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

Öz

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.

Anahtar Kelimeler

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

Kaynakça

1. Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism. 2017; 69, 36-S40.
2. Zakeri S, Konstantas D, Sorooshian S, Chatterjee P. A novel ML-MCDM-based decision support system for evaluating autonomous vehicle integration scenarios in Geneva’s public transportation. Artificial intelligence review. 2024; 57(11), 1-64.
3. Stević Ž, Ersoy N, Başar EE, Baydaş M. Addressing the global logistics performance index rankings with methodological insights and an innovative decision support framework. Applied sciences. 2024; 14(22), 1-21.
4. Baydaş M, Elma OE, Stević Ž. Proposal of an innovative MCDA evaluation methodology: knowledge discovery through rank reversal, standard deviation, and relationship with stock return. Financial Innovation. 2024; 10(1), 1-35.
5. Odoi-Yorke F, Kabiru SA, Sanful RE, Otoo GS, Lamptey FP, Abbey AA, et al. A review of recent trends, advancements, and future directions in near-infrared spectroscopy applications in biofuel production and analysis. Infrared physics & technology. 2024; 1-41.
6. Lăpădat C, Bădîrcea RM, Manta AG, Georgescu AI. Bibliometric analysis of the common agricultural policy: intersections of agriculture, economy and environment. Finante-provocarile viitorului (Finance-Challenges of the Future). 2024; 1(26), 46-65.
7. Matta-Pacheco J, Tsukamoto-Jaramillo A, Tinedo-Lôpez PL, Espinoza-Carhuancho F, Pacheco-Mendoza J, Mayta-Tovalino F. Bibliometric study of periodontitis and alzheimer\'s disease: trends, collaboration, and emerging patterns. The journal of contemporary dental practice. 2024; 25(9), 863-868.
8. Toker Z, Aksoy E. A bibliometric review of studies on mathematics teacher professional development with an emphasis on mathematics coaching research. Journal of mathematics teacher education. 2024; 1-75.

