A multi-objective slot allocation framework with adaptive fairness for coordinated airport clusters Available to Purchase

This study introduces a multi-objective slot-allocation model designed for coordinated airport clusters, with a focus on adaptive fairness. The model optimises the trade-off between minimising schedule deviations, promoting fairness among airlines and airports, and limiting individual flight adjustments. Utilising an $ε$-constraint method, the model transforms the multi-objective problem into a single-objective framework, with dynamically adjustable fairness constraints based on real-time flight volume. A novel piecewise function links the trade-off parameter (⁠$ε$⁠) to flight volume, allowing the model to adaptively balance fairness and efficiency. Key operational constraints – airport and waypoint capacity limits, turnaround times and maximum deviation thresholds – are incorporated. A case study of the Beijing–Tianjin–Hebei airport cluster validates the model, showing significant improvements in fairness and operational efficiency. Slot-allocation adjustments of 75 min (Beijing Capital airport), 70 min (Beijing Daxing airport; ZBAD) and 35 min (Tainjin Binhai airport; ZBTJ) were achieved, with fairness indices improving at ZBAD (from 1.43 to 1.11) and ZBTJ (from 2.42 to 0.93). The adaptive model ensures equitable resource distribution among airlines, reducing maximum fairness deviation from 1.35 to 0.51. Sensitivity analysis confirms the model’s robustness, providing a scalable and practical solution for multi-airport systems facing dynamic traffic demands.