Summary of Harvesting Efficient On-demand Order Pooling From Skilled Couriers: Enhancing Graph Representation Learning For Refining Real-time Many-to-one Assignments, by Yile Liang et al.
Harvesting Efficient On-Demand Order Pooling from Skilled Couriers: Enhancing Graph Representation Learning for Refining Real-time Many-to-One Assignments
by Yile Liang, Jiuxia Zhao, Donghui Li, Jie Feng, Chen Zhang, Xuetao Ding, Jinghua Hao, Renqing He
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes an enhanced attributed heterogeneous network embedding approach for on-demand food delivery (OFD) platforms. The goal is to construct high-quality order pooling that harmonizes platform efficiency with consumer and courier experiences. The authors develop a Skilled Courier Delivery Network (SCDN) that extracts features from temporal and spatial data, uncovering latent potential for order combinations embedded in courier trajectories. This approach enables scalable similarity calculations of low-dimensional vectors, making comprehensive and high-quality pooling outcomes more easily identified in real-time. The SCDN is deployed in Meituan’s dispatch system, showing improved pooling quality and extent, as well as a 45-55% boost in couriers’ efficiency during peak hours while maintaining timely delivery commitment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps improve on-demand food delivery (OFD) services by creating a better way to group orders together. This makes the service more efficient and can help reduce wait times for customers. The authors developed a special system that uses data from couriers’ routes to figure out the best way to combine orders. This approach allows for faster and more accurate decisions about which orders to combine, making the delivery process smoother and more reliable. |
Keywords
* Artificial intelligence * Embedding
