Version 1
: Received: 17 July 2024 / Approved: 17 July 2024 / Online: 17 July 2024 (12:47:44 CEST)
How to cite:
Li, A.; Zhuang, S.; Yang, T.; Lu, W.; Xu, J. Optimization of Logistics Cargo Tracking and Transportation Efficiency based on Data Science Deep Learning Models. Preprints2024, 2024071428. https://doi.org/10.20944/preprints202407.1428.v1
Li, A.; Zhuang, S.; Yang, T.; Lu, W.; Xu, J. Optimization of Logistics Cargo Tracking and Transportation Efficiency based on Data Science Deep Learning Models. Preprints 2024, 2024071428. https://doi.org/10.20944/preprints202407.1428.v1
Li, A.; Zhuang, S.; Yang, T.; Lu, W.; Xu, J. Optimization of Logistics Cargo Tracking and Transportation Efficiency based on Data Science Deep Learning Models. Preprints2024, 2024071428. https://doi.org/10.20944/preprints202407.1428.v1
APA Style
Li, A., Zhuang, S., Yang, T., Lu, W., & Xu, J. (2024). Optimization of Logistics Cargo Tracking and Transportation Efficiency based on Data Science Deep Learning Models. Preprints. https://doi.org/10.20944/preprints202407.1428.v1
Chicago/Turabian Style
Li, A., Wenran Lu and Jiahao Xu. 2024 "Optimization of Logistics Cargo Tracking and Transportation Efficiency based on Data Science Deep Learning Models" Preprints. https://doi.org/10.20944/preprints202407.1428.v1
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.
Keywords
Intelligent logistics algorithms; path optimization; deep reinforcement learning; data mining; transport efficiency optimization
Subject
Computer Science and Mathematics, Data Structures, Algorithms and Complexity
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
