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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

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


    Additional title:

    Int. J. ITS Res.


    Contributors:


    Publication date :

    2025-08-01


    Size :

    17 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English