Efficient Adaptive Matching for Real-Time City Express Delivery

Yafei Li, Qingshun Wu, Xin Huang, Jianliang Xu, Wanru Gao, Mingliang Xu*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

2 Citations (Scopus)


City express delivery services (a.k.a. last-mile delivery) have become more prominent in recent years. Many logistics giants, such as Amazon, JD, and Cainiao, have deployed intelligent express delivery systems to deal with the growing demand for parcel delivery. Existing works adopt queuing or batch processing approaches to assign parcels to couriers. However, these approaches do not fully consider the distribution of parcels and couriers, leading to poor quality of task assignment. In this paper, we investigate a problem of delivery matching based on revenue maximization in real-time city express delivery services. Given a set of couriers and a stream of parcel collection tasks, our problem aims to assign each collection task to a suitable courier to maximize the overall revenue of the platform. The problem is shown to be NP-hard. To tackle the problem efficiently, we present a time-aware batch matching algorithm to offer high-quality courier-task matching in each sliding window. We further theoretically analyze the matching approximation bound. In addition, we propose an efficient deep reinforcement learning based approach to adaptively determine the sliding window size for better matching results. Finally, extensive experiments demonstrate that our proposed algorithms can achieve desirable effectiveness and efficiency under a wide range of parameter settings.

Original languageEnglish
Pages (from-to)5767-5779
Number of pages13
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number6
Early online date24 Mar 2022
Publication statusPublished - 1 Jun 2023

Scopus Subject Areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

User-Defined Keywords

  • adaptive matching
  • City express delivery
  • location-based service
  • optimization
  • real-time system


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