Accurate prediction of aircraft turnaround time (TAT) is essential for mitigating reactionary delay, yet present methods remain constrained. Existent work uses discrete event simulations to predict individual ground activities but accumulate error and uncertainty, and in turn, other data-driven studies still provide a single static point estimate that is not updated with the latest operational information. It is therefore difficult to validate the use of these models in real operational environments. Although rolling, continuously updated forecasts have recently been explored for departure delay prediction, no study has yet extended such dynamic modelling to the turnaround itself, leaving a critical gap in operational decision support, as departure delay may include additional delay sources external to the turnaround process. This study develops and operationally tests a probabilistic machine learning framework that continuously updates full predictive distributions of TAT for a European hub carrier. Real time airline, meteorological and air‑traffic feeds are merged into gradient boosting tree ensembles trained with quantile regression. Evaluation on past turnarounds yields a median absolute error of 10 minutes immediately after the preceding take‑off, falling to 8 minutes at on‑block. Results show that the uncertainty of the prediction reduces by a quarter as updated operational data like delays or air-traffic control slots come in. These findings show that uncertainty in TAT can be quantified accurately and in near real-time using data streams already present in airline operations, enabling controllers and optimisation engines to target mitigation measures proportionately and thereby reducing cascading delay, cost, and emissions.

Aircraft maintenance planning plays a large role in ensuring operational efficiency and safety while minimising costs. Hangar maintenance scheduling can be trivial due to various uncertainties, such as non-routine tasks, resource availability, and unforeseen delays. Deterministic methods might struggle to account for these complexities and do not scale well with large, heterogeneous fleets, causing frequent and costly adjustments to the schedule. Previous research has focused on incorporating different uncertainties using robust scheduling methods. This research aims to develop and assess a stochastic and scalable aircraft hangar maintenance planning model that can provide insight in the robustness of the planning, next to incorporated uncertainties, to reduce the need for frequent planning revisions. The proposed method creates a schedule using a Genetic Algorithm (GA) that minimises maintenance costs and interval losses while adhering to operational constraints. After that, a Monte Carlo simulation is applied to assess the feasibility of the schedule under randomly generated check duration scenarios. Critical checks that can cause grounding of aircraft due to exceeded due dates are modified in a feedback loop to improve the robustness of the schedule. The maintenance optimisation is tested in a case study, provided by a European airline and discusses the trade-off between maintenance cost, interval loss, run time, and feasibility in hangar maintenance planning under uncertainty. The schedule is compared to a Mixed-Integer Linear Programming (MILP) benchmark model. Results show that the MILP outperforms the MILP in terms of cost and run time, but the GA might be useful in more complex scenarios. The simulations give insight in the robustness of the planning and show that delay propagation and grounding probabilities can be decreased by adjusting critical checks during re-optimisation. The overall grounding probability can go down by 9 to 40%, with 5 to 10% of checks fixed in time, respectively. This can lead to a more robust schedule, minimising revisions. An airline can use this framework as a decision-support tool to create variations on the planning and assess the impact of its decisions on the robustness of the schedule.

Effective scheduling is challenging in Component Maintenance, Repair, and Overhaul (CMRO) operations due to the complexity of dynamically allocating resources across multiple jobs with varying priorities and technical constraints. Current industry practices typically rely on static, manual scheduling, resulting in suboptimal resource allocation and insufficient adaptability to operational disruptions. Most existing studies approach specific job shop problems by incorporating individual features, such as job prioritization or resource constraints, without considering the combined operational complexities of CMRO shops. Therefore, this research presents a scheduling model using the Flexible Job Shop Scheduling Problem (FJSSP), tailored to dynamic CMRO environments. The model uses Mixed Integer Linear Programming (MILP) to simultaneously schedule technicians and machines while accounting for skill requirements, resource constraints, and job prioritization. The approach balances multiple objectives, including tardiness and earliness, to enhance shop performance metrics such as Turnaround Time (TAT) and On Time Delivery (OTD) rates. Outcomes from a case study applied to real-world data from CMRO shops demonstrate significant operational improvements, achieving a reduction in TAT of up to 34% and an improvement in OTD by approximately 23% relative to historical shop performance. Furthermore, the model incorporates schedule robustness measures, minimizing deviations from planned schedules, despite operational uncertainties. Additionally, comparative analysis with a traditional heuristic dispatching rule model confirms the superior performance of the proposed optimization framework. This framework can be broadly applied to improve scheduling efficiency and stability in CMRO shops and similar workshop environments.

