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About

Zheyi Tan is a researcher specializing in intelligent warehouse automation, human-robot collaboration, and logistics optimization. His work sits at the intersection of operations research and robotics, with a particular focus on designing and deploying robotic systems that enhance the efficiency of modern e-commerce fulfillment operations. Tan's most influential contributions center on robotic mobile fulfillment systems (RMFS), where he has tackled complex scheduling challenges involving the coordinated management of orders, robots, and storage pods alongside manual workstations. His 2022 paper on human-robot collaborative routing, which has garnered 30 citations, demonstrates his interest in bridging autonomous systems with human operators in dynamic logistics environments. Further work on order picking optimization and RMFS deployment strategies has collectively attracted nearly 50 citations, underscoring the practical relevance of his research to warehouse managers and systems designers. More recently, Tan has turned his attention to autonomous mobile robots (AMRs) in conventional warehouse settings, exploring how these systems can reduce picker walking distances and boost throughput without requiring costly infrastructure overhauls. With a growing citation record and publications spanning 2022 to 2025, Tan is emerging as a productive voice in the rapidly evolving field of smart logistics and warehouse robotics.