An adaptive agent-based approach for instant delivery order dispatching: Incorporating task buffering and dynamic batching strategies

Miaojia Lu, Xinyu Yan, Shadi Sharif Azadeh, Pengling Wang*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

The volume of instant delivery has witnessed a significant growth in recent years. Given the involvement of numerous heterogeneous stakeholders, instant delivery operations are inherently characterized by dynamics and uncertainties. This study introduces two order dispatching strategies, namely task buffering and dynamic batching, as potential solutions to address these challenges. The task buffering strategy aims to optimize the assignment timing of orders to couriers, thereby mitigating demand uncertainties. On the other hand, the dynamic batching strategy focuses on alleviating delivery pressure by assigning orders to couriers based on their residual capacity and extra delivery distances. To model the instant delivery problem and evaluate the performances of order dispatching strategies, Adaptive Agent-Based Order Dispatching (ABOD) approach is developed, which combines agent-based modelling, deep reinforcement learning, and the Kuhn-Munkres algorithm. The ABOD effectively captures the system's uncertainties and heterogeneity, facilitating stakeholders learning in novel scenarios and enabling adaptive task buffering and dynamic batching decision-makings. The efficacy of the ABOD approach is verified through both synthetic and real-world case studies. Experimental results demonstrate that implementing the ABOD approach can lead to a significant increase in customer satisfaction, up to 275.42%, while simultaneously reducing the delivery distance by 11.38% compared to baseline policies. Additionally, the ABOD approach exhibits the ability to adaptively adjust buffering times to maintain high levels of customer satisfaction across various demand scenarios. As a result, this approach offers valuable support to logistics providers in making informed decisions regarding order dispatching in instant delivery operations.

Original languageEnglish
Pages (from-to)137-154
Number of pages18
JournalInternational Journal of Transportation Science and Technology
Volume13
DOIs
Publication statusPublished - 2024

Keywords

  • Agent-based modelling
  • Deep reinforcement learning
  • Dynamic batching
  • Instant delivery
  • Task buffering

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