Optimization of O2O orders delivery path based on deep reinforcement learning

Xiefei Chen; Guangyu Zou

doi:10.1117/12.3054758

20 December 2024 Optimization of O2O orders delivery path based on deep reinforcement learning

Xiefei Chen, Guangyu Zou

Author Affiliations +

Proceedings Volume 13421, Eighth International Conference on Traffic Engineering and Transportation System (ICTETS 2024); 134215R (2024) https://doi.org/10.1117/12.3054758
Event: Eighth International Conference on Traffic Engineering and Transportation System (ICTETS 2024), 2024, Dalian, China

Abstract

During the COVID-19 pandemic, the Online to Offline (O2O) business sector plays a crucial role in safeguarding people's livelihoods. Millions of transactions occur daily on online logistics ordering platforms. The allocation of orders and the path planning of delivery vehicles present significant challenges to the O2O platforms. This paper presents a collaborative optimization deep reinforcement learning framework (CODRL) based on an encoder-decoder architecture. A node pairing embedding is designed in the encoder to capture the structural dependency relationship between merchant nodes and customer nodes in the same order. Subsequently, the vehicle decoder is utilized to select a delivery vehicle, and the node decoder is used to specify a node that requires service by the delivery vehicle. The preliminary experimental results, utilizing randomly generated order data, demonstrate that the proposed CODRL outperforms all other baselines by at least 5.67% across all settings. Furthermore, the performance enhancement of the node pairing embedding, designed in the encoder, is evaluated through ablation study.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xiefei Chen and Guangyu Zou "Optimization of O2O orders delivery path based on deep reinforcement learning", Proc. SPIE 13421, Eighth International Conference on Traffic Engineering and Transportation System (ICTETS 2024), 134215R (20 December 2024); https://doi.org/10.1117/12.3054758

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