Navigating the challenges of the Electric Vehicle Routing Problem with Battery Swapping Stations (EVRP-BSS), this work is centered on a multi-objective optimization task, simultaneously minimizing battery swap costs and energy consumption costs. Given the intricate nature of this problem and its real- world implications, we propose a particular solution methodology. Our hybridized approach introduces a learn-heuristic that leverages the Non-dominated Sorting Genetic Algorithm II (NSGA II) and the Q-learning algorithm. This method not only addresses the NP-hard complexity of the problem but also aims to improve the sustainability and cost-effectiveness of electric vehicle routing operations. In contributing a fresh perspective to the discourse on efficient and eco-friendly transportation, our study explores novel avenues for sustainable solutions. The experiments showed the good performance of the proposed approach for solving the EVRP-BSS.


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    Titel :

    Integrating NSGA-II and Q-learning for Solving the Multi-objective Electric Vehicle Routing Problem with Battery Swapping Stations


    Weitere Titelangaben:

    Int. J. ITS Res.


    Beteiligte:
    Haddad, Anouar (Autor:in) / Tlili, Takwa (Autor:in) / Dahmani, Nadia (Autor:in) / Krichen, Saoussen (Autor:in)


    Erscheinungsdatum :

    01.08.2025


    Format / Umfang :

    17 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch