Alper Bulut
Papers
1
Total Citations
14
H-Index
1
About
Alper Bulut is a rising scholar in the field of multi-criteria decision-making (MCDM), with a particular focus on advancing fuzzy set theory and its applications. His research centers on developing novel distance and aggregation techniques to address complex ranking challenges under uncertainty. In his most cited work, Bulut introduced the Fermatean vague normal set (FVNS) and pioneered logarithmic operators—such as the log Fermatean vague normal weighted averaging (log FVNWA)—to enhance the handling of vague and imprecise data in multiple attribute decision-making (MADM) problems. This contribution, published in 2023, has already garnered 14 citations, reflecting its timely relevance and utility for researchers tackling real-world decision challenges. Bulut’s work stands out for its methodological innovation, offering more robust tools for ranking alternatives in fields ranging from engineering to economics. As an emerging voice in decision science, his efforts are shaping how vague information is processed and quantified, promising significant impact on both theoretical frameworks and practical applications.
Research Focus
Key Achievements
Top Papers
- 1