Guang Jin

Tsinghua University, University Town of Shenzhen

Papers

Total Citations

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About

Guang Jin’s research centers on the design and optimization of robotic mobile fulfilment systems (RMFS), particularly for e-commerce warehouses where speed and scalability are critical. His major contributions lie in advancing the theoretical and practical understanding of multi-deep compact layouts—configurations that dramatically improve space utilization over traditional single-deep designs. In his most-cited work (2021, 35 citations), Jin models and analyzes these systems to enhance efficiency under rigid order completion times, demonstrating how RMFS can reduce labor dependency while maintaining punctual service. His earlier 2020 study (5 citations) explicitly tackles the challenge of land scarcity by proposing multiple deep layouts, a shift that addresses rising warehouse costs and demand for higher density storage. By integrating robot route planning, throughput analysis, and layout optimization, Jin’s work provides a foundation for next-generation warehouse automation. His research is particularly notable for bridging the gap between theoretical modeling and practical industrial constraints, offering actionable insights for logistics engineers and operations researchers. For students and researchers, Jin’s work exemplifies how thoughtful system design can solve real-world scalability and efficiency problems in modern supply chains.