Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
Practice Summary: Cainiao Enhances the Parcel Sorting Efficiency Through AI-Generated Delivery Zone Codes. |
| Συγγραφείς: |
Yuan, Biao1 (AUTHOR) biaoyuan.ie@sjtu.edu.cn, Zheng, Xusheng2 (AUTHOR) zhengxusheng.xs@alibaba-inc.com, Cui, Weiwei3 (AUTHOR) cuiww67@shu.edu.cn, Han, Youwei2 (AUTHOR) hanyouwei.hyw@alibaba-inc.com |
| Πηγή: |
INFORMS Journal on Applied Analytics. Mar/Apr2026, Vol. 56 Issue 2, p193-197. 5p. |
| Θεματικοί όροι: |
*Transformer models, *Logistics, *Labor costs, *Postal service, *Artificial intelligence, Classification algorithms |
| Περίληψη: |
Cainiao leverages AI to enhance parcel sorting efficiency. Each package must undergo several sorting operations before reaching its destination. To optimize the sorting process, Chinese express logistics companies use a three-level delivery zone code scheme that identifies the destination sorting center, logistics outlet, and local courier for packages. In this work, we approach code generation as a multiclass classification problem in the field of machine learning. We present a lightweight transformer-based architecture developed by Cainiao that is capable of predicting delivery zone codes with high accuracy, achieving 98%–99%. Integrated into logistics management systems, this approach processes tens of millions of parcels daily for major logistics providers and reduces labor costs by 3%–5% while improving sorting efficiency. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: |
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