Reinforcement Learning-Driven Intelligent Truck Dispatching Algorithms for Freeway Logistics  

作　　者：Xiao Jing Xin Pei Pengpeng Xu Yun Yue Chunyang Han 

机构地区：[1]China Telecom Research Institute,Beijing 102209,China [2]Department of Automation,Beijing National Research Center for Information Science and Technology,Tsinghua University,Beijing 100084,China [3]School of Civil Engineering and Transportation,South China University of Technology,Guangzhou 510640,China [4]Faculty of Transportation Engineering,Kunming University of Science and Technology,Kunming 650500,China

出　　处：《Complex System Modeling and Simulation》2024年第4期368-386,共19页复杂系统建模与仿真(英文)

基　　金：supported by the National Key Research and Development Program of China(No.2021YFC3001500);Natural Science Foundation of China(No.52302433);Natural Science Foundation of Guangdong Province,China(No.2023A1515012404);Science and Technology Projects in Guangzhou(No.2024A04J3838);Fundamental Research Funds for the Central Universities(No.2022ZYGXZR052);China Postdoctoral Science Foundation(No.2021M701898).

摘　　要：Freeway logistics plays a pivotal role in economic development.Although the rapid development in big data and artificial inteligence 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 inteligent 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 realworld 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.

关 键 词：full-truckload less-than-truckload truck dispatch REPOSITION reinforcement learning 

分 类 号：F42[经济管理—产业经济]