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Optimization of Logistics Cargo Tracking and Transportation Efficiency based on Data Science Deep Learning Models

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A peer-reviewed article of this preprint also exists.

Ang Li^*,Shikai Zhuang,Tianyi Yang,Wenran Lu,Jiahao Xu

This version is not peer-reviewed

Submitted:

17 July 2024

Posted:

17 July 2024

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Abstract

With the digital transformation of the logistics industry, smart logistics algorithms have become a core technology to improve efficiency and reduce costs. This paper reviews the development history of traditional logistics technology and discusses the key role of technologies such as the Internet of Things, big data analysis, artificial intelligence, and automation in logistics technology innovation. It focuses on the application of intelligent logistics algorithms in path optimization, intelligent scheduling, data mining and prediction, and intelligent warehousing. To solve the problem of inconsistency between training and testing objectives, this paper proposes DRL4Route, a deep reinforcement learning-based path optimization framework, and designs the DRL4Route-GAE model. Validated through extensive offline experiments and online deployments, the model significantly outperforms existing optimal benchmark methods on real datasets, improving the location deviation squared metrics and the top-three location prediction accuracy metrics. These research results provide important support to further promote the intelligent development of the logistics industry.

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Subject: Computer Science and Mathematics - Data Structures, Algorithms and Complexity

Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.