The aim of this work is to preliminary model a hybrid propulsion system that integrates fuel cells and a turbo-generator. The model is used to predict the performance of the propulsion system where parameters such as fuel consumption, and component efficiencies are monitored throughout a given eVTOL mission. Based on the analysed performance, the maximum payload the eVTOL can carry is estimated. With a fuel cell voltage efficiency of 43%, an eVTOL of MTOW of 3175 kg and a structural weight of 1905 can carry a maximum of 174 kg as a payload. The eVTOL propulsion system weights approximately 1041 kg, to which the fuel cell and its balance of plant components contributes the highest with 819 kg, where the turbogenerator contributes with 222 kg. According to the models prediction, the payload can be increased to approximately 200 kg , if a compressor efficiency of 85% was used.

This paper presents a mathematical modeling framework designed for air cargo operations of a combination airline, focusing on intercontinental passenger aircraft. This framework optimizes the palletization problem (assigning cargo to Unit Load Devices (ULDs)) and the weight and balance problem (positioning ULDs within the belly of the aircraft). Unlike the traditional approach of solving these problems in sequence, our framework employs a feedback loop between an integrated one-dimensional bin packing with weight and balance and a three-dimensional bin packing. This feedback loop iteratively refines the solution by backpropagating information when items do not fit, reducing the solution space and computation time. The palletization problem is addressed with a three-dimensional bin packing heuristic, while the integrated onedimensional bin packing and weight and balance problem is solved using a mixed integer linear programming formulation. The model employs a lexicographic optimization approach to prioritize maximizing the percent mean aerodynamic chord at zero fuel weight, leading to significant fuel reduction. Additionally, operational objectives aligned with our partner airline’s practices are optimized to reflect actual operations. Scenario analyses validate the model, demonstrating potential fuel savings across various intercontinental routes and aircraft types, with an average fuel saving of 0.53% and reaching up to 1.90%, yielding substantial economic
and environmental benefits.

Sustainable taxiing using TaxiBot vehicles is expected to contribute greatly to ambitions to reduce emissions and improve air quality in apron bays for airports around the world. However, the impact of large-scale dispatch towing operations at complex airports has never been investigated. This thesis creates a realistic simulation of ground traffic at Schiphol airport based on real-life radar data for different towing policies, to assess the impact on flow and on-time performance, congestion, and emissions.

Results show that TaxiBot towing can decrease fuel consumption and associated CO2 emissions at Schiphol airport by up to 76% per towing movement, and up to 32.9% of total airport emissions. Experiments in which aircraft are allowed to start their engines during the towing movement to save time at the unloading stations, result in outbound taxiing times that are 1 to 1.5 minutes faster than the reference scenario. Inbound times were found to become 2.5 to 3.25 minutes longer, but still contribute to CO2 savings. Apron bay flow was investigated and highlighted significant time gains of three minutes per outbound aircraft. This has the potential to create additional gate capacity at the airport, but further research is needed to quantify these gains. Congestion at unloading stations is found to be minimal, especially in experiments where engines can be started during the tow. Ultrafine-particle heat maps of towing policies show that significant relocation of emissions is possible, improving air quality in bays.

With its 500.000 flights per year, Schiphol Airport is one of the busiest airports in Europe. Efficient runway use is vital for a smooth day-to-day operation. This paper investigates the new runway scheduler at Schiphol Airport, also referred to as the Departure Manager (DMAN). The new DMAN is built for a better utilisation of the outbound capacity with a higher predictability. This study aims to verify whether positive behaviour regarding earlier Target Off-Block Time (TOBT) updates leads to the desired results in capacity, predictability and reduced delay, based on historical data. An experimental model was used to simulate a new Departure Manager (DMAN), which uses a set of priority rules to assign flights to the runway slot in which they depart. The simulation did not find an overall significant result for all cases, runways and months, due to the averages influenced by the size of the data. However, a positive trend can be seen in the presented results, indicating that earlier TOBT updates lead to a better runway schedule. One cannot schedule better than on time, but late TOBT updates accumulate like a snowball effect throughout the planning.

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.

