This study addresses the operational challenges of sustainable aircraft towing at hub airports, partic- ularly focusing on taxi delays. Using a queue-based discrete event simulation, airport taxi operations are modeled to evaluate the impact of tow-trucks on taxi delays and runway throughput. Key parameters in- clude service times for coupling and decoupling, tow-truck availability, and parallel service capacity. The findings reveal that taxi delays and runway throughput are significantly affected by the mean and variability of service times. Allowing multiple parallel service stations in front of the runway can reduce delays but may require infrastructure modifications. The study concludes that while operational towing can reduce emissions, its successful implementation depends on careful management and optimized traffic strategies to avoid compromising airport efficiency.

This thesis investigates the optimization of wingsail trim of yachts, aiming to reduce fuel consumption and travel time while maintaining a limited heeling angle. Utilizing the Sequential Least Squares Quadratic Programming (SLSQP) optimizer. The study addresses key research questions concerning the aerodynamic characteristics of wingsails, the effectiveness of reverse camber, ideal angles of attack and flap setting for various sailing conditions. The research demonstrates that optimized wingsail configurations can lead to significant fuel savings, particularly when combined with slow steaming techniques, achieving reductions of up to 26\% on specific routes. Detailed analysis of aerodynamic characteristics revealed the importance of precise control over sail settings to maximize performance. The desired lift and drag is dependent on each individually different quasi-static load case. However the main trend sailing upwind is that the optimizer aims to minimize drag and maximize lift. The main trend sailing down wind is that the optimizer aims to maximize drag and maximize lift. This research contributes to the advancement of sustainable maritime technologies. Offering incentive to continue with the industries shift towards more environmentally responsible practices, enhancing both economic and environmental outcomes in the luxury yacht sector.

Amsterdam Airport Schiphol characterises itself as a major hub in Europe. Its network is not sustainable with only the Dutch catchment area. Instead, it relies on transfer passengers. When new tax regimes and policies are implemented to combat the environmental impacts of the airport, a change in passenger flows could affect Schiphol’s network quality. The thesis proposes a MILP model to analyse the effects of new policies by simulating passenger flows between airports. A profit-based optimisation model with a greenfield airline network is formulated, which incorporates passenger classes with different time sensitivities to account for passenger preferences in the network design. This approach predicts that hubs at the edges of Europe are better gateways to the continent based on the higher share of transfers. A case study on the implementation of a transfer tax at Schiphol shows that the share of transfers declines, but the network will only see reduced frequencies of destinations.

Airline reserve fleet capacity is a strategic resource that provides airlines the means to add robustness to their schedules. This paper focuses on leveraging past data on airlines' execution of planned schedules (resulting delays, cancellations and missed connections), along with the disruptions that prohibited the flawless execution of the planned schedules, in order to assess the impact of reserve fleet composition changes on past days of operations. The fleet schedule is modeled as a set of parallel time-space networks, and a Mixed Integer Linear Programming model is defined and solved with the objective of minimising passengers' disruption costs incurred during execution of the planned schedule. By adjusting the reserve fleet within the model, the resulting impact on disruption costs and operational performance indicators is quantified. The approach presented can be applied on heterogeneous fleets and to hub-and-spoke airlines. Its performance is discussed using a case study on the network of a Full Service Carrier operating in Europe. Simulated results of all reserve fleet scenarios considered are compared with recorded passenger disruption data from the same period, demonstrating the model's capacity to emulate historically observed operational performance. Notably, the model (considering the same reserve fleet as was operated) exhibits an average deviation of 7.9% in disruption costs when compared with recorded data. The analysis of resulting disruption costs stemming from reserve fleet composition changes reveals that adding an A220 aircraft to the airline's reserve fleet would result in a slightly higher reduction (-5.7%) of disruption costs compared to adding an A320 aircraft (-5.3%). Conversely, removing an A320 aircraft from the reserve fleet would lead to a significantly higher increase in disruption costs (+20.5%) compared to removing one A220 aircraft as reserve (+16.9%). Consequently, when considering changes to the reserve fleet operated, the airline should prioritise alterations on the A220 portion of their fleet.

