Genetic-algorithm-based approaches for enhancing fairness and efficiency in dynamic airport slot allocation

Airports around the world commonly face challenges in managing airport slot allocation. Effective management of limited slot resources by civil aviation authority often requires redistributing requested slots among airlines. The allocation process must operate within the prescribed capacity limits of the airport while adhering to established priorities and regulations. Additionally, ensuring market fairness is a key objective, as the value of airport slots plays a significant role in the adjustment process. This transforms the traditional time-shift-based problem into a complex multi-objective optimization problem. Addressing such complications is of significant importance to airlines, airports, and passengers alike. Due to the complexity of fairness metrics, traditional integer programming models encounter difficulties in finding effective solutions. This study proposes a neighborhood search strategy to tackle the single airport slot allocation, making it adaptable to both static and rolling capacity scenarios. Two Genetic Algorithms (GAs) are introduced, corresponding to time adjustment and sequence adjustment strategies, respectively. The GA based on the time adjustment strategy demonstrates high robustness, while the sequence adjustment strategy builds upon this GA to develop a simple heuristic algorithm that offers rapid convergence. Case studies conducted at seven airports in China confirm that all three algorithms yield high-quality adjustment solutions suitable for the majority of applications. Further, Pareto analysis reveals that these algorithms effectively balance the adjustment shifts and fairness metrics, demonstrating high practical value and broad applicability.