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    This study optimizes aircraft gate assignments by balancing passenger walking distance and operational costs. A novel two-phase large neighborhood search (2PLNS) algorithm demonstrates superior performance over existing methods for the gate assignment problem (GAP).

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    Area of Science:

    • Operations Research
    • Transportation Science
    • Airport Management

    Background:

    • The gate assignment problem (GAP) is crucial for airport operational efficiency and passenger satisfaction.
    • Existing models often simplify GAP, neglecting key bi-objective considerations.
    • Balancing passenger convenience and operational robustness is a complex challenge in airport logistics.

    Purpose of the Study:

    • To model the GAP as a bi-objective constrained optimization problem.
    • To minimize total passenger walking distance and total robust gate assignment cost.
    • To develop an efficient algorithm for solving the complex GAP.

    Main Methods:

    • Formulated GAP as a bi-objective constrained optimization problem.
    • Developed a two-phase large neighborhood search (2PLNS) algorithm incorporating a greedy and stochastic strategy (GSS).
    • Constructed real-world instances using data from Baiyun Airport (CAN).

    Main Results:

    • The proposed 2PLNS algorithm significantly outperforms state-of-the-art algorithms on both synthetic and real-world instances.
    • 2PLNS demonstrates improved convergence speed and avoidance of local optima.
    • Empirical analysis validates the effectiveness of the GSS within the 2PLNS framework.

    Conclusions:

    • The bi-objective GAP model effectively captures operational and passenger-centric objectives.
    • The 2PLNS algorithm provides a superior solution approach for the gate assignment problem.
    • Efficient trade-off selection from non-dominated solutions is a key consideration for practical implementation.