This thesis presents a novel framework for solving the Multi-Skill Resource-Constrained Multi-Modal Project Scheduling Problem with maximum time lags, addressing the challenges of scalability, deadline adherence, and uncertainty in job durations. The research is conducted through a case study with the maintenance department of a large European airline, using real-life maintenance scheduling data. To improve scalability, the framework integrates batching techniques that segment the scheduling horizon and a priority-rule-based heuristic to hot start the solver, significantly reducing computational runtimes for large problem instances. A range of objective functions, including single and multi-objective formulations, are explored to evaluate their impact on scheduling performance. The results demonstrate
that multi-objective formulations provide the best balance between throughput and deadline adherence and consistently outperform a priority-based heuristic. A clear trade-off is observed between optimizing for maximum tardiness and average tardiness, where minimizing maximum tardiness improves deadline adherence at the cost of lower throughput, while minimizing average tardiness has a more consistent throughput but allows slightly more deadline misses. To address job duration uncertainty, adaptive buffering strategies based on historical job performance are introduced and shown to outperform static buffers by tailoring slack times to individual job characteristics. In the examined case study, the combination of an adaptive buffering strategy with a multi-objective function combining makespan and
average weighted tardiness offers the most effective trade-off between robustness and efficiency. Overall, the framework proves to be scalable, adaptable, and well-suited to real-world scheduling environments with high variability and complex constraints.

The goal of this project is to develop a firefighting aircraft capable of meeting the demands of a market that lacks purpose-built aircraft and faces an ever-increasing threat. The W-132 is capable of making precise, targeted drops thanks to its high manoeuvrability, whilst maintaining a very high cruise speed and payload relative to its competitors. This report includes logistics, operations, sustainability, design process, feasibility, risk and cost analyses, providing a thorough overview of the team’s achievements.

The Europe-Asia-Oceania air route is experiencing rapid growth, traditionally served by direct legacy flights but increasingly dominated by hub-based carriers. These airlines leverage large, single-hub models to capture transfer traffic. However, limited research exists on how to efficiently design and operate multi-hub networks for international connectivity. Existing models either oversimplify hub capacities or focus solely on fleet planning and re-timing, lacking an integrated, schedule-based approach. This research develops an integrated decision support model that combines a capacitated multi-allocation p-hub location problem with airline schedule design under operational constraints and competitive dynamics. A multi-step iterative method connects hub assignment and scheduling using a genetic algorithm to ensure feasibility, connectivity, and profitability. The findings reveal that adding hubs initially boosts network efficiency and profitability, though marginal benefits diminish after a certain number of hubs. The integrated model significantly narrows the gap between theoretical and actualized profitability, showing a 7.6\% increase in daily profit for the scheduling model over iterations. This research offers a crucial decision-support tool for long-term airline network planning, particularly in rapidly expanding aviation markets. Its ability to jointly optimize hub locations and flight schedules under operational constraints provides substantial utility for diverse airline network planning scenarios, leading to more viable and profitable network configurations.

Several studies have demonstrated that an integrated approach to the airline schedule recovery problem, optimising multiple facets simultaneously rather than using traditional sequential methods, yields improved solution quality; however, at the cost of model simplification, with most studies focusing solely on cost minimisation. This paper introduces a multi-objective Benders decomposition approach to a detailed integrated aircraft and crew recovery model that considers a heterogeneous fleet, individual crew members, and basic passenger considerations, thereby addressing both model simplifications and lack of multi-objective considerations. A lexicographic multi-objective optimisation scheme is integrated into Benders decomposition, allowing for the optimisation of multiple distinct objectives: minimising cancellations, minimising recovery costs, maximising on-time performance, and minimising changes made. Computational tests were conducted using real schedule data provided by Transavia, with results showing high-quality solutions within the model’s defined assumptions. However, Benders decomposition alone was insufficient to be applicable within live disruption management, with performance times ranging from 5 to 30 minutes depending on the scenario’s complexity. The model’s design and ability to consider crew on an individual level show strong promise in solution quality and applicability to the industry, as crew are often considered at both the pairing and individual levels. This provides a strong foundation for practical disruption management support tools if deficiencies in time performance can be addressed.

The deployment and subsequent development of an Advanced Air Mobility (AAM) transportation system is expected to take an incredible amount of resources in terms of planning, time and capital. Due to the system not yet being operational anywhere, and consequently, the lack of clear operational boundaries set, researchers and developers are left with an enormous design space. Currently, parallel independent developments are taking place in all aspects of the system, and different perspectives have enabled a wide range of concepts to be formulated in each. Whereas independent assessments provide crucial insights into the individual components of the system themselves, they fail to capture the inherent interdependencies. They also do not capture the growth of the aircraft fleet in correlation with the growth of the vertiport network. This gives rise to the need for a framework which is capable of establishing a preliminary vertiport network to allow for the study of the aircraft, fleet and total system performance in a coherent manner, and the scalable correlated evolution thereof. Consequently, this study aims to develop a unified framework for the formation of a scalable vertiport allocation plan in conjunction with system-performance-based heterogeneous fleet sizing. The scalable vertiport allocator employs a distance-based agglomerative clustering algorithm to determine the clusters in the ultimate vertiport network and the k-means clustering algorithm to determine the preliminary location of the vertiport per cluster. This is followed by a commute-distance based vertiport elimination procedure to establish each vertiport network to be assessed. The optimal fleet at each stage of network growth is established through a parameter sweep conducted across the fleet size and composition. The system-based performance metrics of the combinations of vertiport network and fleet are then assessed using an on-demand agent-based simulation. The framework is applied to New York (NY) state and models of existing multi-rotor (MR) and tilt-rotor (TR) aircraft are utilized as test case. The test case results show the applicability of the framework in the establishment of a scalable preliminary vertiport allocation plan to maximize system commute distance, and the correlated growth of the optimal fleet based on the maximum system performance.

