The impact that rescheduling aircraft maintenance may have on both the network and maintenance roster is hard to grasp due to the inherent complexity of the problem. Various works have been dedicated to developing methods for recovering a disrupted airline schedule, with the primary focus being the recovery of the lines-of-flight in the days after the disruption. More recently, attention has been paid to the specific recovery of aircraft maintenance, constrained by the resources required. However, no comprehensive method exists that combines both network and maintenance scheduling in order to assess the effects of rescheduling maintenance in favor of flight execution. This paper presents a novel framework that provides decision-makers with the opportunity to assess the outcome of a choice they are about to make: what are the short- to medium-term effects of delaying airline maintenance in favor of operating a flight instead of canceling it? At its core lies a mixed-integer programming formulation, which considers a double space-time network, allowing the integral scheduling of network rotations and maintenance tasks in ground slots. A case study involving a heterogeneous fleet of 54 aircraft from a major European airline demonstrated the framework's effectiveness in scheduling 1,886 maintenance tasks and 471 flight rotations across 58 ground slots. The framework shows that preventing a rotation from cancellation by rescheduling maintenance results in approximately 13 schedule changes and 10 hours of additional delay within a two-week period, compared to the same schedule without the maintenance rescheduled. Thus, while the airline has prevented a rotation from cancellation, this came at the cost of multiple scheduling changes and additional network delays. It can be concluded that the initial formulation proves to be beneficial in assisting decision-makers in assessing the implications of delaying aircraft maintenance.

The air cargo market is defined by the transportation of high-value, often specialized goods, requiring precise handling. From a planning perspective, air cargo operations involve complex, nested decision-making across multiple levels. While previous research has addressed fleet assignment, cargo routing, and air cargo load planning as individual problems, few studies attempt a fully integrated approach. This work presents a comprehensive model that jointly optimizes fleet assignment, Unit Load Device (ULD) routing, and cargo allocation to ULDs within a full freighter network. We propose two solution methods: a sequential approach and an integrated approach. The sequential approach first performs a myopic fleet assignment and cargo routing, treating each aircraft as a single bin without assigning cargo to ULDs or routing ULDs. These tasks are then addressed in the second stage, ensuring compliance with bin-packing and compatibility constraints in the final result. In contrast, the integrated approach employs column generation to optimize all routing and packing decisions simultaneously. Both models were tested on synthetic instances based on a major European combination airline's freighter operations. Results indicate that the integrated approach consistently outperforms the sequential method, achieving profit increases of 6-24\% across various operational scenarios. This improvement is driven by the ability to yield higher revenue while lowering ULD operating costs.

Air traffic sector demand and capacity balancing is an important process to enable safe and efficient flight execution. In current operations, demand and capacity are determined based on schedules and flight plans. In reality, disruptions to flights create a different situation that may not have been anticipated by the Air Navigation Service Provider (ANSP). Wrong demand forecasts may cause unnecessary network regulations or inefficient flight execution. This research aims to improve air traffic sector demand forecasting, by exploring machine learning based trajectory prediction. In light of the Trajectory Based Operations (TBO) concept that is being developed within Air Traffic Management (ATM) research, a trajectory-based approach is taken to improve demand forecasts. To achieve this, the transformer neural network was identified as a suitable generative model that can predict aircraft trajectories. Using available traffic messages from the Eurocontrol Business-to-Business (B2B) connection, and actual trajectories obtained from the OpenSky ADS-B repository, a successful transformer neural network was built. This trajectory predictor could accurately generate trajectories, outperforming the flight plan and other neural network approaches by a large margin. For demand prediction, the introduction of improved trajectories provided small gains that could potentially lead to more stable predictions.

The evolution of aerial vehicle technology necessitates robust trajectory prediction models. These models are crucial for maintaining safe airspace and enabling autonomous operations. Automatic dependent surveillance–broadcast (ADS-B) is a surveillance system that enables aircraft to receive data from navigation satellites and periodically broadcasts it, enabling it to be tracked. Moreover, using ADS-B data for more general aerial vehicles has become a popular trend because it can provide real-time high-resolution aircraft state information and share this information with other vehicles in real-time for the aviation safety ecosystem.

In this project, we delve into ADS-B-based trajectory prediction for both aircraft and drone motion trajectories with the overarching goal of improving prediction accuracy. We initially implement several model-based Kalman filters—including interactive multiple models (IMM)—to assess the accuracy of aircraft trajectory predictions across different model structures. The results reveal that the IMM filter outperforms the single model predictions in terms of root mean square error (RMSE).

Furthermore, we implement the Gaussian process (GP) with a sliding window scheme to predict online drone trajectories. Recognizing the high computational complexity of the GP, we also introduce a low-rank approximation method, structured kernel interpolation (SKI) GP, aiming to conserve computational resources. Finally, we compare the prediction performances of the IMM filter, classical GP, and SKI GP on real drone trajectories. The results highlight that the classical GP method enhanced prediction accuracy, achieving an RMSE of less than 1.7m, which is 50% lower compared to the model-based IMM filter. Additionally, the SKI GP realizes a 25% reduction in computation time compared to the classical GP, despite a slight compromise in prediction accuracy.

Interest grows rapidly in electric and hybrid electric aircraft. To determine the optimal performance and energy management required with such novel powertrain configurations, a knowledge-based aircraft and powertrain performance model is developed. The model is then used to set up an optimal control problem, which is transcribed to a non-linear programming problem using global orthogonal Legendre-Gauss-Radau collocation for single phase problems, and Hermite-Simpson local collocation for multiphase problems. The solution to the control problem allows identification of the best control strategies and energy management strategies. A case study is performed on the HY4 hybrid fuel cell aircraft and the hybrid electric Pipistrel Panthera. Solutions show that for best fuel economy, flying at a minimum drag airspeed, and keeping a constant power setting, proved more important than the choice of altitude. This was more noticeable for the HY4, with its relatively low power available and good aerodynamic properties following from its glider-based airframe.
The Fuel-optimal energy management strategies proved identical for both aircraft investigated. Batteries are used to provide a power boost during takeoff, after which batteries are discharged gradually throughout the remainder of the flight to maximize discharge efficiency. The engine or fuel cell are kept at approximately constant cruise power settings throughout the flight. The Panthera showed
consistent flight profiles with increasing range. For the HY4, however, achieved airspeeds reduced with increasing range, and additional measures were required to force a climb to non-zero altitudes due to its under-powered nature. The fuel-optimal trajectories offered an average of 10-15% of possible fuel
savings, depending mostly on the size of the onboard batteries. Fuel savings increased significantly at low ranges300km, where the contributions of the batteries have more impact.
Comparing different transcription methods and problem setups, it was concluded that global orthogonal, or pseudo-spectral, methods like Legendre-gauss-Radau collocation are not only faster, but also more consistent compared to simpler direct collocation methods. However, if the problem complexity increases and the performance limits of the aircraft are pushed, switching to a simpler method like Hermite-Simpson collocation reduced the time required to find a solution, with negligible differences in the resulting trajectories. Opting for a multiphase problem set-up, essentially splitting the problem in a series of individual subproblems, appeared less advantageous. While offering more control over the trajectories, time required to find solutions increased drastically, and offered no additional insight into the best energy management strategies.