Optimisation of freight vehicle routes leveraging cloud computing and edge endpoints

In the current freight industry, how to improve the economic benefits of freight transportation and solve the problem of insufficient information timeliness under cloud computing is a persistent challenge. In response to the problems of delayed information in cloud computing path planning and insufficient economic benefits of planning results, this study proposes a freight vehicle path optimisation model supported by cloud computing and edge end. This model introduces edge computing into cloud platforms and constructs a genetic algorithm-based truck path optimisation algorithm. According to test outcomes, the path planning time of the proposed model was at most 160 ms lower than that of a regular cloud computing environment. In simulation testing, the optimised path planning model saved a total cost of 16.8% compared to the pre optimised output. The proposed model effectively optimises the economic benefits of freight transportation and solves the problem of insufficient information timeliness.