VeLP: Vehicle Loading Plan Learning from Human Behavior in Nationwide Logistics System
Summary: VeLP: a data-driven vehicle loading plan learner that mines dispatcher decision patterns from a five-month nationwide JD Logistics dataset and models regular vs. irregular routes via pattern mining plus a deep temporal cross neural network. Outperforms combinatorial baselines (35.8%/50% gains on trunk/branch routes) and deployed on ~400 routes, reducing plan-creation time by ~20%. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Sijing Duan
- 2. Feng Lyu
- 3. Xin Zhu
- 4. Yi Ding
- 5. Haotian Wang
- 6. Desheng Zhang
- 7. Xue Liu
- 8. Yaoxue Zhang
- 9. Ju Ren
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,520 | A Unified Approach to Route Planning for Shared Mobility | 2018 | VLDB | 8.6069685e-05 |
| 2,716 | Davos: A System for Interactive Data-Driven Decision Making | 2021 | VLDB | 8.2429172e-05 |
| 6,006 | Last-Mile Delivery Made Practical: An Efficient Route Planning Framework with Theoretical Guarantees | 2020 | VLDB | 5.2415551e-05 |
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