Papers
1
Total Citations
30
H-Index
1
About
Ye Shi is a researcher whose work sits at the dynamic intersection of operations research, artificial intelligence, and smart manufacturing systems. His scholarship is particularly distinguished by its grounding in real-world industrial challenges, most notably demonstrated through his highly cited 2021 study on IoT-enabled human–robot hybrid sortation systems, which drew direct inspiration from China Post's operational practices. This work, accumulating 30 citations, introduced an innovative online optimization approach that leverages Internet of Things technology to dynamically balance manual and robotic capacity in logistics environments — a contribution with immediate practical relevance to the rapidly evolving landscape of intelligent supply chain management. Shi's research reflects a broader commitment to bridging theoretical optimization methodologies with applied systems, particularly in contexts where human and automated agents collaborate under real-time constraints. By addressing how IoT data streams can drive responsive, adaptive decision-making in hybrid labor environments, he has made meaningful contributions to the fields of intelligent logistics and human-robot collaboration. His work appeals to scholars and practitioners alike who are navigating the challenges of automation integration in modern industrial and postal operations.
Research Focus
Key Achievements
Top Papers
- 1