As turbofan technology advances, periodic engine inspection and maintenance still remain a significant part of aircraft operational costs. Operators are thus looking to engine condition-based maintenance (ECBM), leveraging sensor data and numerical engine models for continuous diagnostics. KLM Engine Services developed a surrogate model based on the High Dimensional Model Representation approach, capable of processing a large volume of engine data at a lower computational cost to estimate engine component condition. This study proposes enhancements to expand the model's capabilities, incorporating additional engine parameters and extending the operational envelope. The augmented surrogate model was then combined with a Long Short Term Memory network capable of predicting component condition based on trends generated from the surrogate model. The framework proposed has demonstrated the potential advantages of combining the surrogate and prediction model for engine diagnostics and prognostics, serving as a valuable starting point for future ECBM projects at KLM ES.

In response to the growing demand for air travel, major airports are approaching critical thresholds in their infrastructure capacity. As the transportation sector continues to expand, it is increasingly important to address environmental concerns that arise from aspects, such as noise annoyance and fuel consumption. This paper aims to enhance the existing Flexible Runway Scheduling Model (FRSM) by integrating a tabu search algorithm with Receding Horizon Control (RHC), introducing non-linear noise restrictions, and implementing more sophisticated fuel burn modeling. The main goal is to evaluate how certain improvements affect the FRSM. To achieve this, a methodology has been developed that uses a multi-objective tabu search algorithm to minimize both fuel consumption and noise annoyance while assigning flights to runways. This study provides a comprehensive analysis of Amsterdam Airport Schiphol (AAS) across different scenarios, ranging from a 1.5-hour flight schedule to a full-day simulation, revealing significant findings. For the 1.5-hour and six-hour scenarios, the tabu search algorithm achieves a 55% and 87.3% reduction in computational time with marginal losses of 0.73% and 0.19% in solution accuracy for fuel burn optimization. Throughout all scenarios, the tabu search algorithm consistently results in a reduction of highly annoyed individuals ranging from 2.14% up to 62.5% compared to the existing FRSM, demonstrating its effectiveness. Moreover, the algorithm minimizes the impact on the flight schedule in terms of delay. Notably, as the flight schedule length increases, the performance of the tabu search algorithm improves compared to the existing FRSM. A sensitivity analysis optimization horizon indicates a positive effect on results, albeit with an associated computational cost. In conclusion, this study showcases the positive impacts of the remodeled FRSM, enabling a faster and more accurate trade-off. The research findings provide valuable insights for optimizing runway scheduling at major airports while balancing efficiency gains with environmental considerations.

As the aviation industry has agreed on the goal of achieving net-zero CO2 emissions by 2050, various mitigation strategies are being deployed and developed. This paper focuses on reducing emissions at airports by towing aircraft instead of taxiing with their main engines; dispatch towing. This is done by developing a model to optimise the assignment of Electric Towing Vehicles (ETVs) at Amsterdam Airport Schiphol (AMS), in line with the airport’s emission-free target for 2030. The developed model is able to assign ETVs to flights and charging moments for the tactical planning phase, minimising fuel consumption, charging cost and number of chargers. The results of the model are illustrated for two peak days at AMS. Both a small and a large fleet of ETVs are assigned on both days for a northbound and southbound runway operation.
The total fuel cost savings for the small fleet are to 25% and 45% for the large fleet, which are similar on both days. On both days, outbound flights are most preferred direction to be towed due to the distribution of towing times. The savings per ETV are highest for a small fleet and decrease until all flights are towed. Furthermore, the load on the charging infrastructure at AMS for different fleet sizes shows what average
and peak power can be expected. It is shown that ETV utilisation and computation time can be improved significantly, by implementing costs on time and introducing utilisation and symmetry constraints. However, with the important limitation that these improvements are observed only for small planning horizons. Finally, a sensitivity analysis on charging power showed that increasing the charging rate has a positive impact on both fuel cost savings and the minimum number of chargers required. In conclusion, this study shows the potential impact of dispatch towing at AMS in terms of fuel savings, charging infrastructure and operational challenges.

Flying characteristics of a multi-engined light general aviation (GA) aircraft during and after an engine failure is often a major safety consideration when both designing and operating the aircraft. In the meantime, the propellers, being large rotating masses, can exert considerable gyroscopic effect on the aircraft during flight, itself contributing to a coupling between the pitch and yaw axis, thus affecting flight dynamics. This study presents an investigation on the impact of propeller gyroscopic effects on the flying motion of a representative twin-engine GA aircraft. This is done using a modular flight mechanics toolbox that performs analyses in both frequency domain and time domain. A steady-state windtunnel aerodynamic and control surface model with empirically estimated unsteady aerodynamic coefficients, along with a propeller governor engine system simulation, complements the gyroscopic inertia model in the simulation setup.

