Papers
6
Total Citations
272
H-Index
5
About
Ting-Yu Lin is a leading researcher in intelligent manufacturing and automation, specializing in the application of reinforcement learning (RL) and optimization algorithms to complex scheduling and robotics problems. Her major contributions lie in transforming traditional production lines into adaptive, intelligent systems. She pioneered the use of multi-agent RL for job scheduling in resource-preemption environments (107 citations) and developed deep RL frameworks for discrete automated production lines (106 citations), significantly improving system flexibility and efficiency. Lin also advanced semiconductor manufacturing with noncyclic scheduling strategies for multi-cluster tools using Pareto optimization, addressing critical residency constraints. Her work extends to real-world applications, including training robots for takeout service automation during the COVID-19 pandemic (16 citations) and developing collision-free motion algorithms for automated sensor deployment in environmental monitoring (15 citations). Additionally, she proposed a cloud manufacturing framework for industrial robot training using deep RL (5 citations). With over 270 total citations, Lin’s research bridges theoretical advances in RL and optimization with practical, high-impact solutions for modern manufacturing and service robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5
- 6