Tabasam Rashid

National University of Computer and Emerging Sciences, University of Management and Technology

Papers

Total Citations

244

H-Index

About

Tabasam Rashid is a prominent researcher whose work spans decision-making methodologies, fuzzy mathematics, and graph theory, with a particular focus on solving complex industrial optimization problems. He is best known for his pioneering contributions to robot selection frameworks using advanced multi-criteria decision-making (MCDM) techniques. His 2014 paper applying generalized interval-valued fuzzy numbers with TOPSIS to robot selection has garnered 98 citations, establishing him as an early innovator in fuzzy-based industrial decision support. Building on this foundation, Rashid developed increasingly sophisticated hybrid methodologies, including the integration of Best-Worst Method (BWM) with EDAS and fuzzy MCGDM frameworks, collectively accumulating over 140 additional citations. These contributions address a critical real-world challenge: selecting appropriate industrial robots from an ever-expanding market of specifications and capabilities. His comparative analyses of hybrid fuzzy methodologies have further refined criteria-weighting reliability in group decision-making contexts. Beyond industrial applications, Rashid has extended his mathematical expertise into graph theory, exploring fractional metric dimensions of generalized prism graphs. His body of work reflects a researcher committed to bridging theoretical mathematical innovation with practical engineering and optimization challenges, making him a valuable reference for students navigating fuzzy decision analysis and intelligent systems research.