Yanjie Song

National University of Defense Technology, ShanghaiTech University

Papers

Total Citations

H-Index

About

Yanjie Song is a leading researcher in smart manufacturing and robotics, with a focus on optimizing production logistics through intelligent automation. Her work addresses critical challenges in modern manufacturing systems, where timely and efficient material supply is essential for uninterrupted production. Song’s most cited paper, “Multi-mobile robots and multi-trips feeding scheduling problem in smart manufacturing system: An improved hybrid genetic algorithm” (2019, 7 citations), introduces a novel approach to coordinating multiple mobile robots for multi-trip feeding tasks, using an enhanced genetic algorithm to minimize delays and improve overall system reliability. This work is foundational for smart factories seeking to automate material handling. In another notable contribution, “BORA: A Bag Optimizer for Robotic Analysis” (2020, 3 citations), Song presents a file system middleware that optimizes the acquisition of ROS bag files—critical for timestamped data storage in robotic systems. By enabling semantic-aware data management, BORA enhances the efficiency of robotic analysis pipelines. Song’s research bridges the gap between theoretical optimization and practical deployment, making her a key figure in advancing autonomous manufacturing and robotic data systems.