Multi-Objective Optimization of Flight Sequencing in Multi-Runway Airports Using Genetic Algorithms

This study explores the multi-objective optimization of flight sequencing in multi-runway airports using genetic algorithms. As air traffic continues to grow, airports face increasing pressure to optimize resource allocation. This research focuses on the sequencing of takeoffs and landings to minimize delays, reduce runway idle time, and enhance sequence robustness while maintaining a fair delay distribution among flights. A genetic algorithm-based approach is employed to balance these objectives while adhering to safety and operational constraints. Despite its low computational cost, this method ensures high convergence and solution diversity, leading to improved airport efficiency. Experimental evaluations compare multiple multi-objective genetic algorithms under different traffic conditions, identifying the most effective solutions for complex scheduling challenges. The results demonstrate that evolutionary multi-objective optimization can reduce total delays by up to 70% and runway idle times by 60% while maintaining fairness and robustness across flights. Among the tested algorithms, U-NSGA-III achieved the most consistent and reliable performance, confirming its suitability for real-time air traffic sequencing. This work aims to contribute practical, high-performance strategies for real-world airport operations.