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About

Bipradas Bairagi is a researcher specializing in multi-criteria decision making (MCDM), robotics selection, and intelligent manufacturing systems. His work sits at the intersection of fuzzy logic, operations research, and industrial automation, with a particular focus on developing robust methodologies for evaluating and selecting robotic systems in complex manufacturing environments. Bairagi's most influential contribution, "Selection of robot for automated foundry operations using fuzzy multi-criteria decision making approaches" (2014, 33 citations), introduced an integrated Fuzzy Analytical Hierarchy Process (FAHP) framework that brought greater rigor to robot selection in industrial settings. Building on this foundation, his 2022 paper on homogeneous group decision making using extended TOPSIS (22 citations) advanced the field by simultaneously addressing subjective and objective factors in robotic system selection. A notable hallmark of Bairagi's research is his attention to practical limitations in existing MCDM methods. His development of the Technique of Accurate Ranking Order (TARO) directly tackles the persistent problem of rank reversal — a source of ambiguity that undermines confidence in decision-making processes. Across more than a decade of scholarship, Bairagi has consistently pushed toward more reliable, transparent, and comprehensive decision frameworks, accumulating over 75 citations and establishing himself as a meaningful contributor to applied industrial decision science.