Research on Gate Assignment Optimization Based on Multi-Neighborhood Local Search and Simulated Annealing

To effectively improve the utilization efficiency of contact gates at large airports, a hybrid optimization algorithm based on multi-neighborhood local search and simulated annealing is proposed. First, a gate assignment model is established with the objective of maximizing contact gate utilization, comprehensively considering practical operational constraints such as aircraft-gate compatibility, minimum safety time intervals between consecutive flights at the same gate, and airline-specific gate area requirements. Then, a greedy strategy is introduced to generate an initial feasible solution, and multi-neighborhood local search and simulated annealing algorithms are designed to improve the solution. Comparative analysis with branch and bound, simulated annealing, and genetic algorithms is conducted. Finally, simulation using actual airport operational data demonstrates that the proposed algorithm improves contact gate utilization by 1.36%, 1.63%, and 2.72% compared to branch and bound, simulated annealing, and genetic algorithms, respectively. This indicates that the proposed method can effectively enhance contact gate utilization and provides a practical and efficient solution for large-scale gate assignment problems.