Papers
4
Total Citations
42
H-Index
3
About
Yugang Yu is a leading researcher in intelligent logistics and robotics, with a focus on optimizing human–robot hybrid systems and autonomous intralogistics. His work bridges operations research, Internet of Things (IoT) analytics, and multi-robot coordination to tackle real-world challenges in parcel sortation and warehouse automation. Yu’s most cited paper, “Analytics for IoT‐Enabled Human–Robot Hybrid Sortation: An Online Optimization Approach” (2021, 30 citations), introduces a dynamic capacity adjustment framework for China Post, demonstrating how IoT data can enhance efficiency in mixed human–robot environments. He further advances robotic sorting systems through studies on dynamic robot routing and destination assignment (2025, 7 citations), offering policies that improve flexibility and accuracy under demand fluctuations. His recent work on proactive multi-robot path planning (2025, 3 citations) uses Monte Carlo congestion prediction to prevent collisions and deadlocks in uncertain intralogistics settings. Yu’s contributions are notable for their practical impact—directly informing industry implementations—and for advancing theoretical models that balance real-time adaptability with system throughput. His research is essential reading for students and practitioners in supply chain engineering, robotics, and IoT-enabled automation.
Research Focus
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