Yanchen Deng
Papers
1
Total Citations
3
H-Index
1
About
Yanchen Deng is an emerging researcher at the intersection of operations research, reinforcement learning, and industrial automation. Their most-cited work, "Battery Management for Automated Warehouses via Deep Reinforcement Learning" (2020), introduces a novel framework that leverages deep RL to optimize battery charging and swapping schedules in robotic warehouses—a critical challenge for sustainable logistics. By formulating the problem as a Markov decision process and training a deep Q-network, Deng demonstrates how intelligent agents can reduce operational costs and extend battery lifespan, achieving significant efficiency gains over traditional heuristic policies. Though still early in their career, with this paper accruing 3 citations, the work signals a promising trajectory in applying AI to real-world energy management systems. Deng’s contributions are particularly relevant as industries seek to decarbonize supply chains, and their research offers a scalable blueprint for integrating reinforcement learning into automated material handling. For students and researchers exploring the synergy between machine learning and industrial engineering, Deng’s work provides a clear, applied example of how deep RL can solve complex, resource-constrained problems in dynamic environments.
Research Focus
Key Achievements
Top Papers
- 1