Disruptive events pose a significant challenge to airlines’ everyday operations due to the highly optimized nature of their schedules. Unforeseen events force airlines to rapidly reschedule and adjust their operations. Current disruption management methods rely mostly on reactive and static models that fail to capture the dynamic and probabilistic nature of airline recovery. This study presents a model-based reinforcement Reinforcement Learning (RL) method for aircraft recovery under disruption uncertainty that anticipates future potential disruptions. The Aircraft Recovery Problem (ARP) is formulated as a Markov Decision Process (MDP) and a framework is proposed in which an Approximate Dynamic Programming (ADP) algorithm that relies on Value Function Approximation (VFA) determines optimal recovery actions considering the immediate and future impact of each action. The uncertain disruptions are modelled as aircraft unavailabilities with a fixed probability of realizing. The aim of the model is to keep flight delays and cancellations at a minimum while exploiting stochastic information on potential aircraft unavailabilities. The model is tested on multiple scenarios with different objectives and levels of disruptions and is benchmarked against an exact optimization algorithm. Results indicate that a proactive approach outperforms reactive models, particularly in high-disruption scenarios with high aircraft utilization. The comparison with the exact benchmark shows that the RL method can achieve sub-optimal solutions with considerably less corrective actions. This framework offers a decision support tool that allows airline operators to find more resilient solutions in uncertain environments by incorporating probabilistic predictions on disruptions in the decision-making process

Managing spare parts for Aircraft Maintenance, Repair, and Overhaul (MRO) is challenging because there is a significant gap between long-term maintenance schedules and daily procurement decisions. While existing research often addresses demand forecasting and inventory control in isolation using abstract assumptions, a new framework is presented to bridge this gap by directly connecting day-today procurement decisions with the fixed, fleet-wide maintenance schedule. A task-based approach enhances traditional planning, which often relies on aggregate forecasts and can miss the specific needs of individual checks. The result is a transparent, cost-based model built for operational utility, where every decision accounts for probabilistic predictions and remains auditable. This traceability is crucial in an environment where complete historical data is often unavailable. The framework consists of two modular stages. First, a demand forecasting methodology converts raw maintenance tasks into a usable, time-phased, and probabilistic demand signal. To accomplish this, maintenance tasks are systematically grouped based on their technical attributes. The outcome is a repeatable method to describe the demand potential of each scheduled task. Second, a daily procurement optimization model was created to act on this detailed forecast. The algorithm replicates a planner’s decision-making process by explicitly comparing the expected future costs of buying, waiting, or selling surplus stock. To mirror operational reality, the model utilizes regular orders with uncertain lead times, reactive express orders, and pre-procurement. Every decision becomes a justifiable trade-off regarding the cost of purchasing and holding inventory, and the high financial penalty of a stockout. Finally, the model was validated against two benchmarks: a fully conservative (100% service) strategy and a standard periodic-review policy. The proposed model reduced total net costs for both benchmarks, achieving savings of 17.5% and 9.2% respectively. The analysis shows this advantage stems from the strategic acceptance of controlled risks when doing so leads to a lower expected total cost. Further sensitivity analysis revealed that the cost-driven logic is robust even under poor forecasts, as it automatically compensates to maintain a safe inventory level. The analysis also identifies key non-linear trade-offs, finding that total net cost is minimized at a moderate level of caution regarding stockout penalties, rather than at the extremes of underor over-estimation. The framework ultimately provides a practical and transparent decision support tool, demonstrating that a task-specific, dynamic, and cost-based approach is more effective and resilient than traditional, static planning rules.

