Shujun Yang
Papers
2
Total Citations
31
H-Index
2
About
Shujun Yang is a rising force in the intersection of evolutionary computation and logistics automation, whose research is reshaping how intelligent warehouses operate. Her work centers on developing advanced multi-objective optimization algorithms specifically tailored for complex scheduling problems, with a particular focus on multi-robot coordination in modern e-commerce environments. Yang’s most influential contribution is her novel maximin-based multi-objective evolutionary algorithm featuring a one-by-one update scheme, which has garnered 23 citations for its innovative approach to solving multi-robot scheduling optimization challenges. This work directly addresses the growing complexity of warehouse logistics, where traditional methods struggle with increasing order volumes and tighter processing cycles. She further advanced the field with her modified nondominated sorting algorithm for intelligent warehouse robot scheduling systems, which optimizes task assignment for automated guided vehicles (AGVs) and autonomous robots. Her research demonstrates how sophisticated evolutionary algorithms can dramatically improve efficiency in real-world logistics operations, making her work particularly valuable for both academic researchers in optimization theory and industry practitioners seeking practical solutions to modern supply chain challenges.
Research Focus
Key Achievements
Top Papers
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- 2