Papers
7
Total Citations
63
H-Index
5
About
Xiang Shi is a leading researcher in warehouse logistics optimization, with a focus on robotic mobile fulfillment systems (RMFS) and multi-robot coordination. Their major contributions center on developing novel algorithms for order picking efficiency, particularly through simultaneous assignment of orders and racks to picking stations—a critical bottleneck in e-commerce automation. Shi’s most cited work, “A Two-Stage Hybrid Heuristic Algorithm for Simultaneous Order and Rack Assignment Problems” (2021, 18 citations), introduced a joint optimization approach that significantly improves throughput in RMFS environments. Their research on multi-robot task allocation and path planning (2020, 16 citations) combines market-based auction algorithms with enhanced A* pathfinding, enabling efficient coordination in warehouse logistics. More recently, Shi’s bi-level optimization framework for joint rack sequencing and storage assignment (2023, 7 citations) addresses large-scale operational challenges. Their work spans from humanoid robot motion control (2012) to sequential decision-making for pod retrieval (2022), demonstrating versatility across robotics domains. With over 60 total citations across seven publications, Shi’s research directly impacts the design of automated warehouses for companies like Amazon and Alibaba, offering practical solutions for reducing labor costs and improving order fulfillment speed in the rapidly growing e-commerce sector.
Research Focus
Key Achievements
Top Papers
- 1
- 2Multi-robot Task Allocation and Path Planning System Design16 citations · 2020
- 3
- 4Motion control system analysis and design for a humanoid robot8 citations · 2012
- 5
- 6Learning to Solve Pod Retrieval as Sequential Decision Making Problem2 citations · 2022
- 7