9. Roucham B, Lefilef A, Zaghdoud O, Mohammed KS. The evolution of green hydrogen in renewable energy research: Insights from a bibliometric perspective. Energy reports. 2025; 13, 576-593.
10. Şengöz A, Orhun BN, Konyalilar N. A holistic approach to artificial intelligence-related research in the transportation system: bibliometric analysis. Worldwide hospitality and tourism themes. 2024; 16(2), 138-149.
11. Farooq R. A review of knowledge management research in the past three decades: a bibliometric analysis. VINE journal of ınformation and knowledge management systems. 2024; 54(2), 339-378.
12. Donthu N, Kumar S, Mukherjee D, Pandey N, Lim WM. How to conduct a bibliometric analysis: An overview and guidelines. Journal of business research. 2021; 133, 285-296.
13. Small, H. Visualizing science by citation mapping. Journal of the american society for information science. 1999; 50(9), 799–813.
14. Van Eck NJ, Waltman L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics. 2010; 84 (2), 523–538.
15. Persson O, Danell R, Schneider JW. How to use Bibexcel for various types of bibliometric analysis. Celebrating scholarly communication studies: a festschrift for olle persson at his 60th birthday. 2009; 5, 9–24.
16. Van Eck NJ, Waltman L, CitNetExplorer: a new software tool for analyzing and visualizing citation networks. Journal of informetrics. 2014; 8(4), 802–823.
17. Cobo MJ, López‐Herrera AG, Herrera‐Viedma E, Herrera F. SciMAT: A new science mapping analysis software tool. Journal of the american society for information science and technology. 2012; 63(8), 1609-1630.
18. Chen C. CiteSpace II: detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the american society for information science and technology. 2006; 57 (3), 359–377.
19. Sahoo SK, Choudhury BB, Dhal PR. A bibliometric analysis of material selection using MCDM methods: trends and insights. Spectrum of mechanical engineering and operational research. 2024; 1(1), 189-205.
20. Medina-Mijangos R, Seguí-Amórtegui L. Research trends in the economic analysis of municipal solid waste management systems: A bibliometric analysis from 1980 to 2019. Sustainability. 2020;12(20), 1-20.
21. Judge AT, Cable DM, Colbert AE, Rynes SL. What causes a management article to be cited—Article, author, or journal?. Academy of Management Journal. 2007; 50(3): 491-506.
22. Angehrn AA, Dutta S. Integrating case-based reasoning in multi-criteria decision support systems. Proceedings of the IFIP Tc8/wg8.3 Working Conference on Decision Support Systems: Experiences and Expectations, Fontainebleau. North-Holland; 1992. p. 133-150.
23. Malakooti B, Zhou YQ. Feedforward artificial neural networks for solving discrete multiple criteria decision making problems. Management Science. 1994; 40(11), 1542-1561.
24. Blank J, Deb K. Pymoo: Multi-objective optimization in python. Ieee Access. 2020; 8, 89497-89509.
25. Khosravi K, Shahabi H, Pham BT, Adamowski J, Shirzadi A, Pradhan B, et al. A comparative assessment of flood susceptibility modeling using multi-criteria decision-making analysis and machine learning methods. Journal of hydrology, 2019; 573, 311-323.
26. Khosravi K. et al. A GIS-based flood susceptibility assessment and its mapping in Iran: a comparison between frequency ratio and weights-of-evidence bivariate statistical models with multi-criteria decision-making technique. Natural hazards, 2016; 83: 947-987.
27. Costache R. et al. Flash-flood susceptibility assessment using multi-criteria decision making and machine learning supported by remote sensing and GIS techniques. Remote Sensing, 2019; 12(1), 106.
28. Ali SA., et al. GIS-based landslide susceptibility modeling: A comparison between fuzzy multi-criteria and machine learning algorithms. Geoscience frontiers, 2021; 12(2): 857-876.
29. Mohammed MA., et al. Benchmarking methodology for selection of optimal COVID-19 diagnostic model based on entropy and TOPSIS methods. Ieee access, 2020; 8: 99115-99131.
30. Belhadi A, et al. Building supply-chain resilience: an artificial intelligence-based technique and decision-making framework. International journal of production research, 2022; 60(14): 4487-4507.
31. Ali R, Lee S, Chung TC. Accurate multi-criteria decision making methodology for recommending machine learning algorithm. Expert systems with applications, 2017; 71: 257-278.
32. Hong H, et al. Landslide susceptibility assessment at the Wuning area, China: A comparison between multi-criteria decision making, bivariate statistical and machine learning methods. Natural hazards, 2019; 96: 173-212.
33. Pham QB, et al. A comparison among fuzzy multi-criteria decision making, bivariate, multivariate and machine learning models in landslide susceptibility mapping. Geomatics, Natural hazards and risk, 2021; 12.1: 1741-1777.
34. Small H. Co-citation in the scientific literature: A new measure of the relationship between two documents. J. Am. Soc. Inf. Sci. 1973; 24, 265–269.
35. Chen P, Chu Z, Zhao M. The Road to corporate sustainability: The importance of artificial intelligence. Technology in Society. 2024; 76(2024), 1-9.
36. Wang J, Du H, Niyato D, Kang J, Cui S, Shen XS, et al. Generative AI for integrated sensing and communication: Insights from the physical layer perspective. IEEE Wireless Communications. 2024; 246-255.
37. König H, Frank D, Baumann M, Heil R. AI models and the future of genomic research and medicine: True sons of knowledge? Artificial intelligence needs to be integrated with causal conceptions in biomedicine to harness its societal benefits for the field. BioEssays. 2021; 43(10), 1-12.
38. Bibri SE, Alexandre A, Sharifi A, Krogstie J. Environmentally sustainable smart cities and their converging AI, IoT, and big data technologies and solutions: an integrated approach to an extensive literature review. Energy Informatics. 2023; 6(1), 1-39.
39. Taherdoost H, Madanchian M. Multi-criteria decision making (MCDM) methods and concepts. Encyclopedia. 2023; 3(1), 77-87.
40. Alshahrani R, Yenugula M, Algethami H, Alharbi F, Goswami SS, Naveed QN, Lasisi A, et al. Establishing the fuzzy integrated hybrid MCDM framework to identify the key barriers to implementing artificial intelligence-enabled sustainable cloud system in an IT industry. Expert systems with applications. 2024; 238(2024): 1-28.
41. Nguyen TMH, Nguyen VP, Nguyen DT. A new hybrid pythagorean fuzzy AHP and CoCoSo MCDM based approach by adopting artificial intelligence technologies. Journal of experimental & theoretical artificial intelligence. 2024; 36(7), 1279-1305.
42. Ghoushchi SJ, Haghshenas SS, Vahabzadeh S, Guido G, Geem ZW. An integrated MCDM approach for enhancing efficiency in connected autonomous vehicles through augmented intelligence and IoT integration. Results in Engineering. 2024; 23, 1-21.
43. Hsueh SL, Feng Y, Sun Y, Jia R, Yan MR. Using AI-MCDM model to boost sustainable energy system development: A case study on solar energy and rainwater collection in guangdong province. Sustainability. 2021; 13(22), 1-25.
44. Arabameri A, Lee S, Tiefenbacher JP, Ngo PTT. Novel ensemble of MCDM-artificial intelligence techniques for groundwater-potential mapping in arid and semi-arid regions (Iran). Remote Sensin. 2020; 12(3), 1-27.
45. Düzen MA, Bölükbaşı İB, Çalık E. How to combine ML and MCDM techniques: an extended bibliometric analysis. Journal of innovative engineering and natural science. 2024; 4(2), 642-657.