In the phase of logistics distribution vehicle scheduling, the distribution time and travel distance of vehicles are relatively long due to the influence of the real-time change attribute characteristics of the actual traffic environment state. Therefore, this paper proposes an optimization scheduling algorithm for logistics distribution vehicles based on the Internet of Vehicles platform. The Internet of Vehicles platform including acquisition layer, transmission layer, data layer and application layer is constructed to achieve the acquisition and analysis of real-time information of the actual traffic environment status. In the logistics distribution vehicle scheduling phase, the objective function of comprehensively arranging the number of vehicles, the total distance traveled by the distribution vehicles and the time window constraint punishment is constructed. After the objective function is input into the Internet of Vehicles platform, the greedy algorithm strategy is used to achieve the optimal scheduling of vehicles. In the test results, the design algorithm achieves the goal of shortening the vehicle delivery time and driving distance without considering the results under traffic conditions and the scheduling effect under dynamic conditions. To sum up, the optimization scheduling algorithm of logistics distribution vehicles based on the Vehicle-to-everything platform can help optimize the travel distance and distribution time of vehicles, and improve the efficiency and accuracy of logistics distribution.


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    Title :

    Optimization Scheduling Algorithm of Logistics Distribution Vehicles Based on Internet of Vehicles Platform


    Additional title:

    Lect.Notes Social.Inform.


    Contributors:
    Yun, Lin (editor) / Han, Jiang (editor) / Han, Yu (editor) / Li, Zhuorong (author) / Yao, Yongchang (author) / Zhang, Haibo (author) / Li, Nan (author) / Sun, Jianmei (author)

    Conference:

    International Conference on Advanced Hybrid Information Processing ; 2023 ; Harbin, China September 22, 2023 - September 24, 2023



    Publication date :

    2024-03-24


    Size :

    17 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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