Reinforcement Learning-Driven Intelligent Truck Dispatching Algorithms for Freeway Logistics
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 f...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2024-12-01
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| Series: | Complex System Modeling and Simulation |
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| Online Access: | https://www.sciopen.com/article/10.23919/CSMS.2024.0016 |
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| author | Xiao Jing Xin Pei Pengpeng Xu Yun Yue Chunyang Han |
| author_facet | Xiao Jing Xin Pei Pengpeng Xu Yun Yue Chunyang Han |
| author_sort | Xiao Jing |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-0e3c30ecefb849f4adb25b684d984a2b |
| institution | Kabale University |
| issn | 2096-9929 2097-3705 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Complex System Modeling and Simulation |
| spelling | doaj-art-0e3c30ecefb849f4adb25b684d984a2b2025-01-10T06:47:38ZengTsinghua University PressComplex System Modeling and Simulation2096-99292097-37052024-12-014436838610.23919/CSMS.2024.0016Reinforcement Learning-Driven Intelligent Truck Dispatching Algorithms for Freeway LogisticsXiao Jing0Xin Pei1Pengpeng Xu2Yun Yue3Chunyang Han4China Telecom Research Institute, Beijing 102209, ChinaDepartment of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, ChinaSchool of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510640, ChinaDepartment of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, ChinaFaculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, ChinaFreeway 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.https://www.sciopen.com/article/10.23919/CSMS.2024.0016full-truckloadless-than-truckloadtruck dispatchrepositionreinforcement learning |
| spellingShingle | Xiao Jing Xin Pei Pengpeng Xu Yun Yue Chunyang Han Reinforcement Learning-Driven Intelligent Truck Dispatching Algorithms for Freeway Logistics Complex System Modeling and Simulation full-truckload less-than-truckload truck dispatch reposition reinforcement learning |
| title | Reinforcement Learning-Driven Intelligent Truck Dispatching Algorithms for Freeway Logistics |
| title_full | Reinforcement Learning-Driven Intelligent Truck Dispatching Algorithms for Freeway Logistics |
| title_fullStr | Reinforcement Learning-Driven Intelligent Truck Dispatching Algorithms for Freeway Logistics |
| title_full_unstemmed | Reinforcement Learning-Driven Intelligent Truck Dispatching Algorithms for Freeway Logistics |
| title_short | Reinforcement Learning-Driven Intelligent Truck Dispatching Algorithms for Freeway Logistics |
| title_sort | reinforcement learning driven intelligent truck dispatching algorithms for freeway logistics |
| topic | full-truckload less-than-truckload truck dispatch reposition reinforcement learning |
| url | https://www.sciopen.com/article/10.23919/CSMS.2024.0016 |
| work_keys_str_mv | AT xiaojing reinforcementlearningdrivenintelligenttruckdispatchingalgorithmsforfreewaylogistics AT xinpei reinforcementlearningdrivenintelligenttruckdispatchingalgorithmsforfreewaylogistics AT pengpengxu reinforcementlearningdrivenintelligenttruckdispatchingalgorithmsforfreewaylogistics AT yunyue reinforcementlearningdrivenintelligenttruckdispatchingalgorithmsforfreewaylogistics AT chunyanghan reinforcementlearningdrivenintelligenttruckdispatchingalgorithmsforfreewaylogistics |