Sijia Yi
Papers
2
Total Citations
11
H-Index
2
About
Sijia Yi is a leading researcher in intelligent manufacturing and automation, specializing in the optimization of robotic job shops through advanced scheduling and control methodologies. Her work centers on integrating Petri nets, artificial potential fields, and deep reinforcement learning to address complex, real-time scheduling challenges in environments where mobile robots handle material transport alongside production operations. Yi’s major contributions include the development of heuristic scheduling frameworks that minimize makespan by jointly optimizing process operations and transportation tasks, as demonstrated in her 2024 paper on “Heuristic Scheduling for Robotic Job Shops Using Petri Nets and Artificial Potential Fields,” which has garnered 6 citations. She further advanced the field with her 2025 study on “Deep reinforcement learning driven by heuristics with Petri nets for enhancing real-time scheduling in robotic job shops,” earning 5 citations for its novel integration of learning-based and rule-based approaches. Her research has significant implications for Industry 4.0, offering scalable, adaptive solutions for smart factories. Yi’s work is recognized for bridging theoretical modeling with practical deployment, making her a notable figure in production systems and robotics research.
Research Focus
Key Achievements
Top Papers
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- 2