Huiling Wang

Papers

Total Citations

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About

Huiling Wang is a researcher focused on the optimization of logistics and robotic systems, particularly in warehouse automation. Her key research areas include robotic mobile fulfillment systems, task assignment algorithms, and multi-robot coordination. Wang’s major contribution lies in developing efficient pod assignment models for parts-to-picker systems, where robots transport movable shelves to stationary pickers. Her 2019 paper, "The Pod Assignment Model and Algorithm in Robotic Mobile Fulfillment Systems," with 12 citations, introduces a method to optimize product placement based on item correlation, significantly improving system throughput and reducing travel time. This work addresses critical challenges in e-commerce warehousing, offering practical solutions for real-world deployment. Additionally, her 2020 study on "Task Assignment Optimization of Multi-logistics Robot Based on Improved Auction Algorithm" advances decentralized task allocation, enhancing efficiency in multi-robot environments. Though her citation counts are modest, Wang’s research is foundational for emerging automated logistics, directly impacting industry practices. Her work is notable for bridging theoretical optimization with scalable, real-world applications, making her a valuable contributor to the field of intelligent warehouse systems.