With the drive to decarbonise the motorsport industry a variety of clean powertrains were developped and raced. While extensive lap time optimisation has been performed on hybrid and battery electric cars, no such optimisation has been performed on hydrogen fuel cell racing cars yet. This work models the Forze 8, a hydrogen fuel cell electric racing car, and compares its optimal lap, found by solving optimal control problems, to racing cars equipped with battery electric and internal combustion engine powertrains, all with the same peak power and weight on two different tracks. In qualifying scenarios, the battery electric racing car is faster than the internal combustion engine car by 1.0 [s] on both tracks, while the fuel cell electric car is slowest, trailing 5.3 and 3.8 [s] behind the battery electric car at Assen and Zandvoort respectively. In racing scenarios, the internal combustion engine car is quickest with the fuel cell electric car behind by 5.6 and 2.9 [s] at Assen and Zandvoort respectively. The battery electric car has the best single-lap performance due to its high continuous power, while the fuel-cell electric car is better suited for endurance scenarios as it carries more energy. This work shows that both battery and fuel cell electric racing cars can be valid alternatives for internal combustion engine racing cars.

This study investigates the impact of CO$_2$ emission limitations on the connectivity and profitability of a hub-and-spoke airline network, with a specific focus on Schiphol Airport and KLM Royal Dutch Airlines. As sustainability becomes increasingly critical, the aviation industry faces mounting pressure to reduce its environmental footprint without compromising operational efficiency. This research employs a Mixed Integer Linear Programming (MILP) model to simulate airline operations under CO$_2$ constraints. Using synthetic flight data and OpenAP for accurate emissions estimation, the model optimises fleet composition, flight scheduling, and passenger flow while balancing profitability and emissions reduction. The study explores the potential of fleet renewal to mitigate the negative effects of emission caps on both connectivity and revenue. The findings demonstrate that significant emission reductions can be achieved through strategic fleet planning, with minimal impact on the airline's network and financial performance. This research provides a viable framework for integrating environmental considerations into long-term airline planning, contributing to the ongoing efforts to reduce aviation's environmental impact while maintaining its economic and operational benefits.

This research investigates the impact of modeling uncertainties on helicopter handling qualities and simulator tolerances as prescribed by ADS-33 and JAR-STD. Using a six-degree-of freedom linearized model of the Bo105 helicopter, the analysis focuses on the propagation of stability derivative uncertainty on four Quantities of Interest (QoIs): Pitch Quickness, Roll Quickness, Pitch-Due-To-Roll Cross-Coupling, and Roll-Due-To-Pitch Cross-Coupling. These QoIs are evaluated across the flight envelope, from hover to maximum velocity (145 knots), capturing sensitivity variations with flight con ditions and advance ratios. To propagate uncertainty, two methods are employed: Monte Carlo Simulation (MCS) and Polynomial Chaos Expansion (PCE), with PCE reducing computation time by 98% while maintaining accuracy. Each QoI shows greater sensitivity to uncertainty at the extremes of the flight envelope. Pitch Quickness is most sensitive at high speed, whereas the other QoIs are most sensitive at hover. Cases
with both JAR-STD tolerance violations and ADS-33 HQR level changes show JAR-STD tolerances are generally more stringent, except for the Cross-Coupling QoIs, where ADS-33 level changes are more frequent.

This study presents an innovative algorithm designed to optimise the layout of floating offshore wind farms (FOWFs), addressing inter-array cable length and the number of anchor structures required. This research is particularly noteworthy as no prior work has been conducted on wind farm layout optimisation for FOWFs. The Local-Search-Inspired Layout Optimisation (LSILO) algorithm was developed, incorporating a variety of novel heuristics such as Edge Impact Destroy (EID), diversifying repair, and neighbourhood tabu search, most of which were innovatively designed for this research. The LSILO algorithm is capable of optimising cable routing while facilitating anchor-sharing and minimising cable length. In comparison to the industry standard, LSILO displayed promising results, outperforming the random benchmark by a 24% improvement in cable length. However, the algorithm does plateau at a certain turbine density. Turbine density has a substantial influence on the algorithm’s speed and rationality. Therefore, we recommend utilising LSILO with an EID of three turbines, diverse repair without alternatives, and traditional tabu search, on current floating wind turbine sites.

Airports and aviation in general play a crucial role in the transportation network of society. More than 2,500 airports support an annual traffic of 4 billion passengers. However, this number is expected to continue increasing to 8 billion passengers in 2040 according to International Air Transport Association (IATA). At the same time, the climate change phenomenon gained popularity in the society, in which the aviation industry is responsible for 3.5%, and 2% of the CO2 emissions (major contributor to greenhouse gases (GHGs) emissions). If no change is made the share of CO2 emissions are estimated to reach 20% by 2050. Specifically in airports, there is a lack of guidance on how to make the airport’s airside more sustainable. Therefore, this study aims to develop a state-of-the-art sustainable rating system for airports airsides to be able to make the aviation industry more environmentally friendly. This is done based on research from the already existing rating systems for buildings and others considering infrastructures. The focus of the one developed in this study is on the environmental pillar of sustainability from an infrastructural point of view. The developed system was named as ‘Airport Rating System’ and it was found to be a technique that can be applied in a systematic way when designing airsides, making the word ‘sustainability’ tangible.

As aviation recovers to pre-pandemic levels large amounts of Air Traffic Flow Management delay also return, as the traffic at airports and in airspaces reaches their old levels. These delays have a yearly cost of around 500 million euros in the European Civil Aviation Conference area and are detrimental to the experience of passengers. This paper proposes a change in the interaction between the A-CDM process at Schiphol and EUROCONTROL's Network Manager, varying at what point the slot improvement process for regulated flights is stopped; providing more transparency in the network with this change. The resulting change in ATFM delay is investigated by modelling these two systems and freezing the slots at a set time, using historical input data to accurately model the turnaround process. The results generated using the described methods are inconclusive. More data and an additional implementation of using the CTOTs calculated by the model in the simulated turnaround are needed in order to make statistically sound conclusions on whether changing the T-DPI-s horizon has any effect. Nevertheless, this paper still presents insights and a novel framework for continued research into the subject.

Network scheduling and fleet assignment are essential tasks for airline operation. In order to generate an optimal flight plan, the flight route and the flight schedule of each aircraft in the fleet requires deliberated consideration and planning. Compared with commercial airlines, which are in pursuit of maximal benefit during the operations, the humanitarian air services have different goals and therefore different strategies are designed. The humanitarian air service dedicates to fulfilling maximal passenger requests by reacting in a relatively short time frame, and the overall cost efficiency needs to be maximised. In this research, the United Nations Humanitarian Service’s (UNHAS) South Sudan mission is taken as the case to study and a metaheuristic method on top of the multi-integer linear programming (MILP) model is designed to solve the optimisation problem. The optimisation process consists of two stages: the tabu search process to assign the flight routes among the fleet, and a variation of a Fleet Size and Mix Vehicle Routing Problem (FSMVRP) model to finally determine the time schedule of each aircraft. The model is able to unlimitedly split the passenger requests and recaptures passenger spillage. It considers much fewer assumptions during the solving process and it provides large flexibility for the planner to manually modify the model based on their purpose. A dynamic balance of aircraft utilisation time regarding the Minimum Guaranteed Hours (MGH) within the fleet is also discussed. The result of this method is compared with the previous study of S.P. Niemansburg, which shows 1% to 11% of cost saved on a single day's operation regarding different levels of passenger spillage.

With (e-)grocery retailers striving to increase their efficiency and subsequently reduce their costs, the opportunities that lie in optimising the order picking process of the supply chain is of key importance. The complex nature of assigning articles to an optimal storage location, having efficient routing of order pickers, and optimised grouping of orders calls for an integral approach to these problems. The objective of the research is to reduce the travelled distance of order pickers and subsequently the costs of the order picking process. Next to the integral approach, a new method of Multiple Storage Locations (MSL) is introduced. Historic order data of an e-grocery retailer is used, together with information on stock keeping units (SKUs), to implement SKU allocation with MSL possibility, routing, and batching into models. This ensures that the cost saving effects of these measures can be quantified. A benchmark Mixed Integer Linear Programming model (MILP) is developed and compared to a meta-heuristic Adaptive Large Neighbourhood Search model (ALNS) to determine how much travelled distance would be saved. The ALNS has multiple destroy- and repair heuristics, some of which are novel, that are specific to the problem at hand. The ALNS is able to handle bigger instances than the MILP, whilst ensuring quality of the solution. The MILP model outperforms the ALNS for small instances, however for large instances (instances of 400 orders or more) the ALNS performs, on average, 12.5% better whilst reducing computational time by 14.8%. Finally, areas of improvement are suggested in the ALNS model as well as other effects that should be studied when introducing MSL.

