Aviation is difficult to decarbonise due to its reliance on high energy-density fuels, making liquid hydrogen a promising option for reducing in-flight CO2 emissions. This study develops a profitdriven fleet development and network optimisation model for intra-European aviation (2035–2050). The problem is formulated as a rolling-horizon mixed-integer linear programme (MILP); scenario and sensitivity analyses and SHAP-based feature importance are used to interpret results. The model links fleet replacement and route allocation to technology readiness and a staged roll-out of hydrogenready airports with spatially differentiated LH2 prices. In the most favourable scenario, LH2 demand reaches 1.7 Mt/yr by 2050, corresponding to a 24% CO2 reduction relative to the no-hydrogen reference case; less favourable pathways yield lower demand due to delayed entry, operational penalties, and higher LH2 costs. Uptake forms north–south corridors and, as conditions worsen, shifts toward low-LH2-cost airports. Sensitivity results indicate that LH2 cost and operational performance dominate uptake: demand collapses once LH2 costs rise by 20–30% above the assumed price levels, and in a global sensitivity analysis, LH2 cost explains 40–50% of the variance in LH2 demand. Overall, meaningful hydrogen deployment by mid-century is conditional on coordinated progress in hydrogen cost competitiveness, operational efficiency, technology entry, and strategically sequenced airport infrastructure roll-out.

Current airport stand demand modelling approaches show great potential in accurately determining stand demand, but face challenges with high computational times. In this paper, a novel pre-solving Tabu search model for determining the stand demand for hub airports is presented. The Tabu search model is designed to be used prior to a mixed-integer linear programming model such that it reduces computational time while retaining a close-to-optimal solution. It determines a stand demand based on a flight schedule. In this research, a case study is performed on two hub airports of comparable size. When comparing the results to the optimal stand demand, the combined model setup performs well in estimating the total number of stands, with median deviations of 3-5%; however, the estimations of the number of stands per stand type could be improved further. The Tabu search model ran for up to 40 minutes, and the combined model runtime was less than an hour in 78% of runs. It can be concluded that the stand demand can be estimated with reasonable accuracy using a combination of the Tabu search model and the exact model in less than an hour. However, the quality of the results remains limited in some cases; the primary recommendations are to test the model on multiple flight schedules across more airports and to improve the initial solution setup.

Air cargo is a vital component in the worldwide supply chain and economy. In the KLM Ground Handling (GH) terminal, Unit Load Device (ULD) breakdown and shipment pickup are scheduled separately, with limited consideration of storage capacity and truck delays, which results in inefficiencies in schedules and higher storage requirements. To counteract these issues, this study proposes a scheduling model which synchronises ULD breakdown and shipment pickup and determines the effect of a truck slot confirmation system where drivers must confirm their presence some time before their scheduled slot, called the confirmation horizon. A Mixed-Integer Linear Programming (MILP) model extended from cross-docking literature is considered. Moreover, a Genetic Algorithm (GA) is developed to improve computational complexity. It is extended to a rolling horizon (RH) implementation, called the RH-GA, which can react to discovered delays. Using the baseline MILP (b-MILP) a comparison to the current scheduling method is performed. Moreover, experiments on realistic KLM GH scenarios are carried out using the RH-GA. The MILP failed to find feasible solutions within 8 hours for many small/mid-size scenarios. The GA always reached feasibility in tested scenarios and its runtime scaled linearly with scenario size. In the few scenarios which could be benchmarked, the synchronised GA outperformed the b-MILP. Moreover, introducing a non-zero confirmation horizon significantly improved the objective compared to no confirmation horizon. Across tested cases, mean peak inventory decreased by 9% and total tardiness by 2-3% relative to the 0-hour horizon case. Synchronised scheduling with a slot confirmation system can reduce storage requirements and improve on-time performance at the KLM GH terminal, indicating that further development and implementation of both the model and confirmation system may be valuable.

