A bi-objective slot allocation model under airport capacity and resource utilization

Most airports operate below their declared capacity, yet expansion through costly infrastructure development remains the primary strategy for meeting the rising demand for flights. Inefficient slot allocation, underutilised airside resources, and a lack of detailed demand–capacity analysis hinder performance, leading to rejected slot requests that may cost the industry billions of dollars annually. This research proposes a practical alternative: optimizing existing capacity before pursuing expansion using a bi-objective mathematical model. This model simultaneously maximizes the utilisation of runway, apron, and terminal gate capacity through revised slot scheduling, and incorporates real-time operational constraints to minimise delay propagation while maintaining separation minima. The model was validated using real data from Bandaranaike International Airport-Colombo (BIA). The proposed linear programming model demonstrated increased average airside resource utilisation on peak days, from 44.9% to 62.7%, while ensuring that the current schedule peak traffic intensities are maintained. Through delay optimization, the proposed schedule is capable of reducing congestion and cumulative delays compared to the non-optimized schedule, mainly when delays are propagated due to uncertainties. With an average delay reduction of 140.80 min per scenario, the model's validity was confirmed, providing strong evidence of its robustness and reliability. These results demonstrate the potential of optimized slot allocation as a decision-support tool, enabling fairer access for new entrants, reducing delays, and enhancing efficiency across existing operations.