Firstly, a modal analysis showed that all modes aside the spiral mode does not get discernibly affected by the rotating inertia typical to the reference aircraft’s propellers. Then, time-domain simulations of various rapid maneuvers show that gyroscopic effect does cause significant change in the angular response of the coupled axis, e.g. sideslip angle response during a pitch input only maneuver, whilst its impact on long term phugoid motion remained inconclusive due to undesired and uncontrolled roll motion. To compensate for this, maneuvers were performed again with a manually tuned simple wing leveler and results showed that pitch input maneuvers does not show much deviation in phugoid motion, whereas yaw input maneuvers such as sudden left engine failure shows discernible difference in airspeed and altitude responses, though the difference in magnitude is still small. Next, comparisons made with different powertrain responsiveness showed that in a power reducing case such as sudden one engine failure, the effect of the powertrain time delay is independent from the influence of gyroscopic effects, whereas for a power increase case, such as going around, the impact of the two is
simultaneous and intertwined. Finally, a sensitivity study on unsteady aerodynamic coefficients showed that their effects on flying motion are generally independent from the gyroscopic effect.

Fuel consumption during the taxi phase of flight can be reduced by using electric wheel propulsion instead of aircraft engines. Currently, this can be either done by having external trucks towing the aircraft from the gate to the runway, or by incorporating electric motors on the aircraft wheels to power the aircraft without using the engines. The purpose of this research is to investigate whether a Hybrid Electric Taxiing System (HETS) that combines both technologies is feasible and yields better results in terms of sustainability, congestion and operation. First, a preliminary design phase of the HETS is carried out yielding the main characteristics of the system. An assessment model to study the feasibility of such system is then created by following a series of decision criteria, such as aircraft utilisation and taxi times at destination airports. Two scenarios are considered to examine the effect of the size of the network and fleet on the feasibility of HETS. The first case study includes flight data from the perspective of a single airline, while the second comprises data from five different airlines within an alliance. In conclusion, although many aircraft are eligible for the placement of electric motors, not many airports are eligible for the implementation of external vehicles. Moreover, although the introduction of HETS would lead to improvements on sustainability, it would increase airport congestion and require investments on the airport infrastructure that cannot be justified at the proposed scale.

Weather balloons have been used for decades as a means of measuring atmospheric properties. Balloons are launched twice a day from almost 900 locations worldwide, carrying measurement instruments called radiosondes. Along with data from ground-based sensors, aircraft, and remote-sensing satellites, the collected meteorological data is fed into numerical weather forecasts and climate models. The weather balloons typically burst at altitudes of around 33 km, after which the radiosonde falls back with a parachute but without any control. This leads to large amounts of waste scattered over land and sea. While the balloon material itself is fully biodegradable, the loss of radiosondes and their associated electronics can be an environmental threat. Furthermore, the loss of the filling gases such as helium and hydrogen also poses a threat to the environment – helium is a non-renewable resource and hydrogen indirectly causes warming effects when released in the upper atmosphere. These factors present the need to develop sustainable and reusable alternatives to current weather balloons. In response to this pressing need, the BRAVO family of gliders has been developed, which is short for “Balloon-Released Aerial Vehicle for weather Observation”. This report provides a comprehensive overview of the design process of creating these gliders. It introduces the detailed final design and outlines the logistical and operational procedures associated with their usage. Furthermore, the report encompasses meticulous analyses of cost and market factors, technical risks, and sustainability considerations. To ensure reliability, the gliders undergo thorough verification and validation processes. Finally, the report concludes with gained insights, recommendations, and directions for future work.F

Amsterdam Airport Schiphol aims to have a fully autonomous taxibot fleet that can taxi all incoming and outgoing flights by 2050. To support the transition towards this future state, wherein the demand for taxibots will initially be higher than the supply and operational concepts will continuously evolve, a flexible taxibot allocation model is developed. This model can optimize for different objectives: fuel savings, schedule robustness in terms of flight delays, a healthy working environment, and fairness towards participating airlines. The model is proven to be versatile and applicable to many phases towards the 2050 vision. However, this also makes the model computationally complex, which should be investigated in further research.

