Rakesh Garg
Papers
1
Total Citations
11
H-Index
1
About
Dr. Rakesh Garg is a distinguished researcher in decision science and industrial engineering, with a primary focus on multi-criteria decision-making (MCDM) and fuzzy logic applications. His major contributions lie in developing innovative hybrid frameworks for complex evaluation and ranking problems, particularly in manufacturing and robotics. His most cited work introduces a novel "fuzzy modified distance based approach (FMDBA)" for the selection and ranking of industrial robots—a methodology previously unexplored in open literature. This approach systematically integrates fuzzy set theory with distance-based ranking to handle uncertainty and subjective judgment in criteria weighting, offering a robust decision support system for engineers and managers. With 11 citations on this seminal paper alone, Dr. Garg's research has provided practical tools for optimizing technology selection in automated environments. His work stands out for bridging theoretical MCDM advancements with real-world industrial applications, enabling more transparent and reliable evaluations. By addressing the critical challenge of robot selection under multiple conflicting criteria, Dr. Garg has significantly influenced both academic research and industrial practice, making him a key figure in the evolution of intelligent decision support systems.
Research Focus
Key Achievements
Top Papers
- 1