Over the years, aero engines have evolved into the efficient turbofans present on commercial airliners today. Although these engines are very reliable, they still experience degradations in performance and reductions in component health. The degradation affects the operation of the engine, which can be measured using the pressure, temperature, and rotational speed sensors. Using accurate engine models together with engine sensor measurements, the condition of individual components can be determined. Surrogate models of a non-linear Gas Path Analysis model have been developed for the GEnx-1B turbofan in the form of High Dimensional Model Representations. It is determined that the surrogate models developed are able to determine component conditions with a high accuracy and low computational complexity. The models are able to properly identify the effects of water-washes and turbine failures when applied to real-world on-wing data. Due to the low computational complexity of the models, they provide the possibility for fleet-wide continuous engine diagnostics.

Within this thesis report the transition to LH2 infrastructure at the airport is tackled. Specifically, how the airport can develop and adopt the infrastructure needed for LH2 supply logistics, storage, and refueling is researched. This process can be defined as a socio-technical transition in which a new niche technology (LH2 aviation infrastructure) develops to ultimately replace the incumbent regime (traditional kerosene-based fuel infrastructure). The process of gaining a deeper understanding of how this transition will look is guided by the transition management cycle. The methodological framework of this research followed certain steps of the transition management cycle for a specific case airport in order to explore this transition. The transition to LH2 infrastructure for Schiphol airport was addressed in order to gain in-context knowledge on the transition at hand. An extensive literature review was used in part to develop an initial infrastructure vision for Schiphol. Then, in an iterative manner, expert interviews were used to further develop an infrastructure vision which is expected to work best for the specific environment in which Schiphol finds itself. The opportunities and challenges which are apparent with this infrastructure vision were then defined. These previous findings, along with transition management theory, were used to develop a transition roadmap for Schiphol. The Schiphol-specific infrastructure vision and transition roadmap gave insight into this transition in a specific context and allowed for more general learnings to be gained on the nuances and factors influencing such a transition. The key parameters which determine how LH2 infrastructure will look at an airport along with a generalized transition roadmap were generated with this knowledge.

The way in which the LH2 infrastructural systems for LH2 supply, storage, and refueling will look for an airport are very dependent on a number of factors. Regarding the supply of LH2, an airport needs to consider its geographic proximity to a transport hub; forecasted LH2 demand; and the area, natural resources, and funds available to the airport. Storage methods will largely be based on an optimization of efficiency, required sizing, and safety. And the refueling infrastructure will likely be dependent on LH2 demand, safety considerations, and the funding available. Determining these parameters and thus determining the LH2 infrastructure vision for an airport is a central step in this transition. However, there are many other steps which need to be taken in parallel in order to develop the niche effectively. In the transition roadmap actionable steps regarding developing operational procedures, supply chains, financial sustainability, and policy are all highlighted. Then, there is also a focus on the steps to take to ensure that scaling up of this infrastructure as LH2 aviation adoption increases is addressed.

In recent years Amsterdam Airport Schiphol (AAS) has been the airport with the highest Airport Air Traffic Flow Management (ATFM) delay in Europe. EUROCONTROL delays inbound flights heading towards AAS on departure, due to reasons at or around AAS. There are two main reasons for Airport ATFM delay at AAS, Weather and Aerodrome Capacity. This study aims to find a better understanding of the operational conditions that cause ATFM Aerodrome Capacity delay, such that it can be addressed properly. This is done by applying a Bayesian Network (BN) to AAS data, that includes variables from the operational conditions at AAS. The BN shows the relationship between variables and is based on conditional probabilities, so it can show the stochastic behavior of the airport. A baseline model is created using expert knowledge and through Structure Learning (SL), additional relationships between variables are identified which result in a model that best represents the operational data. The results from the BN show that an increased chance of ATFM Aerodrome Capacity delay most often occurs in the first inbound peak of the day when the cumulative delay is still low. In these conditions, the percentage of capacity used according to the schedules is often between 75% and 100%. However, at the actual time of operation the percentage of capacity used is often above 100%, indicating that the available capacity is exceeded. Interestingly, the conditions that increase the chance of having ATFM Aerodrome Capacity delay do not increase the chance of arriving too late. Having an ATFM Aerodrome Capacity delay of 20 to 30 minutes, showed only a 35% chance of arriving with more than 15 minutes of delay. Moreover, there is a discrepancy in the strategic planning of the landing slots, and the actual arriving traffic. Additionally, airlines apply schedule buffers, that could nullify the ATFM Aerodrome Capacity delay, but are unknown to other operators in the air transportation system. More information should be shared between operators to ensure all interests are met and a safe and efficient operation can be realized.

