Dr. Neha Singhal’s research primarily focuses on the evaluation of reliability in industrial systems using both classical and modern analytical techniques. Her work aims to enhance the understanding and quantification of system reliability in complex industrial environments, which is critical for ensuring operational efficiency and safety. She has contributed to the development and application of reliability models that incorporate real-world system constraints, component interactions, and uncertainty.

In addition to traditional reliability theory, her research also explores advancements in metaheuristic techniques, including nature-inspired optimization algorithms. These techniques have proven effective in solving high-dimensional and computationally intensive reliability optimization problems. Her contributions include hybrid models that integrate metaheuristics with soft computing approaches to achieve more accurate and robust solutions.

Another important dimension of her research involves transportation problems under fuzzy environments. Recognizing the presence of ambiguity and imprecise data in real-life logistics and distribution systems, she has worked on the formulation and solution of transportation problems with fuzzy parameters, improving decision-making under uncertainty.

Overall, her research combines mathematical rigor with practical relevance, contributing to the fields of reliability engineering, optimization, and fuzzy decision-making, with applications in manufacturing, logistics, and engineering system design.