Papers
2
Total Citations
125
H-Index
2
About
Dr. Minglun Ren is a prominent researcher in multi-criteria decision-making (MCDM) and information fusion, with a focus on linguistic modeling and evidential reasoning. His most-cited work, "An interval 2-tuple linguistic MCDM method for robot evaluation and selection" (2013, 101 citations), introduced a novel approach to handling uncertainty in industrial robot selection, addressing the growing complexity manufacturers face when choosing optimal robots from an expanding array of models. This method has become a foundational tool in production and operations research. More recently, Dr. Ren advanced sensor fusion techniques with "Decision fusion of two sensors object classification based on the evidential reasoning rule" (2022, 24 citations), demonstrating his continued impact on intelligent systems and data integration. His contributions bridge theoretical frameworks and practical applications, offering robust solutions for decision-making under ambiguity. With over a decade of influential work, Dr. Ren’s research remains essential for engineers and researchers tackling real-world optimization and classification challenges.
Research Focus
Key Achievements
Top Papers
- 1An interval 2-tuple linguistic MCDM method for robot evaluation and selection101 citations · 2013
- 2