This thesis presents a renewed feasibility analysis of an aircraft employing a Propulsive Empennage (PE), focusing on the Delft University Unconventional Configuration (DUUC). Existing aerodynamic and flight mechanics models from earlier DUUC studies are improved and extended to more accurately assess the concept's viability. Two alternative PE positions - one at the tail and one near the front of the fuselage - are analyzed and benchmarked against the ATR72-600. Enhancements include an updated thrust model, revised mass and center of gravity estimations and an improved control vane model. Despite the potential benefits of ducted fans and clean wing configuration, results indicate that increased mass, center of gravity excursion and increased trim drag outweigh the propulsion gains. Ultimately, both DUUC configurations underperform relative to the benchmark, and while the PE concept introduces intriguing design possibilities, it falls short of outperforming conventional turboprop configurations in cruise condition.

The aviation industry continues to grow at a steady annual rate of approximately 4.4%, intensifying global environmental concerns in light of international climate goals. In response, airlines are under increasing pressure to adopt sustainable innovations, with electrified aviation emerging as a promising pathway. One of the main challenges in electrified aviation is planning profitable flight schedules despite long turnaround times for battery recharging. To address this, a Flight Scheduling and Electrified Aircraft Routing (FSEAR) model was developed, advancing beyond models assuming full recharging or battery swaps. It integrates partial recharging through a recursive three-dimensional time-space-energy dynamic programming framework, combining multi-label dominance on profit and energy with a CO2 tax penalty for climate optimization. Based on the KLM Cityhopper network, three case studies with varying demand and distance profiles were developed. Results show that partial recharging increases profit by 22.5% to 27.8% compared to limiting operations to full recharging constraints. Emission reductions of 45.1% and 48.9% were achieved in close-range cases, while the long-range case showed a modest increase of 4.66%, reflecting a trade-off for enabling more profitable
operations with higher flight frequency and greater demand coverage. A consistent reduction in fleet size and a shift to fully all-electric compositions were also observed. This study demonstrates that partial recharging significantly enhances both the operational efficiency and environmental performance of electrified aviation, supporting lower-emission fleet compositions and enabling a more sustainable, cost-effective alternative to regional air transport.

Urban Air Mobility requires propulsion with high power and low emissions. This thesis presents a modular tool that benchmarks three eVTOL powertrain architectures: Battery-only, fuel-cell–battery, and fuel-cell–turbogenerator, over a representative mission. Component models employ a second‐order Thevenin equivalent circuit for the lithium‐ion battery, the Amphlett static approach for the polymer‐electrolyte‐membrane fuel cell, and precomputed zero‐dimensional maps from the Gas Turbine Simulation Program for the turbogenerator. A non‐causal power‐management algorithm allocates base load to the primary source and peak demands to secondary power source. While Differential Evolution optimizes battery voltage, fuel-cell voltage, and fuel-cell power cap to minimize operational empty weight within a MTOW constraint. Results show the battery-only concept is mass-prohibitive; the fuel-cell–battery hybrid reduces battery mass yet remains overweight; and the fuel-cell–turbogenerator configuration offers the lightest solution but still marginally exceeds MTOW. Sensitivity studies indicate that doubling battery specific energy or adopting liquid-hydrogen tanks could render hybrid or fully electric eVTOLs feasible.

Green hydrogen is emerging as a viable alternative to kerosene for medium-range aviation, with airports playing a critical role in the distribution of liquid hydrogen (LH2). This study uses a simulation-based optimisation model to investigate hydrogen refuelling operations at Rotterdam The Hague Airport. Different analyses reveal that on-time performance and capacity are strongly influenced by hydrogen-flight penetration rate, refuelling-truck fleet size, and turnaround times. Operational efficiency depends on maintaining adequate fleet sizing and minimising LH2 refuelling durations, while factors like truck speed and trailer swap time have limited impact in compact airport layouts. Moderate integration by 2040 requires gradual investment, but high penetration by 2050 demands substantial infrastructure and vehicle expansion. As hydrogen usage grows, a second refuelling truck becomes necessary between 22% to 39% penetration, depending on the specific day and required refuelling time. Also, end-of-day and multiple close LH2 departures risk exceeding allowable departure time slots. Safety zone constraints and trailer capacity trade-offs further highlight the need for coordinated planning. These results underline that a successful transition to hydrogen aviation hinges on strategic fleet sizing, optimised processes, and phased infrastructure upgrades to ensure reliable and sustainable airport operations.

Long haul flights account for over half of yearly aviation carbon emissions, while only taking up 6% of the total flights. To reduce the emissions of those flights, hydrogen combustion blended wing body (BWB) aircraft are a promising new technology. A conceptual design tool for hydrogen BWB aircraft is created to evaluate the effects of the cabin design and hydrogen tank placement on the aircraft level performance of the concepts. It is found that for a 350 passenger BWB concept a narrow cabin with 45% of the fuel placed next to the cabin results in the lowest fuel burn. For a 250 passenger BWB concept a narrow cabin with 40% of the fuel next to the cabin offers the lowest fuel burn.  The spread in performance is lower for the 250 passenger concepts than for the 350 passenger concepts, indicating the internal layout has a larger effect on the performance of the 350 passenger concepts. Optimising cruise conditions resulted in high optimal altitudes due to the low wing loading of LH2 BWBs, however limitations of the model with respect to the cruise altitude reduce the certainty of conclusions drawn. Conclusions drawn with respect to the sensitivity of the performance to design parameters are limited by uncertainty in the wave drag estimation of the concepts.

Returnable Transport Items (RTIs) play a critical role in sustainable logistics by enabling asset reuse across supply chains. However, damaged RTIs must be efficiently transported to repair centers and reintegrated into the network to maintain service quality and cost-efficiency. This thesis addresses the challenge of optimizing such repair-integrated transport flows within RTI pooling systems, using Container Centralen as a real-world case study. An Integer Linear Programming (ILP) model is developed to jointly determine the optimal allocation of broken RTIs to repair centers and the subsequent redistribution of repaired items to depots, under constraints such as repair capacity, transport time, and demand fulfillment. The model is implemented in Python using Gurobi and embedded in a user-friendly dashboard for decision support. Through simulation experiments based on historical data and realistic scenarios, the integrated approach demonstrates significant improvements over heuristic strategies, including reduced transport distances, better repair center utilization, and improved service reliability. The findings highlight the practical and theoretical value of integrated logistics modeling for RTI networks and propose several avenues for future research, including the extension to multiple RTI types, incorporation of uncertainty, and scalability enhancements. This work contributes to both operations research literature and practical decision-making in circular supply chain management.

The assignment of cargo shipments to available capacity is a complex process. Shipments that vary greatly in weight, volume, and shape must be loaded into Unit Load Devices (ULDs). A slight overestimate of the required number of ULDs can be very costly in terms of delayed cargo. This paper presents a decision-support tool that provides packing strategies for the handling & operations department of a cargo airline. At regular intervals, advice is provided concerning: when to build up a ULD, what items to place in the ULD, and where to place the items within the ULD. As the model considers all flights handled by the warehouse, it is able to optimize operations on a macro-scale rather than an individual flight level. The scheduling of build-up requires accurate information on the availability time of cargo at the warehouse. to deal with uncertainty inherent to such availability, bespoke machine learning prediction models are developed which can provide prediction distributions rather than point estimates. The performance of the model is evaluated through a simulation with real-world data from historic operations of a partner airline. Particularly, the effects of the uncertainty of cargo arrival times on loading performance is investigated. The method to deal with this uncertainty is implementing staff buffer as a contingency. The tool can analyse the cost and benefit of introducing staff buffer to find the optimal amount for each scenario.

Airside cargo transportation is a critical component of the air cargo supply chain, requiring timing and coor dination to minimize operational costs and ensure high service quality for the airline transporting the cargo. The Airside Cargo Transportation Problem (ACTP) addresses the efficient routing and scheduling of cargo vehicles carrying ULDs across the airport’s service roads between the cargo terminal and passenger aircraft stands while adhering to operational and safety requirements. Key aspects of the ACTP include vehicle ca pacity utilization, differentiation between cargo commodities, and the consideration of time-dependent travel times. This paper presents a Mixed-Integer Linear Programming (MILP) formulation of the ACTP as a Set Partitioning Problem. The sets cover all possible consolidations of various cargo transportation requests on paths generated using preprocessing algorithms. The ACTP is computationally feasible by applying a rolling horizon-based heuristic. The model is applied to a case study at Amsterdam Airport Schiphol (AAS) after a potential relocation of KLM Cargo’s terminal. This relocation provides a unique opportunity to evaluate various strategies to optimize airside cargo transportation and study the implications for both KLM Cargo and AAS. Two strategies were evaluated to optimize network capacity: one focusing on spatial distribu tion and the other on time distribution by using traffic predictions. While the spatial distribution strategy degrades the overall performance, the traffic prediction strategy resulted in poor solution quality due to increased model complexity. Accordingly, it could not be proven that its results are comparable to those of other strategies. In addition, two strategies examined the impact of different cargo terminal operational concepts on cargo transportation. The pull strategy, which schedules cargo to arrive at the aircraft stand just in time for loading, improved overall airside cargo transportation performance. In contrast, the push strategy, which transports cargo immediately after processing at the cargo terminal, resulted in a decline in overall airside cargo transportation performance.

The rapid expansion of the e-grocery market has led to significant challenges in last-mile delivery, par- ticularly in managing heterogeneous fleets, accommodating multiple capacity constraints, and adhering to strict time windows. This paper introduces TOET (Tailored Optimisation for E-grocery Transport), a novel framework that addresses these issues through advanced metaheuristic techniques, dynamic hyperparameter tuning, and specialized arrival time calculations. Benchmark experiments on real-world dispatch plans demonstrate that while TOET variants substantially reduce runtime and drive time, the benchmark VROOM algorithm still excels at minimizing the number of routes, revealing a trade-off between drive time and fleet utilization. Overall, TOET shows promise for enhancing operational efficiency and sustainability in e-grocery logistics. Future work will refine arrival time strategies, improve scalability, and conduct an economic and environmental impact analysis of the trade-off between drive duration and route consolidation.

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.