Liying Yu
Papers
2
Total Citations
69
H-Index
2
About
Liying Yu is a distinguished researcher in industrial engineering and decision science, with a primary focus on risk assessment, quality function deployment (QFD), and multi-criteria decision-making (MCDM) under uncertainty. Her work is particularly notable for advancing fuzzy set theory applications in complex industrial systems. Yu’s most cited paper, “The FMEA model based on LOPCOW-ARAS methods with interval-valued Fermatean fuzzy information for risk assessment of R&D projects in industrial robot offline programming systems” (2023, 55 citations), introduces a novel hybrid MCDM framework that enhances failure mode and effects analysis (FMEA) by integrating Fermatean fuzzy logic, addressing critical gaps in handling ambiguous and incomplete data for high-stakes R&D environments. Her earlier contribution, “A novel IVIF QFD considering both the correlations of customer requirements and the ranking uncertainty of technical attributes” (2022, 14 citations), innovatively extends intuitionistic fuzzy QFD to capture interdependent customer needs and technical attribute ranking instability, offering more robust product design solutions. Yu’s work is widely recognized for bridging theoretical fuzzy decision models with practical engineering challenges, particularly in robotics and manufacturing. Her methodologies are increasingly adopted by researchers and practitioners seeking to manage risk and optimize design processes in uncertain, data-scarce contexts, cementing her reputation as a key contributor to modern industrial decision analysis.
Research Focus
Key Achievements
Top Papers
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