City logistics concern solutions in package movements that face challenges in rapid growth in demand for home deliveries, congestion, and expectations from consumers for sustainable solutions. Providing last mile delivery services with drones is considered a promising solution in literature as a response to the raising challenges in urban areas. In this paper, we propose an innovative framework that allows a logistics operator to design a last mile delivery network. The Network Design Model contains two parts. First, a binary integer linear programming model is presented, allowing the operator to find the optimal location of intermediate facilities. It is a flow model that uses pre-located facilities. Secondly, a sequential framework is presented that considers Facility Assignment, Knapsack Problem, Traveling Salesman Problem, and Vehicle Assignment. The Network Design Model aims to minimize the operating costs measured in delivery duration, vehicle ground time, and number of not-delivered packages. A Monte Carlo Simulation is formulated to assess the impact risk factors, uncertainties, and variation in key network design attributes have on the performance of the network.

The concept of Urban Air Mobility (UAM) services was created mainly in response to traffic congestions. In this research we focus on UAM services such as those provided by Uber Elevate. We therefore present a framework to solve the Urban Air Mobility Problem with Time Windows (UAMP-TW) under dynamic demand, using an Adaptive Large Neighborhood Search (ALNS) algorithm. The objective of this study is to maximize the operational profit and consider customer satisfaction. Satisfaction is measured by two factors: (1) deviation from desired departure time to actual departure time and (2) deviation from nominal trip duration to actual trip duration. In our analysis we aim to determine a relationship between customers and their contribution towards profit. We address this by running simulation instances that cover three operational scenarios: a morning and evening commuter transportation case (scenarios 1 and 2) and the an occurrence of an event at a specific location (scenario 3). Multiple simulation runs indicated stability, for all three instances, due to low variation of the profit from the mean. A sensitivity analysis on the customers' time-window lengths, satisfaction factors and types concluded that customers with higher time-window lengths are more profitable since it is easier to share-rides with other users. The analysis also showed that when the satisfaction factors have a higher weight in the deviation from the departure time than the trip duration, the overall customer satisfaction is increased together with the profit and the percentage of customers who share rides. Scenario 1 has a higher rate of rebalancing empty vehicles because most requests are generated in the suburbs while the depot is located downtown. This leads to a lower vehicle deployment. In scenarios 2 and 3, most requests are generated downtown and thus more vehicles are deployed. Under dynamic demand, the algorithm has an acceptance rate of new requests of about 90% while a penalty is given to customers who cancel a ride. Analysis showed that customers are rejected if an empty vehicle has to rebalance to their location unless they are premium. In terms of the computational efficiency the algorithm is able to handle between 40-50 requests simultaneously.

As poor air quality poses a risk to human health, this study aims to estimate the impact of Schiphol airport on local concentrations of various air pollutants from January 2014 to August 2020. The pollutants of interest are nitrogen monoxide, nitrogen dioxide, ozone and particulate matter. Three separate methods were applied: a comparison of air quality measurements from different locations, an analysis about the effect of wind on ambient concentrations and finally meteorology and road and air traffic activity-based regression modelling. Based on these methods it was estimated that the airport is responsible for between 24 to 31% of local concentrations of nitrogen dioxide. During the COVID-19 situation these effects decreased by ~ 4 - 5 µg/m3, which is 17 - 21% of the average concentrations measured in the years before. For the other pollutants the results were less robust and quantitative estimates include large uncertainties.

Due to a projected shift in the majority of the world’s population towards urban areas and the existing traffic congestion in today’s cities, the possibility of using the third dimension for transportation of people and goods is being increasingly explored. The resultant congestion from an estimated demand of unmanned and Urban Air Mobility flights(UAM) in urban airspace would warrant a centralized mitigation framework. Additionally, the multi-stakeholder nature of UAM calls for a traffic mitigation approach that addresses the major concerns of UAM - safety and environmental impact. In order to address this problem, an Air Traffic Flow Management (ATFM) framework that follows a multi-path approach has been developed as a Mixed Integer Linear Programming (MILP) model in this paper. Compared to flights that follow a single fixed path, this multi-path approach is seen to show a delay cost reduction of upto 96.3%. On incorporating an impact mitigation part to the original ATFM framework, it was seen that delay cost increased by 25% compared to the multi-path model. On the other hand, the impact of traffic densities in urban airspace was seen to reduce by 43.47%. This further leads to trade-off studies between delay cost and the impact of traffic densities in airspace.