The decarbonization of heavy-duty road freight transport requires overcoming the dual challenge of limited driving range and insufficient charging infrastructure for battery-electric trucks (BETs). This thesis develops a nationwide bi-level optimization framework that integrates the deployment of static charging stations (SCS), electrified road systems (ERS), and truck-level battery assignment, leveraging large-scale tour-based freight data from the Netherlands. By operating explicitly at the tour level rather than the trip level, the framework captures cumulative energy feasibility across multiple linked trips, thereby providing a more realistic representation of freight operations. The model is solved using a Genetic Algorithm (GA), capable of evaluating more than 1.5 million tours and 3.5 million trips, and validated against exact MILP solutions on small instances. The results show that the optimization raises feasibility from 58% to 89.9%, reducing infeasible tours to 10.1%, while overall fitness improves by 34.7%. ERS emerges as the backbone of electrification, with 12,792 km deployed (€25.7 billion, 65.6% of CAPEX), while 251 SCS facilities (€50.2 million, <1% of CAPEX) provide low-cost redundancy at regional hubs. Battery allocation is highly heterogeneous: 25% of trucks operate on 90 kWh, 40% on 600 kWh, and the remainder on intermediate sizes, yielding an average of 357 kWh—closely aligned with ElaadNL’s benchmark of 289.5 kWh/day. This heterogeneity reduces battery CAPEX by approximately 19% compared to a uniform-capacity baseline. Operating expenditures (OPEX) are dominated by ERS charging, while penalty costs for infeasible tours remain substantial, averaging €362 per unserved tour. These findings demonstrate that nationwide electrification is feasible under a layered strategy: ERS as the long-haul backbone, SCS as regional redundancy, and heterogeneous batteries as cost optimizers. The study advances the literature by moving from trip-level to tour-level modelling at unprecedented scale, explicitly quantifying infeasibility, and decomposing system costs into CAPEX, OPEX, and penalties. The results provide actionable insights for policymakers and industry stakeholders seeking cost-effective and operationally viable pathways for freight decarbonization in the Netherlands and beyond.

Delay propagation is a significant driver of flight delay in aviation networks, yet modelling it at a network-wide scale remains challenging. This study investigates to what extent scheduled max-plus linear systems, as used in railway delay modelling, can be applied to aviation networks. Using the Hawaiian Airlines network as a case study, a methodology is developed to model aircraft rotation and passenger transfer precedence relations within a max-plus linear system. The approach enables the calculation of stability indicators such as maximum cycle mean, recovery times, and network slack, as well as the simulation of delay propagation under various initial delay scenarios.

Results show that the recovery matrix is a valuable tool for identifying structurally vulnerable parts of the network and for assessing the impact of holding aircraft for transferring passengers. However, predictive accuracy of delay propagation for individual flights is limited, primarily due to uncertainties in process time estimation and incomplete knowledge of precedence relations. The 24-hour periodicity of aviation timetables, combined with large overnight buffers, further limits multi-day delay propagation modelling. These limitations are partly specific to the case under study and partly inherent to the deterministic, periodic structure of scheduled max-plus systems.

The study concludes that max-plus linear systems can provide meaningful insights into structural robustness and the systemic impact of schedule design choices, but their use for precise short-term delay prediction in aviation is constrained without high-quality operational data. Future work should explore integration of stochastic max-plus models, application to networks with shorter periodicity, and validation using airline-provided operational datasets.

Online grocery delivery has seen accelerated growth in the last decade. This growth introduced new challenges in operations, such as damaged groceries upon delivery. Companies have started collecting relevant data, yet operations remain largely disconnected from the data. Motivated by this gap in the industry, we formulated the main research question of this study: "How to improve quality of service by integrating customer feedback in a bin-packing model in the context of online groceries?". A review of recent literature revealed two complementary gaps: limited applications of machine learning in offline one-dimensional bin packing where ML directly assists with the packing process, and the lack of customer feedback integration in bin packing models to improve quality of service.

Therefore, we propose a novel two-part theoretical framework to process historical data of customer damage reports into quantifiable parameters that can be used by a bin packing model. In the first part, we formulate a predictive task and propose a classification machine learning model which provides a probability of damage for the bag, given a set of bag characteristics obtained from customer data. In the second part, we propose to integrate the machine learning component into a bin packing model as a weighted term in its objective function. This integrated model comprises our damage-aware bin packing model.

