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Open Access

Reinforcement Learning-Based Dynamic Order Recommendation for On-Demand Food Delivery

Department of Automation, Tsinghua University, Beijing 100080, China
School of Mechanical and Automotive Engineering, Qingdao Hengxing University of Science and Technology, Qingdao 266100, China
Meituan, Beijing 100015, China
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Abstract

On-demand food delivery (OFD) is gaining more and more popularity in modern society. As a kernel order assignment manner in OFD scenario, order recommendation directly influences the delivery efficiency of the platform and the delivery experience of riders. This paper addresses the dynamism of the order recommendation problem and proposes a reinforcement learning solution method. An actor-critic network based on long short term memory (LSTM) unit is designed to deal with the order-grabbing conflict between different riders. Besides, three rider sequencing rules are accordingly proposed to match different time steps of the LSTM unit with different riders. To test the performance of the proposed method, extensive experiments are conducted based on real data from Meituan delivery platform. The results demonstrate that the proposed reinforcement learning based order recommendation method can significantly increase the number of grabbed orders and reduce the number of order-grabbing conflicts, resulting in better delivery efficiency and experience for the platform and riders.

Keywords

on-demand food deliveryorder recommendationreinforcement learningactor-critic networklong short term memory

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Tsinghua Science and Technology
Volume 29 Issue 2,
April 2024
Pages 356-367
DOI: 10.26599/TST.2023.9010041
Cite this article:
Wang X, Wang L, Dong C, et al. Reinforcement Learning-Based Dynamic Order Recommendation for On-Demand Food Delivery. Tsinghua Science and Technology, 2024, 29(2): 356-367. https://doi.org/10.26599/TST.2023.9010041

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Received: 19 April 2023
Revised: 05 May 2023
Accepted: 09 May 2023
Published: 22 September 2023
© The author(s) 2024.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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