Reserve cockpit crew allocation at major airlines is typically determined by scaling historical averages to planned network demand, without uncertainty quantification or systematic incorporation of operational drivers such as crew illness rates, seasonal absence patterns, and calendar effects. Within the broader workforce forecasting literature, uncertainty quantification is largely absent, and moreover, only two prior studies have addressed reserve cockpit crew demand forecasting directly, neither of which covers the European short-haul fleet or produces probabilistic forecasts across multiple planning horizons. This paper presents a probabilistic forecasting framework for cockpit crew reserve demand, evaluated on a cockpit division at KLM Royal Dutch Airlines across forecast horizons of 30, 90, 180, and 360 days. A direct multi-step forecasting strategy is applied, with dedicated models trained per horizon. A diverse set of classical, machine learning (Random Forest, LightGBM), and deep learning models (DeepAR, NHiTS, Temporal Fusion Transformers) is benchmarked on point accuracy and probabilistic calibration, with quantile regression and level-set forecasting (LSF) as uncertainty quantification methods. The experimental results show that the LightGBM model, optimized via quantile regression, achieves the lowest mean pinball loss at every horizon, leading the next-best model by up to 38.0%, but exhibits under-coverage relative to the empirical 90% target at forecasting horizons of 30, 90, and 180 days. Despite its strong probabilistic performance, quantile LightGBM’s point accuracy is modest: its mean absolute error (MAE) is only 4.5% lower than a naive mean baseline at its best horizon, underscoring how little exploitable signal a point forecast alone can extract from this series. The best-performing point model overall, Random Forest, improves on the mean baseline by 9.1% MAE, still a modest margin that highlights the intrinsic difficulty of the forecasting problem and motivates a probabilistic approach. Feature importance and ablation studies show that autoregressive lags and incidental illness rates dominate short-horizon predictions, while planned network demand becomes the primary driver at H = 360; weather alarm indicators are uninformative across all horizons. The framework is extended to planned reserves, voluntary reserves, and incidental illness as separate targets: voluntary reserve usage is up to roughly four times harder to forecast than planned usage on a normalized scale, while incidental illness is the most predictable sub-target. The results establish a proof of concept for data-driven probabilistic reserve planning on the European short-haul fleet and provide a basis for an explainable decision support tool for operational crew planning. The probabilistic forecasting framework reveals that reserve demand predictability differs across forecast horizons, quantile levels, and reserve types, information that a single point-forecast approach cannot provide. ... Read more

This paper develops a system-level techno-economic optimisation framework to assess European aviation transition pathways from 2025 to 2050. The model jointly determines fleet evolution, technology adoption, energy-carrier supply, and infrastructure in a mixed-integer linear programming formulation. A three-objective optimisation over discounted system costs, cumulative well-to-wake CO2 emissions, and upstream clean energy demand is solved using the AUGMECON2 ϵ-constraint method to construct Pareto frontiers, after which a representative compromise is selected via a normalised closest-to-utopia metric. Results show pronounced and asymmetric trade-offs. Cost minimisation yields the lowest expenditures but produces CO2 emissions around six times higher than the emissions-optimal benchmark. Emissions minimisation delivers deep abatement, yet increases both costs and clean energy demand by roughly a factor four due to deployment of capital-intensive and upstream-energy-intensive technologies. Clean-energy minimisation reduces upstream demand but still results in emissions about seven times higher than the emissions-optimal solution. Across scenarios, the relationship between costs and sustainability objectives remains strongly conflicting, while emissions and clean energy demand exhibit a non-monotonic relationship. Closest-to-utopia solutions consistently originate from cost-optimal primary runs, indicating that cost-efficient baselines provide the most flexible starting point for improving emissions and clean energy performance via ϵ-constraints. Sensitivity analysis further shows that emissions outcomes are dominated by well-to-wake assumptions, whereas costs and clean energy demand are mainly driven by market growth and SAF ambition, highlighting clean energy availability as a potential binding constraint. ... Read more

