In recent years, the society has paid more and more attention to the problem of excessive greenhouse gas emissions, and the carbon emissions in the transportation industry have accounted for one-fifth of the global carbon emissions. Therefore, reducing the carbon dioxide emissions in the transportation industry can help The whole society has made a huge contribution to energy conservation and emission reduction. In order to improve transportation efficiency while reducing carbon dioxide emissions, transportation methods and transportation routes should be selected reasonably. This article is based on these aspects as a starting point to study the selection of transportation routes and methods in multimodal transportation. Based on time constraints, this paper comprehensively considers carbon emissions and transportation cost factors, constructs a low-carbon multimodal transportation route optimization model that targets the carbon emission cost, transportation cost and transshipment cost of cargo transportation, and uses genetic algorithms to obtain the optimal multimodal transportation. The genetic algorithm is used to obtain the optimal multimodal transport combination mode and route, and the feasibility of the model is verified through case analysis. The results show that the low-carbon multimodal transport mode can effectively reduce carbon emissions by integrating the technical and economic advantages of multiple modes of transportation, has a significant effect on energy conservation and emission reduction, and is conducive to promoting low-carbon sustainable economic and social development.


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    Title :

    Research on optimization of multimodal transport path under low-carbon background


    Contributors:
    Song, Wenbo (author)

    Conference:

    International Conference on Algorithms, Microchips and Network Applications ; 2022 ; Zhuhai,China


    Published in:

    Proc. SPIE ; 12176


    Publication date :

    2022-05-06





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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