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A Bilevel Ant Colony Optimization Algorithm for Capacitated Electric Vehicle Routing Problem.

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    This study introduces a new bilevel ant colony optimization algorithm to solve the electric vehicle routing problem. The method effectively balances customer service order and EV recharging schedules for improved efficiency.

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    Area of Science:

    • Operations Research
    • Transportation Science
    • Artificial Intelligence

    Background:

    • Electric vehicle (EV) adoption presents unique routing challenges, known as the capacitated EV routing problem (CEVRP).
    • Limited EV range and charging infrastructure necessitate simultaneous consideration of customer service order and EV recharging schedules.
    • Integrating these two aspects into a single optimization problem is computationally complex.

    Purpose of the Study:

    • To develop a novel algorithm for solving the CEVRP by treating it as a bilevel optimization problem.
    • To effectively manage both customer demand fulfillment and EV recharging constraints.
    • To enhance the efficiency and practicality of EV logistics.

    Main Methods:

    • A novel bilevel ant colony optimization algorithm is proposed, decomposing CEVRP into two subproblems: capacitated VRP and fixed route vehicle charging.
    • The upper level uses a max-min ant system to determine customer service order, ignoring electricity constraints initially.
    • The lower level employs a heuristic to optimize EV recharging schedules for generated routes, satisfying electricity constraints.

    Main Results:

    • The proposed bilevel algorithm effectively integrates routing and recharging decisions.
    • The algorithm demonstrates superior performance compared to state-of-the-art methods across various benchmark instances.
    • This approach significantly improves the efficiency of electric vehicle logistics.

    Conclusions:

    • The bilevel optimization framework provides an effective solution for the complex CEVRP.
    • The proposed algorithm offers a practical and efficient method for managing EV fleets.
    • This research contributes to the advancement of sustainable transportation and logistics.