DOI
https://doi.org/10.47689/2181-1415-vol5-iss6-pp181-192Keywords
smart logistics algorithms , route optimization , advanced reinforcement learning , data analysis , enhancement of transportation efficiencyAbstract
Amid the digital transformation of the logistics industry, smart logistics algorithms have emerged as a crucial technology to enhance efficiency and reduce costs. This paper reviews the evolution of traditional logistics technologies and highlights the pivotal roles played by advancements such as the Internet of Things, big data analytics, artificial intelligence, and automation in driving logistics innovation. It delves into the application of intelligent logistics algorithms across areas like path optimization, intelligent scheduling, data mining and prediction, and smart warehousing. To address the challenge of inconsistencies between training and testing objectives, the paper introduces DRL4Route, a deep reinforcement learning-based framework for path optimization, along with the DRL4Route-GAE model. Extensive offline experiments and online deployments validate that the model significantly outperforms existing optimal benchmark methods on real datasets, improving metrics like location deviation squared and top-three location prediction accuracy. These research findings provide essential support for advancing the intelligent development of the logistics industry.
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Copyright (c) 2024 Азам Нуруллаев (Автор)
This work is licensed under a Creative Commons Attribution 4.0 International License.
