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About

Madan Jagtap is a researcher focused on advancing multi-criteria decision-making (MCDM) under uncertainty, with a particular emphasis on **m-polar fuzzy sets** and the **ELECTRE-I algorithm**. His major contributions lie in systematically analyzing how normalization methods and criteria weight calculations affect ranking performance in fuzzy decision environments—work that has direct applications in engineering selection problems, such as choosing non-traditional machining (NTM) processes and assessing robot performance. His most cited paper, “Effect of normalization methods on rank performance in single valued m-polar fuzzy ELECTRE-I algorithm” (2021), has garnered **17 citations**, establishing a foundation for his subsequent studies. Jagtap’s 2023 work integrating ELECTRE-I with the AHP approach for NTM process selection has earned **12 citations**, reflecting its practical relevance. His ongoing refinement of performance score analysis and normalization techniques (2024) demonstrates a sustained commitment to improving the robustness and applicability of fuzzy MCDM tools. Through this focused body of work, Jagtap is helping bridge theoretical fuzzy set innovations with real-world industrial decision-making challenges.