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    This study introduces an adaptive Ant Colony Optimization (ACO) algorithm to enhance instant delivery scheduling by considering real-time logistics. The new method improves delivery efficiency across various conditions.

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    Area of Science:

    • Operations Research
    • Computer Science
    • Logistics Management

    Background:

    • Ant Colony Optimization (ACO) is utilized for instant delivery scheduling due to its distributed nature.
    • Current ACO methods face challenges in maintaining delivery efficiency with dynamic logistics statuses.

    Purpose of the Study:

    • To enhance the performance of Ant Colony Optimization (ACO) for instant delivery order scheduling.
    • To develop an adaptive ACO algorithm that incorporates real-time logistics features (AACO-RTLFs).

    Main Methods:

    • Feature extraction from event, spatial, and time dimensions to define real-time logistics status.
    • Development of an adaptive instant delivery model incorporating customer acceptable delivery time, emergency order marks, and weather conditions.
    • Proposal of an adaptive ACO algorithm with adjusted parameters based on extracted key logistics factors.

    Main Results:

    • The adaptive ACO algorithm (AACO-RTLF) effectively improves instant delivery order scheduling.
    • Numerical experiments using the Gurobi solver validated the algorithm's effectiveness on classical datasets.
    • AACO-RTLF demonstrated superior performance compared to existing state-of-the-art algorithms in instant delivery scenarios.

    Conclusions:

    • The proposed AACO-RTLF algorithm offers significant advantages for instant delivery order scheduling.
    • Real-time logistics feature integration and adaptive parameter adjustment are crucial for optimizing delivery efficiency.
    • The adaptive instant delivery model effectively accounts for critical factors influencing delivery times.