Subir K. Sanyal

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About

Subir K. Sanyal is a researcher specializing in intelligent decision-making systems, robotics, and advanced manufacturing automation. His work sits at the intersection of fuzzy logic, multi-criteria decision making (MCDM), and industrial robotics, with a particular focus on developing systematic frameworks to address complex selection and optimization challenges in manufacturing environments. Sanyal's most significant contribution lies in his pioneering application of Fuzzy Multi-Criteria Decision Making (FMCDM) methodologies to robot selection problems. His 2014 study on automated foundry operations — his most cited work with 33 citations — demonstrated the power of integrating Fuzzy Analytical Hierarchy Process (FAHP) with complementary ranking techniques, providing practitioners with robust tools for evaluating robots across both tangible and intangible criteria. This was further extended in his 2015 work, where he refined TOPSIS-based fuzzy approaches to improve selection precision for broader robotic systems. His earlier 2012 paper introduced a novel multiplicative model for robot selection, reflecting his longstanding commitment to addressing the growing complexity of industrial decision-making as manufacturing technologies rapidly evolve. Collectively, Sanyal's research offers valuable methodological contributions that help engineers and decision-makers navigate increasingly sophisticated automation landscapes with greater confidence and analytical rigor.