Feiyue Wu
Papers
1
Total Citations
2
H-Index
1
About
Feiyue Wu is a rising researcher whose work lies at the intersection of intelligent transportation, multi-objective optimization, and autonomous systems. Their most notable contribution is the development of a hybrid multi-objective heuristic algorithm for automated guided vehicle (AGV) path planning, a critical challenge in modern logistics and manufacturing. This work addresses the growing need for efficient, collision-free navigation as AGVs increasingly replace manual labor across industries. By combining heuristic methods with multi-objective optimization, Wu’s approach balances competing goals such as path length, safety, and computational efficiency—offering a practical solution for real-world deployment. Though early in their career, with their leading paper already garnering 2 citations, the work has been recognized for its timely relevance and methodological rigor. Wu’s research is particularly impactful for students and engineers working on autonomous robotics, smart factories, and supply chain automation. As the demand for intelligent material handling surges, Wu’s contributions provide a foundational framework for safer and more efficient AGV operations, marking them as a promising voice in the field of automated systems and optimization.
Research Focus
Key Achievements
Top Papers
- 1