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About

Mingyao Qi is a prominent researcher specializing in warehouse automation, robotic mobile fulfillment systems (RMFS), and intelligent logistics optimization. His work sits at the intersection of operations research, robotics, and supply chain management, addressing real-world challenges in e-commerce distribution and automated warehousing. Qi's most influential contributions focus on the design and optimization of RMFS — parts-to-picker systems where robots autonomously transport storage pods to stationary human pickers. His most cited work (35 citations) tackles the complex joint problem of order and rack sequencing across multiple picking stations, while complementary studies examine system performance under time-varying demand and high-density storage conditions. Together, these papers have garnered nearly 100 citations, establishing him as a leading voice in this rapidly evolving field. Beyond RMFS, Qi has explored picker-robot collaboration frameworks for e-commerce distribution centers, non-traditional warehouse layout designs, and cutting-edge multi-tote autonomous mobile robot systems. His 2018 work on picking station location optimization introduced integer programming approaches that laid important groundwork for subsequent research. His early investigation into landmark-based visual positioning for AGVs further demonstrates his broad technical versatility. Qi's body of work offers students and practitioners valuable frameworks for designing and optimizing the next generation of automated fulfillment operations.