Caiyun Yang
Papers
2
Total Citations
23
H-Index
2
About
Caiyun Yang is a leading researcher in the field of intelligent logistics and automation, with a primary focus on the operational optimization of automated container terminals. Her work centers on the application of multi-agent systems and reinforcement learning to solve complex coordination and control problems in these high-throughput environments. Yang’s major contributions include pioneering a multi-agent reinforcement learning approach for adaptive control of Automated Rail-Mounted Gantry (ART) cranes, a method that dynamically manages vehicle congestion and improves quayside efficiency. Her 2024 paper on this topic has already garnered 17 citations, signaling its rapid impact on the field. In earlier foundational work from 2022, she proposed a distributed consistent cooperative control schema for multiple ARTs, treating the terminal as a multi-agent system to address congestion through dynamic speed coordination. This research is critical for advancing the efficiency and autonomy of modern ports. Yang’s work is notable for bridging theoretical control algorithms with practical, real-world logistics challenges, making her a key figure in the next generation of smart port technology.
Research Focus
Key Achievements
Top Papers
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