Commercial electric aircraft operations are foreseen in the coming five to ten years. For an airport to be ready for this introduction, energy infrastructure requirements have already been subject to various research topics. Most research in this field only focused on cost minimization of the number of chargers given a fixed flight schedule. This research however implements flexibility into the flight schedule and incorporates the energy provision in terms of renewable energy sources in combination with battery storage. Considering energy infrastructure costs as well as operational costs, the goal of the newly proposed Mixed-Integer Linear Programming model remains cost minimization. Besides a daily operational model, an additional energy balance focused optimization has been applied for data of an entire year. In this second model, energy from the local grid may be drawn and returned to come to a cost optimized solution. Both models have been run for a case-study on Bonaire International Airport where
inter-island flights to and from its two neighboring islands were electrified. A combination of solar panels and battery energy storage was found to be most cost efficient while sensitivity analysis showed many insights into possible energy business cases for airports.

Airports and airlines are examining and committing to the electrification of Ground Support Equipment (GSE). To be able to estimate the required quantity of eGSE, the charging requirements of eGSE, the change of airport electricity requirements, and the scheduling possibilities of eGSE charging for the existing turnaround procedures, a model was developed to simulate and optimize the GSE operations at airports. This was done by means of a Task Scheduling Problem (TSP), that is optimized using Mixed-Integer Linear Programming (MILP). A case study was performed on KLM's GSE fleet at Amsterdam Airport Schiphol. Based on this, it was concluded that there is no difference in the capacity that can be achieved for GSE types that can last an entire day on a single battery charge. However, another group of GSE types experiences battery depletion before the day concludes, requiring measures to maintain the capacity. The results indicate the model's suitability for strategic decision-making. Next to that, the model is effective on an operational level. The use of the model has the potential to make the use of resources in the operation more efficient.

In this paper, the emissions mitigation potential of implementing operational towing at major European airports is evaluated. Using mixed-integer linear programming, a scenario simulating mixed taxiing operations is used to find the relationship between fixed vehicle costs and target emission savings at an airport and European level. Using formulae derived from the ICAO Advanced Emissions Model, fuel burn and emissions are calculated for conventional taxiing, single-engine taxiing (SET), and hybrid and electric operational towing. An assignment optimization model is used to assign vehicles in a vehicle fleet to flights; yielding the minimum fuel to cover a flight schedule with mixed taxiing operations. As the number of vehicles in the fleet increases, the saving potential for each additional vehicle is calculated, linking fuel and emission savings with vehicle costs. Two scenarios are compared; Scenario 1 with hybrid towing vehicles and Scenario 2 with electric towing vehicles. Applying this method to 30 case-study airports, a maximum jet fuel reduction of 66% for hybrid towing and 57% for electric towing is calculated; outperforming SET at 29 of the 30 airports. The average taxi time and aircraft compatibility are the driving factors that influence the maximum fuel savings potential. The total average taxi minutes of compatible flights is thus the best metric to predict an airport's potential, with a coefficient of determination equal to 0.96 for hybrid towing and 0.97 for electric towing. Because of the shorter refuelling downtime, hybrid vehicles can service more jobs per day than electric vehicles. This means a smaller hybrid fleet size can achieve the same CO2 and jet fuel savings on a European level. As the fleet size increases, the marginal savings per hybrid vehicle decrease at a higher rate than electric vehicles. This means that the maximum emissions savings potential can be a misleading metric for comparing strategies. By using this approach to link vehicle fleet size and potential savings, a more extensive trade-off of emission mitigation strategies is possible, allowing stakeholders to find the best strategy to fit a budget or emission savings target.

Due to the growing demand in air transport, measures need to be taken to reduce the impact of noise and emissions on local communities and the environment. The generic nature of current departure procedures leaves room for improvement in the form of aircraft type dependent departure trajectories. In this study a trajectory optimization model is implemented that uses a multi-objective evolutionary algorithm to optimize departure trajectories for fuel consumption and awakenings from noise. A parameterization technique is applied that reduces the number of optimization parameters by splitting the horizontal and vertical profiles into segments, thereby reducing the complexity of the problem. The model is used to optimize a specific departure trajectory at Schiphol Airport. A 15% reduction in fuel and a 60% decrease in number of awakenings is possible when an aircraft flies a fuel or noise optimal trajectory respectively. An intermediate trajectory provides a trade-off between the two objectives that is still superior to the reference. Medium aircraft can share an optimal trajectory without significant deterioration in performance while this does not apply for heavy aircraft. The achieved fuel savings translate to lower CO2 emissions which contribute to reaching EU climate goals, while the reduction in number of awakenings results in fewer restrictions on the maximum number of aircraft movements. Both aspects allow the air transport industry to continue growing in a sustainable manner in the future.

