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    Area of Science:

    • Operations Research
    • Artificial Intelligence
    • Computer Science

    Background:

    • Traditional Traveling Salesman Problems (TSP) assume fixed customer locations and travel times.
    • Real-world logistics face dynamic changes in traffic conditions and customer demands.
    • Existing TSP models struggle to adapt to these real-time environmental fluctuations.

    Purpose of the Study:

    • To address the limitations of static TSP by introducing a dynamic version (DTSP).
    • To extend DTSP to the dynamic pickup and delivery problem (DPDP).
    • To develop an adaptive algorithm capable of handling real-time environmental changes in logistics.

    Main Methods:

    • Amelioration of an attention model to perceive environmental changes.
    • Proposal of a deep reinforcement learning algorithm for solving DTSP and DPDP.
    • Testing on instances with up to 40 customers across 100 locations.

    Main Results:

    • The proposed method effectively captures dynamic changes in logistics environments.
    • Highly satisfactory solutions are produced within a short computation time.
    • Demonstrated over 5% improvement compared to baseline approaches in many cases.

    Conclusions:

    • Deep reinforcement learning offers a powerful approach for dynamic routing optimization.
    • The enhanced attention model successfully adapts to real-time logistics variables.
    • The algorithm provides significant efficiency gains for dynamic traveling salesman and pickup and delivery problems.