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About

Dilip Kumar Sen is a distinguished researcher specializing in multi-criteria decision making (MCDM), industrial robotics selection, and decision support systems under uncertainty. His work sits at the intersection of operations research and industrial engineering, where he has made substantial contributions to developing and extending sophisticated decision-making frameworks for complex real-world applications. Sen is perhaps best known for his highly cited 2015 paper on multi-criteria decision making for industrial robot selection (71 citations), which addressed the increasingly complex challenge of choosing appropriate robotic systems amid a rapidly expanding marketplace. Building on this foundation, he systematically extended established MCDM methodologies — including PROMETHEE and the Brazilian-origin TODIM framework — to tackle robot selection problems more effectively. His notable adaptations incorporated grey numbers and fuzzy set theory, enabling decision-making under conditions of uncertainty and vague information, reflecting the practical realities engineers face. With a cumulative citation count exceeding 140 across his key publications, Sen's research has meaningfully influenced how academics and industry practitioners approach structured decision-making problems. His 2017 work on decision support systems further broadened his scope, exploring fuzzy and grey set theories across multiple industrial domains. For students and researchers working in robotics selection, intelligent manufacturing, or applied MCDM methodologies, Sen's body of work represents an essential and highly practical reference.