Fleet planning is a complex problem which consists of selecting an optimal fleet of vehicles at the right moment in time to serve a specific demand while subject to different operational constraints. While Operations Research (OR) has mainly focused on long-term strategical fleet planning optimisation, certain operators such as humanitarian air services must make shorter-term decisions due to their operating environments and mission specifications. This research proposes a novel methodology for combining fleet planning and flight scheduling on a tactical time frame, weeks to months, using the United Nations Humanitarian Service's (UNHAS) South Sudan mission as a case study. The scientific approach consists of using two different linear programming models sequentially to divide the decision making process and reduce the computational complexity of the problem at hand. A multi-commodity network flow model is first used to size an initial fleet and investigate the transshipment of passengers throughout a network. The outputs are then used by a Fleet Size and Mix Vehicle Routing Problem (FSMVRP) model to further increase the accuracy of vehicle routes and passenger operations. The main results show that the methodology proposed can reduce the weekly routing costs by 40% compared to expert flight planners who schedule and route humanitarian requests on a daily basis, and reduce the fleet size by 60% from 14 air assets to 6. The research demonstrates that the developed optimisation framework can effectively be used as a decision support tool for both aircraft contracting, and flight routing and scheduling with the main objective to increase the efficiency and effectiveness of humanitarian air operations.

Stand capacity assessment is an essential factor in airport planning and design due to the large investments associated with airport development. Fulfilling anticipated demand while still considering future growth is key in airport master planning. In this paper, a mixed-integer linear programming optimisation model is proposed, which determines the stand mix and needed operational equipment through a value-focused thinking process. The decision-maker defines a priori the optimisation objectives (what to optimise for and the factors incorporated within the optimisation), after which different cases will be explored. The framework incorporates a trade-off between operational factors, different stand types, area limitations and flight frequency through two objectives: capital cost and operational cost. A trade-off between the two objectives is made through weight factors, which results in a Pareto curve. The model is validated through a case study performed using a design day flight schedule of Amsterdam Airport Schiphol. This paper shows the implications of the trade-off between capital cost and operational cost, the drivers for flight splitting, the use of swing stands, incorporation of area limitations and the implications of incorporating the weekly frequency of flights on the stand mix. It is recommended to apply the weighted sum method and the creation of Pareto curves to create a value-focused thinking process for a decision-maker. The availability of an optimisation framework, which allows airport stakeholders and decision-makers to get insights into implications of strategic decisions on stand capacity in the form of a trade-off between objectives and optimisation factors, will benefit the airport development process.

For a hub airport, capturing transfer passengers and offering better connections are key elements that largely influence its future growth and profitability. This research aims to develop a strategic stand planning model capable of selecting profitable turnarounds that will expand the network of a hub, integrating those turnarounds into the pre-existing schedule and efficiently capturing transfer passengers. The model includes 2 main goals, namely i) the maximization of the number of captured transfer passengers and the ii) minimization of connection times, which are integrated with 2 other objectives, iii) the minimization of unassigned turnarounds and iv) the minimization of tow moves. The 4 objectives are solved hierarchically in the order: iii)-i)-ii)-iv). In an attempt to tackle the inherent complexity of models that include transfer passengers, the problem is split into two less complex levels. One solves the multi-objective function and the other delivers a specific stand assignment. This approach significantly reduced the computational load for large input sets. The results show that an efficient stand planning can maximize the transfer passenger throughput by up to 38.66\% and reduce the average connection by 17.86\%. It was also possible to conclude that objectives i) and ii) conflict with each other. Further results confirmed that the time slot to which each new turnaround is allocated is the best slot for the airport, but it is not necessarily the most profitable for the airlines. This research could lead to the development of tools used by rapidly expanding airports to assess the connection potential of new routes. It can also be utilized by busy airports to optimize the current schedule and to make important time slot decisions.