Machine learning algorithms and models for airport gate assignment problem: A systematic literature review

As global air traffic continues to grow, the efficient utilization of airport terminal gates has become critical for adhering to turnaround schedules, minimizing arrival delay propagation, and reducing missed passenger connections. The Gate Assignment Problem (GAP)—which involves allocating arriving (and departing) aircraft to gates under operational constraints—has traditionally been addressed using exact optimization methods, heuristics, and metaheuristics. However, these methods struggle to either scale or adapt to the uncertainty and complexity of real-world airport operations. In recent years, Machine Learning (ML) has emerged as a promising alternative or complement to classical methods, offering a fundamentally data-driven approach to prediction and adaptive decision-making. ML techniques have shown potential to anticipate disruptions before they occur, rapidly approximate optimal solutions, and learn complex, nonlinear patterns in historical gate assignments that are difficult to codify using handcrafted heuristics. Yet, despite increasing academic interest, the application of ML to GAP remains fragmented and poorly synthesized. Existing studies apply diverse ML techniques and hybrid models but rarely benchmark them against traditional or standalone counterparts, and rely on inconsistent evaluation practices—using non-standardized, often proprietary datasets with limited reproducibility—hindering comparative analysis and generalizability.