Yizhuo Zhang
Papers
1
Total Citations
2
H-Index
1
About
Dr. Yizhuo Zhang is a rising researcher in industrial manufacturing and intelligent scheduling, whose work focuses on optimizing multi-agent systems to enhance production efficiency. Their most-cited paper, "Optimization of Multi-Agent Scheduling Based on MA-ID3QN for the Riveting and Welding Work Cell" (2025), introduces a novel deep reinforcement learning approach—the Multi-Agent Improved Dueling Double Deep Q-Network (MA-ID3QN)—to tackle the complex task scheduling challenges in riveting and welding work cells. This contribution addresses critical issues such as task interference and system complexity, offering a scalable solution that improves coordination and throughput in automated manufacturing environments. With 2 citations already, this work signals growing recognition in the field. Dr. Zhang’s research bridges the gap between theoretical multi-agent reinforcement learning and practical industrial applications, demonstrating significant potential for real-world impact. Their achievements highlight a promising trajectory in advancing smart manufacturing, making them a notable voice among emerging scholars in production optimization and intelligent automation.
Research Focus
Key Achievements
Top Papers
- 1