The transfer station of railway freight system, as an important department of freight undertaking and distribution, is responsible for the demand of freight transfer. Due to the influence of many factors such as the quantity and batch of transportation, not only the railway system needs to be planned and arranged, but also the highway logistics system needs to be arranged in order to improve the efficiency of logistics transportation, thus putting forward higher requirements for the location of transfer stations. To improve the competitiveness of the freight market and rationally plan the location of railway transfer stations, this paper constructs a neural network model based on multiple indicators of cost and path to optimize the location of railway transfer stations. The traditional ant colony algorithm is improved to solve the location of transfer stations, which is to achieve the purpose of efficient freight transfer and distribution. Through the analysis of simulation experiments, the improved ant colony algorithm can be adopted to solve the location path optimization of railway freight transfer index. Compared with the BP neural network and the traditional ant colony algorithm, it is verified that the method in this paper has better calculation accuracy and convergence effect. It proves that this method has the best overall integrity. It meets the efficiency of freight distribution, achieves the goal of the lowest cost, and provides technical verification results for the location of railway freight transfer stations and freight layout planning, which has good theoretical value.


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    Titel :

    Location method of transit station for rail transit based on variable step size any colony algorithm


    Beteiligte:
    Dong, Huajun (Herausgeber:in) / Jia, Shijie (Herausgeber:in) / Wang, Lili (Autor:in)

    Kongress:

    Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023) ; 2023 ; Hangzhou, China


    Erschienen in:

    Proc. SPIE ; 12800


    Erscheinungsdatum :

    11.10.2023





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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