Papers
1
Total Citations
56
H-Index
1
About
Xuefang Han is a leading researcher in industrial engineering and logistics, specializing in the optimization of automated warehouse systems through artificial intelligence. Her work focuses on developing intelligent solutions for complex operational challenges, particularly in parts-to-picker warehouse environments. Han's most notable contribution is her pioneering application of reinforcement learning-based hyper-heuristics to coordinate automated guided vehicles (AGVs), addressing the dual challenge of task assignment and route planning. Her 2024 paper on this topic has already garnered 56 citations, reflecting its immediate impact on both academia and industry. This work offers a scalable, adaptive framework that significantly improves warehouse efficiency by dynamically balancing workloads and minimizing travel times. Han's research bridges the gap between theoretical AI and practical logistics, providing actionable insights for next-generation smart warehouses. Her achievements demonstrate a commitment to advancing automation in supply chain management, making her a key figure in the evolution of intelligent material handling systems.
Research Focus
Key Achievements
Top Papers
- 1