Practice Summary: Cainiao Enhances the Parcel Sorting Efficiency Through AI-Generated Delivery Zone Codes

Biao Yuan (), Xusheng Zheng (), Weiwei Cui () and Youwei Han ()
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Biao Yuan: Data-Driven Management Decision-Making Lab, Sino-US Global Logistics Institute, Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai 200030, China
Xusheng Zheng: Cainiao Network, Hangzhou 311100, China
Weiwei Cui: School of Management, Shanghai University, Shanghai 200444, China
Youwei Han: Cainiao Network, Hangzhou 311100, China

Interfaces, 2026, vol. 56, issue 2, 193-197

Abstract: 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.

Keywords: logistics; artificial intelligence; natural language processing; multiclass classification; text classification (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:inm:orinte:v:56:y:2026:i:2:p:193-197

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