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Abstract
Aiming at the problems of unreasonable distribution routes in the current logistics distribution field, without considering the impact of real-time road conditions, and the inability to reduce the impact on the timeliness of distribution, this paper proposes a dynamic vehicle distribution path optimization method based on the collaboration of cloud, edge and end devices. This method considers the requirements of demand points for the delivery time and considers the changes in road traffic conditions caused by random road traffic incidents. Combining the characteristics of vehicle speed and time penalty cost in the vehicle delivery process establishes a logistics delivery vehicle path optimization model. Solve it and optimize it with the A* algorithm and dynamic schedule. This method collects road condition data in real-time through terminal equipment, evaluates and judges road conditions at the edge, and makes real-time adjustments to the distribution plan made in advance at the cloud data center. Through simulation experiments on application examples, the vehicle path optimization method proposed in this paper that considers real-time road conditions changes and the optimization method that does not consider road conditions are compared and analyzed, verifying the effectiveness of this method. Experimental results show that this method can reduce distribution costs, reduce distribution time, and reduce the impact of changes in road conditions on the distribution results.
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Acknowledgment
This work was supported by the National Key Research and Development Project of China (No. 2018YFB1702600, 2018YFB1702602), National Natural Science Foundation of China (No. 61402167, 61772193, 61872139), Hunan Provincial Natural Science Foundation of China (No. 2017JJ4036, 2018JJ2139), and Research Foundation of Hunan Provincial Education Department of China (No. 17K033, 19A174).
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Authors and Affiliations
School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, China
Tiancai Li, Yiping Wen, Zheng Tan, Hong Chen & Buqing Cao
Key Laboratory of Knowledge Processing and Networked Manufacturing, Hunan University of Science and Technology, Xiangtan, China
Tiancai Li, Yiping Wen, Zheng Tan, Hong Chen & Buqing Cao
- Tiancai Li
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- Yiping Wen
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- Zheng Tan
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- Hong Chen
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- Buqing Cao
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Shandong University, Jinan, China
Yuqing Sun
Guangdong University of Technology, Guangzhou, China
Dongning Liu
Shenzhen University, Shenzhen, China
Hao Liao
Tongji University, Shanghai, China
Hongfei Fan
University of Shanghai for Science and Technology, Shanghai, China
Liping Gao
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Li, T., Wen, Y., Tan, Z., Chen, H., Cao, B. (2021). Dynamic Vehicle Distribution Path Optimization Based on Collaboration of Cloud, Edge and End Devices. In: Sun, Y., Liu, D., Liao, H., Fan, H., Gao, L. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2020. Communications in Computer and Information Science, vol 1330. Springer, Singapore. https://doi.org/10.1007/978-981-16-2540-4_5
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