Kexin Tang
Papers
2
Total Citations
23
H-Index
2
About
Kexin Tang is a leading researcher in the field of automated logistics and multi-agent systems, with a primary focus on the operational efficiency of automated container terminals. Their work centers on developing intelligent control strategies for Automated Rail-Mounted Gantry cranes (ARTs) to solve critical congestion and coordination challenges in port environments. Tang’s most impactful contribution is a multi-agent reinforcement learning approach for ART adaptive control, published in 2024 and already garnering 17 citations for its novel application of AI to real-time traffic management. This work builds on their earlier 2022 study, which proposed a distributed consistent cooperative control scheme, treating the terminal as a multi-agent system (MAS) to dynamically coordinate vehicle speeds and alleviate quayside bottlenecks. By integrating reinforcement learning with cooperative control theory, Tang has advanced the practical deployment of autonomous vehicles in high-density port operations. Their research is essential reading for engineers and scholars working on smart ports, autonomous vehicle coordination, and the intersection of reinforcement learning with industrial logistics.
Research Focus
Key Achievements
Top Papers
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