Reinforcement Learning-Driven Intelligent Truck Dispatching Algorithms for Freeway Logistics
Year: 2024
Type: article
Abstract: Freeway logistics plays a pivotal role in economic development. Although the rapid development in big data and artificial intelligence motivates long-haul freeway logistics towards informatization and intellectualization, the transportation of bulk commodities still faces serious challenges arisen from dispersed freight demands and the lack of co-ordination among different operators. The present study thereby proposed intelligent algorithms for truck dispatching for freeway logistics. Specifically, our contributions include the establishment of mathematical models for full-truckload (FTL) and less-than-truckload (LTL) transportation modes, respectively, and the introduction of reinforcement learning with deep Q-networks tailored for each transportation mode to improve the decision-making in order acceptance and truck repositioning. Simulation experiments based on the real-world freeway logistics data collected in Guiyang, China show that our algorithms improved operational profitability substantially with a 76% and 30% revenue increase for FTL and LTL modes, respectively, compared with single-stage optimization. These results demonstrate the potential of reinforcement learning in revolutionizing freeway logistics and should lay a foundation for future research in intelligent logistics systems.
Authors: Xiao Jing, Xin Pei, Pengpeng Xu, Yun Yue, Chunyang Han
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
Funders: National Natural Science Foundation of China, China Postdoctoral Science Foundation, Natural Science Foundation of Guangdong Province, Guangzhou Science and Technology Program key projects, National Key Research and Development Program of China, Fundamental Research Funds for the Central Universities
OpenAlex ID: W4406046516