegrasyon, karmaşık karar verme süreçlerini optimize ederek ve daha hızlı, tutarlı ve etkili çözümler sunmaktadır. Analiz, performans analizi ve bilim haritalama teknikleri kullanılarak yapılmıştır. Veriler, WoS veritabanından toplanmış ve 1992-2024 yıllarını kapsayan 993 makale analiz edilmiştir. Ortak atıf, eş birliktelik ve ortak yazar analizleri VOSviewer yazılımı ile görselleştirilmiştir. Buna göre, Hindistan, Çin ve İran en fazla yayına sahip ülkeler olarak öne çıkarken, Indian Institute of Technology en yüksek katkıyı sağlamaktadır. 'Annals of Operations Research' ve 'Expert Systems with Applications' en sık atıf yapılan dergiler arasında yer almıştır. University of Technology Sydney ve King Abdulaziz University, kurumsal işbirliği alanında öne çıkmıştır. Bu çalışma, özellikle başlık vurgusu ve elde edilen bazı bulgular açısından, AI-ÇKKV yöntemleri için bibliyometrik analiz yapan öncü bir çalışmadır ve değerli bilgiler sunmaktadır.

Keywords

• Bibliyometrik analiz
• Web of Science
• Makine Öğrenimi
• Yapay Zekâ
• Çok Kriterli Karar Verme

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