In the approach phase of a flight, aircraft transitions from the en-route phase to the final approach. The transit time between these phases is highly uncertain and frequently subject to delays. Efficient and reliable prediction of this time is essential for airline fuel planning and flight scheduling, yet current practice still relies largely on fixed deterministic buffers. Most existing work on arrival delay prediction focuses on deterministic models and aggregate indicators (e.g the difference between planned and actual arrival times), often at forecast horizons during the airborne phase. This paper develops and validates an explainable probabilistic forecasting model for flight duration within the Amsterdam Schiphol (AMS) Flight Information Region (FIR), with a forecast moment in the pre-departure phase. The primary objective is to forecast the duration within the AMS FIR using information available at planning while providing interpretable contributors of delay that can be clearly communicated to flight dispatchers and pilots. The study uses a historical operational dataset of roughly 280,000 inbound flights, combining airline planning data, AMS traffic data, and METAR/TAF weather reports. On the held-out test set, the model achieves a reduced MAE of 33% and a reduced RMSE of 26% relative to the current operational baseline, while capturing about one third of the variance in FIR duration (R2= 0.33). Error analysis shows that typical delay contributors are captured logically and have intuitive effects on the predictions. It is found that the largest under-predictions are mainly driven by weather forecast errors and unexplained tactical ATC interventions. The results indicate that quantile-based transit time forecasts can provide airlines with a more risk-aware basis for fuel and schedule planning than fixed deterministic buffers. However, the relatively low R2 shows that a substantial share of the variation in FIR duration remains unexplained, largely associated with tactical ATC interventions that occur under otherwise acceptable weather and capacity conditions. ... Read more

Island airports face unique decarbonization challenges: highly variable electricity demand, limited land, and a costly reliance on diesel. This study quantifies the techno-economic potential of hybrid renewableenergy systems (HRES) at Colombias two Caribbean airports Gustavo Rojas Pinilla (ADZ) located at San Andrés island and El Embrujo (PVA) located at Providencia island.
A two-stage workflow is developed. Stage 1 reconstructs 2024 hourly demand by (i) distributing monthly utility-metered energy with a reference 24 hours load-share pattern, and (ii) adjusting that baseline using flight movements and ambient temperature from 2024; inputs are Open-Meteo re-analysis, Flightradar24 traffic logs, and Aerocivil electricity bills. Stage 2 solves a mixed-integer linear programme that selects optimal capacities and hourly dispatch of photovoltaic (PV), wind, lithium-ion storage batteries (separate PV- and wind-coupled banks), and back-up diesel generation. Six energy systems are explored; four relevant configurations-full hybrid (PV+Wind+Diesel), Renewables-combined (PV+Wind), Renewables-only (PV) & (Wind), and Diesel Renewables (Diesel+PV) and (Diesel+Wind) are analysed in detail.
Results for 2024 show that a full hybrid can satisfy the airports loads at present-worth costs of US$ 458,000 for Gustavo Rojas Pinilla and US$ 11,700 for El Embrujo, reducing diesel use by 82% and 79 %, respectively, relative to current practice. The battery banks operate at moderate time average states of charge (30 - 40 %) with roughly 280-416 equivalent cycles per year. The optimal system build-outs for San Andrés comprise approximately 1 ha of land for 2,461 PV modules (generating 1752 MWh annually), together with four midsize wind turbines requiring about 2 ha (producing 1281 MWh), less than1 MWh of battery storage, and 580 MWh of diesel generation. In contrast, Providencia requires only 228 m2 of land for 57 PV modules (producing 30 MWh), a single wind turbine occupying roughly 0.5 ha (generating around 31 MWh), about
16.5 kWh of battery storage, and 15 MWh of diesel use.
The methodology outlines a framework that utilizes publicly available data for demand analysis and a clear Mixed-Integer Linear Programming (MILP) optimization approach. This framework serves as a reproducible and adaptable model for small-island airports aiming to create resilient and low-carbon energy systems. It not only reduces reliance on diesel power but also promotes the development of locally generated energy solutions, thereby enhancing energy autonomy and sustainability in remote and island airport settings.
The islands located in the Caribbean or in the Pacific often benefit from favourable weather conditions, including consistent solar irradiance and adequate wind speeds, which facilitate the effective implementation of renewable energy technologies, particularly solar and wind power systems. ... Read more