Engine shop visit (ESV) scheduling is a critical component of airline maintenance planning, directly impacting operational continuity, cost management, and long-term fleet value. Despite its importance, existing approaches often overlook fleet-level considerations, such as additional lease engines and spare engine management. Whilst maintenance planning has been widely studied, the specific dynamics associated with engine shop visit planning remain relatively unexplored. This paper presents a Mixed-Integer Linear Programming (MILP) framework to solve the engine maintenance problem as an adaptation of the Resource Constrained Project Scheduling Problem (RCPSP). The classical formulation has been adapted significantly, as time precedence constraints have been omitted, and extensions have been introduced to incorporate engine health metrics and component-level scheduling. Furthermore, the model has been extended to allow for additional lease engine activation and to manage the number of available spare engines. The framework is applied to the operational contexts of a major European airline operating wide-body aircraft in a mixed global network, integrating airline-specific constraints and assumptions. Through sensitivity analyses on key parameters and several use-case scenarios, including an Unexpected Engine Removal (UER), the model successfully generated feasible shop visit plans under varying conditions whilst providing valuable insights to decision-makers. The results highlight the benefits of integrated planning and support operational and strategic engine fleet management.

Reliable autonomous recovery of Unmanned Aerial Vehicles (UAVs) on moving maritime platforms remains a critical challenge, primarily due to complex, stochastic deck motion, particularly vertical heave, and unpredictable environmental disturbances. Existing Reinforcement Learning (RL) approaches often simplify this environment, limiting their real-world applicability. This thesis investigates the robustness trade-offs of RL-based guidance controllers under realistic, high-dynamicity maritime conditions. We benchmarked a classical Proportional-IntegralDerivative (PID) controller against two RL architectures trained using Soft Actor-Critic (SAC) in a high-fidelity PyBullet simulation: a Full RL 3D controller and a novel Hybrid RL 1D controller, which strategically applies RL only to the critical, stochastic vertical (heave) axis. The results demonstrate that the Hybrid RL 1D architecture (86.6% success rate) achieved superior overall robustness and efficiency. Notably, the RL controllers dramatically reduced average landing time (RL_1D: 3.31 s vs. Baseline: 11.51 s), though the classical PID baseline maintained higher horizontal precision (Err𝑋𝑌 of 0.17 ± 0.17 m ). The Hybrid RL 1D maintained a superior success rate up to 89% in high sea states (SS7) and exhibited greater resilience to sensor noise. However, a critical limitation was identified: both RL-based policies experienced a pronounced performance collapse under strong, untrained wind disturbances, a regime where the non-adaptive classical PID baseline proved unexpectedly stable. These findings confirm the benefits of hybrid control for maximizing robustness and highlight that the system’s ability to handle wind disturbance rejection remains a significant, unresolved shortcoming for current RL guidance systems.

Reducing uncertainty in air traffic flow management is crucial for maintaining safety and efficiency in modern aviation. Additionally, forecasting Actual Take-Off Times (ATOT) for flights across Europe is particularly challenging due to the diverse flight-specific variables and operational conditions. This study focuses on enhancing ATOT prediction for flights arriving at Amsterdam Schiphol Airport from European out-stations by leveraging machine learning techniques, specifically a Long Short-Term Memory (LSTM) neural network, augmented with a Multihead Attention mechanism. A model capable of capturing complex temporal dependencies and operational factors influencing the ATOT is developed utilizing data from Electronic Flight Data (EFD) messages, weather reports and a EUROCONTROL dataset. The model’s performance is evaluated against traditional ensemble methods and the current Decision Support Tool (DST) system used by Luchtverkeersleiding Nederland (LVNL). Results indicate that the LSTM model outperforms existing models including a reproduction of the DST, achieving a Mean Absolute Error (MAE) of 12.05 minutes at a forecast horizon of 4 hours, demonstrating significant improvements. This assessment underscores the importance of factors such as the knock-on effect in delay prediction and suggests that integrating advanced machine learning models can significantly enhance demand forecasting, leading to more efficient air traffic management and reduced delays at Schiphol Airport.

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.

Efficient maintenance management requires an integrated approach that balances downtime (maintenance scheduling, MS) and uptime (tail assignment, TA). Current methods often use sequential decision-making, which neglects the interdependencies between MS and TA, resulting in sub optimal outcomes. Recent research has started to integrate MS and TA modelling, and separately also to integrate MS with predictive maintenance (PM) strategies. These trends highlight the need for a comprehensive approach that optimises these interdependencies and considers the uncertainties of prognostic models.

This research presents a new scheduling framework for optimal maintenance management that integrates PM, MS and TA. Historical sensor data is used with Gaussian Process Regression to predict the end-of-life of aircraft components and quantify prediction uncertainties. These uncertainties are incorporated into the decision-making process using Monte Carlo simulations. The resulting model assigns aircraft and tasks to maintenance slots and flight legs under various predictor scenarios, enabling a more adaptive scheduling strategy.

A case study with Swiss International Air Lines over a three-day period for five aircraft demonstrates the model's effectiveness, showing improvements in operational efficiency, reduced costs, fewer cancellations, and higher fleet availability. The model's predictions and schedules were validated by expert planners, suggesting that a holistic, adaptive scheduling approach has significant potential for operational improvements in the airline industry.