Kadir Dönmez

Integrated gate assignment and ground movement scheduling faces a long-standing challenge: plans optimized for nominal schedules frequently fail when arrival or pushback times deviate, yet robust formulations struggle to scale at hub traffic densities. Traditional approaches face a dichotomy between exact methods on oversimplified layouts and metaheuristics that sacrifice optimality for speed. This study introduces a two-stage approach that breaks this trade-off through three contributions. First, we propose a high-fidelity MILP-as-Policy approach with scenario-based robustness. Unlike methods that simplify airport graphs for tractability, our approach operates on full-scale geospatial representations. Novel lossless spatial and temporal pruning techniques tame the quadratic growth from uncertainty scenarios, enabling a single gate-route plan guaranteed feasible across all sampled delays. Applied against state-of-the-art deterministic methods, the robust planning stage achieves 0% gate conflicts where baselines fail in 100% of scenarios at peak load; the real-time execution stage solves with median time of approximately 12 seconds. Second, the approach is engineered for universal portability and structural generalizability. Using an airport-agnostic graph representation, the approach can be deployed across airside geometries by updating standard geospatial data alone. Validation at both Singapore Changi and Istanbul Airport confirms that pruning effectiveness and solve times generalize across diverse taxiway topologies. Third, an offline Monte Carlo policy tuning protocol optimizes tactical execution by sweeping objective weights to traffic characteristics. At peak loads, selected policies achieve up to 10-15% lower delay and up to 2.5% lower fuel consumption than neutral baselines. The approach demonstrates scalable, uncertainty-aware gate-taxi optimization at hub airports, bridging robust strategic planning with adaptive tactical execution.

This paper develops and compares four distinct constraint-aware and adaptive matheuristic approaches based on tabu search, simulated annealing, bacterial foraging optimisation and multi-ant colony optimisation. By integrating metaheuristic search with a mixed-integer linear programming (MILP) model, a hybrid framework is designed to optimise aircraft departure sequencing with rigorous precision. The core novelty lies in how this integration reconciles the exactness of mathematical programming with the interpretability of metaheuristics. Standard MILP models offer precise solutions but function as opaque ‘black boxes’, while standard metaheuristics operate via understandable moves but often rely on simplified feasibility checks. The proposed solution merges these strengths, employing adaptive metaheuristics to generate candidate sequences using interpretable moves while a restricted MILP acts as a high-fidelity evaluator. Fixing the sequence enables the MILP to focus solely on optimising continuous timings and holds. This ensures that every candidate step is strictly feasible and optimally timed, turning the solution process into a sequence of human-readable queue adjustments backed by the numerical precision of an exact solver. In a calibrated Antalya Airport case study with 40 aircraft, all algorithms attain the MILP optimum or remain within 1% of it, achieving a 29% reduction in hold fuel compared to first-come-first-served (FCFS). In denser 50-aircraft scenarios, the approach maintains high solution quality within feasible time limits, proving that explainability does not require sacrificing computational efficiency.

Safe and efficient flight operations depend on effective air traffic management and the decision-making skills of air traffic control officers (ATCOs). However, managing air traffic in terminal control areas (TMAs), especially in approach control units, is challenging due to the complexity of the airspace. This is particularly evident in metroplex airspaces like the Istanbul TMA, which features multiple civil and military airports, diverse approach systems, and heavy traffic volumes, all contributing to an exceptionally complex operational environment. This study examines how experienced ATCOs perceive airspace complexity, focusing on approach control units within TMAs. Using Istanbul TMA as a case study, the research combines qualitative and quantitative methods to analyse the factors contributing to complexity. In the first phase, the Content Validity Method (CVM) is used to identify and confirm the key factors influencing airspace complexity. In the second phase, the Best-Worst Method (BWM) is applied to measure the importance of these factors. The study involves two groups of ATCOs: 40 in the first group and 20 in the second. The results reveal that ‘conflicts’ are the most critical factor affecting airspace complexity, highlighting the importance of conflict resolution in air traffic control. Other significant factors include rules and procedures, airspace design and traffic density. This study provides clear insights into the challenges of managing TMA, especially in metroplex airspace. Identifying and analysing the key factors offers valuable guidance for improving air traffic management and supporting ATCOs in making better decisions.