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. ... Read more

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. ... Read more

Aircraft maintenance is critical to an airline's operations to ensure the reliability, availability, and safety of their assets. Recently, the approach of using component prognostics in aircraft maintenance has received increasing attention in academic- and industrial research. Predictive maintenance has demonstrated promising results in using sensor-based prognostics for maintenance decisions. In this paper, we propose a novel predictive maintenance framework that is capable of mapping the individual component degradation levels to an optimal maintenance decision. The independent component degradation levels are computed by a supervised learning model, called "Long Short-Term Memory Networks". Subsequently, the computed degradation levels are utilized in a multi-component maintenance decision framework, by using a model-free reinforcement learning technique named "Deep Q-Learning". The predictive maintenance framework aims to minimize a cost objective based on the type and frequency of a maintenance action. In addition, we analyzed several key performance indicators, such as the number of components used, the component utilization level, as well as the wasted component lifetime. The predictive maintenance framework was evaluated using NASA's turbofan degradation dataset. Ultimately, the results of the numerical experiments showed that the proposed predictive maintenance framework resulted in lower costs than when using a time-based and corrective maintenance policy and competitive costs compared to an ideal maintenance policy. The proposed predictive maintenance framework opens new directions for multi-component sensor-based maintenance decisions. The results found form the basis for application suggestions and future research directions in practice. ... Read more

Over the recent years a significant amount of research has been conducted to develop models which are able to estimate a components Remaining Useful Life (RUL) based on available sensor readings. In this research a deep learning (DL) model in combination with a similarity-based curve matching technique is used to estimate the RUL of a component. The data-driven RUL estimation scheme consist of an online and offline step. In the online step, a 1-dimensional Convolutional Auto-Encoder (1DConvNet-AE) is trained in an unsupervised way to convert multi-sensor readings, collected from historical run-to-failure instances, into a 1-dimensional reconstruction error vector. The set of generated 1-dimensional reconstruction error vectors is used to generate 1-dimensional Health Index (HI) curves which represent the various degradation paths of the run-to-failure instances. The HI curves based on historical run-to-failure instances are stored in the HI library. In the online step, multi-sensor readings of a test instance are converted into an online HI curve, representing the degradation path of an operational component. By using the similarity-based curve matching technique, the online generated HI curve is matched with the HI curves situated in the HI library. The RUL is estimated based on a weighted average of a set of matches which pass a set similarity threshold value. The proposed procedure is tested on an operational dataset. This research shows the existence of a relation between the increase of reconstruction error and health deterioration over time. The RUL estimation performance is considered in an operational evaluation procedure in which a similarity threshold value is included. This research shows a usable RUL can be estimated directly from raw multi-sensor input data by the proposed model. ... Read more

The competition in the airline industry has rapidly increased during the last decades, especially with the entrance in the market of low-cost carriers. The high costs incurred in Maintenance, Repair and Overhaul (MRO) activities are generating a great interest in the improvement of maintenance operations as a way to stay competitive in the market. At the same time, the new generations of aircraft are increasingly being equipped with sensors that monitor the component's health condition. This stimulates the shift towards data-driven predictive aircraft maintenance, which is enabled by prognostics. This study proposes a model for maintenance scheduling of a fleet of aircraft based on component Remaining-Useful-Life prognostics and a limited stock of available spares. A discrete-time, rolling horizon approach is proposed, resulting in a sequence of scheduling time windows. For each time window, the goal is to find an optimal maintenance schedule. Moreover, the scheduling model considers three stages in decreasing order of maintenance priority, from critical aircraft leading to grounded condition, to predictive alerts, to non-critical failures. The results show that a cost-efficient maintenance schedule for a large fleet of aircraft is generated with an outstanding computational performance. Moreover, the aircraft operating costs are significantly reduced in the long-run, even when considering limited spares. ... Read more