Considering transportation demand and temporal uncertainty in the multimodal transportation process, a data-driven multimodal transportation path optimization model under uncertainty is suggested to lower operational costs, enhance customer satisfaction, and lower pollutant emissions. The model firstly adopts data analysis and Monte Carlo algorithm to quantify the uncertainty of transportation demand and time; secondly, based on the constraints of transportation capacity, time window and carbon emission, and taking into account the preference of the carriers, the multi-objective optimization model is constructed with the objectives of minimizing the operation cost, maximizing the customer satisfaction and the environmental friendliness, and the model solution method based on the combination of the NSGA-II and the Ideal Point Method is designed. Finally, numerical experiments are used to verify the effectiveness of the proposed model and solution method. The experimental results show that the multi-objective optimization scheme obtained from the designed model can reduce the operation cost, improve customer satisfaction, and reduce the impact of environmental pollution more effectively than the single-objective optimization scheme.


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    Title :

    Data-driven multimodal transportation path optimization under uncertainty conditions


    Contributors:

    Conference:

    International Conference on Smart Transportation and City Engineering (STCE 2024) ; 2024 ; Chongqing, China


    Published in:

    Proc. SPIE ; 13575


    Publication date :

    2025-04-28





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English