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

Author

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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)

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.

Suggested Citation

  • Biao Yuan & Xusheng Zheng & Weiwei Cui & Youwei Han, 2026. "Practice Summary: Cainiao Enhances the Parcel Sorting Efficiency Through AI-Generated Delivery Zone Codes," Interfaces, INFORMS, vol. 56(2), pages 193-197, March.
    • Handle: RePEc:inm:orinte:v:56:y:2026:i:2:p:193-197
    DOI: 10.1287/inte.2025.0212
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