As an efficient logistics mode, multimodal transport can effectively integrate the advantages of various transportation modes and reduce energy consumption and carbon emissions. Aiming at the low-carbon multimodal transport path optimization problem, this paper provides a hybrid optimization method combining genetic algorithm and particle swarm optimization algorithm, establishes a multi-objective optimization model considering transport time, cost and carbon emission, and designs a PSO-GA hybrid algorithm to realize the comprehensive optimization of multimodal transport path. Finally, a numerical example is given to verify the effectiveness and feasibility of the proposed method. The results show that the PSO-GA hybrid algorithm can effectively reduce the transportation cost and carbon emissions of multimodal transportation while ensuring transportation efficiency, and provide theoretical support and practical guidance for the low-carbon development of China’s transportation industry, which will help China set a model of low-carbon development in the field of global transportation and make positive contributions to the global response to climate change.


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    Titel :

    Low-Carbon Multimodal Transport Path Optimization Based on Genetic Particle Swarm Algorithm


    Weitere Titelangaben:

    Lecture Notes in Civil Engineering


    Beteiligte:
    Meng, Lingyun (Herausgeber:in) / Qian, Yongsheng (Herausgeber:in) / Bai, Yun (Herausgeber:in) / Lv, Bin (Herausgeber:in) / Tang, Yuanjie (Herausgeber:in) / Wang, Kun (Autor:in) / Zhang, Jingzhuo (Autor:in) / Shao, Huinan (Autor:in)

    Kongress:

    International Conference on Traffic and Transportation Studies ; 2024 ; Lanzhou, China August 23, 2024 - August 25, 2024



    Erscheinungsdatum :

    14.11.2024


    Format / Umfang :

    8 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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