Jinkun Dai
Papers
2
Total Citations
7
H-Index
2
About
Jinkun Dai is a rising researcher whose work centers on intelligent decision-making, risk assessment, and supply-demand optimization in advanced manufacturing systems. His primary research areas include cloud manufacturing platforms, failure modes and effects analysis (FMEA), and multi-criteria decision-making (MCDM) methods. Dai’s major contributions lie in developing novel evaluation frameworks that integrate sophisticated MCDM techniques—such as ELECTRE III, VIKOR, TODIM, and the Best Worst Method—with fuzzy linguistic environments to address real-world industrial challenges. His 2024 paper on supply-demand matching degree evaluation in cloud manufacturing, which has already garnered 5 citations, proposes an intelligent method to reduce transaction risks for manufacturing entities by improving the accuracy of matching assessments. Additionally, his work on FMEA optimization for industrial robots introduces a Pythagorean fuzzy language approach combined with water-filling theory to enhance reliability assessment, overcoming limitations of traditional risk priority number methods. Though early in his career, Dai’s innovative integration of fuzzy logic and MCDM tools is paving the way for more robust, data-driven decision support in smart manufacturing and industrial automation.
Research Focus
Key Achievements
Top Papers
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