Palash Dutta

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About

Palash Dutta is a researcher advancing decision-making under uncertainty, with a focus on fuzzy logic and multi-criteria analysis. His work centers on developing novel mathematical frameworks for complex selection problems, particularly in industrial automation. Dutta’s most cited paper, "Robot Selection Problem via Fuzzy TOPSIS Method Using Novel Distance and Similarity Measure for Generalized Fuzzy Numbers with Unequal Heights" (2021, 5 citations), addresses a critical challenge for multinational companies: optimizing robot selection to reduce labor and save time. He introduces innovative distance and similarity measures for generalized fuzzy numbers, enabling more precise evaluations. His 2024 study, "A novel approach for arithmetic operations and ranking of generalized fuzzy numbers with application" (3 citations), further refines these tools, overcoming traditional methods that yield irrational outputs in complex scenarios. Dutta’s contributions enhance clarity and precision in fuzzy arithmetic, directly impacting industrial decision-making. His work is notable for tackling real-world inefficiencies, offering robust solutions for high-stakes selection processes. With growing citation counts, Dutta’s research is increasingly recognized as foundational for applying fuzzy logic to operational challenges, making him a key voice in the field of applied mathematics and engineering optimization.