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About

Yunfeng Fan is a leading researcher in warehouse logistics and robotics, specializing in the optimization of robotic mobile fulfillment systems (RMFS). His work focuses on solving complex joint decision-making problems that underpin modern e-commerce and distribution centers, including order and rack assignment, task allocation, and path planning. Fan’s most influential contribution is his 2021 paper on a two-stage hybrid heuristic algorithm for simultaneous order and rack assignment, which has garnered 18 citations and addresses a critical bottleneck in RMFS efficiency. He further advanced the field with a 2020 study on multi-robot task allocation and path planning, earning 16 citations for its novel integration of market-based auction algorithms with an improved A* pathfinding approach. More recently, Fan has explored bi-level optimization for joint rack sequencing and storage assignment (2023, 7 citations) and applied reinforcement learning to pod retrieval as a sequential decision-making problem (2022). His work consistently bridges theoretical optimization with practical system design, making significant strides in enhancing throughput and reducing operational costs in automated warehouses.