An optimization approach for the terminal airspace scheduling problem

Effective air traffic management within the Terminal Manoeuvring Area (TMA) is imperative for mitigating delays, minimizing fuel consumption, and reducing emissions in the aviation sector. While existing research has predominantly focused on optimizing runway sequencing, the Terminal Airspace Scheduling Problem (TASP) has been relatively understudied. This work addresses this gap by proposing an innovative matheuristic algorithm (TMAOpt) that concurrently optimizes both runway aircraft sequencing and decisions within the TMA, including runway selection, speed control, utilization of holding patterns, vectoring, and point merges. The proposed approach combines a Linear Programming (LP) model with metaheuristic algorithms, providing a unique solution approach that balances rapid generation of feasible solutions (within 1 s of computation) and convergence (within 5 min of computation). Validation of our approach involved extensive evaluations using real-world data from the congested terminal airspace of Changi Airport in Singapore. Comparative analyses with existing methods, including commercial microsimulation models like AirTOP, showcase the superior performance of our algorithm, yielding sequences that reduce delays by up to 27%. A sensitivity analysis, exploring varying degrees of permitted TMA interventions, underscores the benefits of their balanced utilization. • New algorithm optimizes runway sequencing and TMA decisions (speed, holding, vectoring, point-merge). • Combines LP model with metaheuristics for rapid convergence to near-optimal solutions. • Validated at Changi Airport, achieving sequences that reduce delays by 27%. • Sensitivity analysis highlights benefits of balanced TMA interventions.