The proposed methodology was implemented and evaluated through a case study using real data from the daily operations of the online supermarket Picnic. As part of the experiments, we analysed over 60 million articles across 2.2 million deliveries. We started with a comprehensive data analysis to explore the relationships in the data and identify trends. We, then, developed and trained two machine learning variants, a logistic regression and an ensemble extreme gradient boost model (XGBoost) to fulfil the predicting task of bag-damage probability estimation. We applied random undersampling to the training dataset to mitigate the extreme class imbalance (0.41% damage rate). Then, we used the logistic regression model as the ML component and implemented a damage-aware bin packing model. We defined strategic empirical metrics to measure its performance and constructed an evaluation framework using counterfactual analysis on 6000 representative deliveries.

Our findings validate the technical feasibility of integrating customer feedback data into a bin packing algorithm. The damage-aware variant recorded a 14.1% relative reduction in the average probability of damage across all bags of the deliveries tested. On the other hand, extreme class imbalance severely limited the performance of the ML models trained (1% precision score in real operational conditions). As a result, a meaningful review of the economic impact of the model is not possible. The experiments illustrated sensible item movements across the bags tested, measured with some empirical key risk parameters, such as item categories, packaging types, and bag density. The implementation showed a tolerable computational overhead of around 18%, indicating that it is realistic to deploy an efficient model to real operations.

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.

Conventional food delivery methods are slow, expensive, and often unreliable. This project aims to develop a fleet of drones for pizza delivery in a Delft-like urban environment as an innovative and sustainable solution to this problem. The focus of the project is twofold — to design a drone capable of carrying and developing pizzas, and to develop an algorithm to plan out the operations of a fleet of drones.

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.

This thesis investigates the use of optimization techniques to determine the value of multimodal (Air+Rail) networks. By using Mixed Integer Linear Programming (MILP), the mathematical model derived especially for this use case determines the optimal way for airlines to route their passengers on ultra-short haul networks. The model does not only consider operational costs, but also addresses the importance of sustainability and the value of time, and includes the value to be found in capturing passengers at cities where there is no airport in the near vicinity. This research demonstrates that by (partially) routing passengers on rail networks, rather than air, flights can be reduced on the short haul network. This results in higher profit, shorter average travel times and reduced average emissions, all whilst capturing a larger market. This research contributes to the knowledge on multimodal air+rail transport by showcasing the potential benefits it can have for airline alliances in a quantitative way, incentivizing airlines to shift towards rail partnerships for their ultra-shorthaul network and adopting more sustainable practices.

This study addresses the initial phase of a multi-modal air cargo transport network, where trucks collect shipments from multiple origins and deliver them to the hub airport of an airline. Efficient coordination between ground transport and outbound flights is crucial for optimising truck load factors, reducing operational costs, and ensuring on-time cargo transfers at the hub. Poor synchronisation can cause delays and increased expenses, reducing the efficiency of the entire transport network. This paper presents a novel Mixed Integer Linear Programming (MILP) formulation and an Adaptive Large Neighbourhood Search (ALNS) framework for an integrated vehicle routing and dock-door scheduling problem that includes split delivery, incompatible products, time windows, and open routes, with the objective of minimising operational costs. The ALNS framework uses a dock-door-based route representation along with multiple insertion and removal operators to improve the solution to the problem at hand. A comparative analysis between the MILP and ALNS model shows that the ALNS model consistently outperforms the MILP model in computational efficiency and solution quality for larger and more complex instances. The ALNS model efficiently finds feasible solutions within significantly reduced computational times, making it practical for real-world applications. Moreover, using a case study of an airline, the ALNS-generated network demonstrates improvements in cost efficiency, fleet utilisation, and truck load factors compared to the airline’s historical routing data. Despite differences between the actual network data and the model-generated data, stemming from assumptions that create an idealised scenario that does not fully capture the complexities of real-world operations, the ALNS model offers significant enhancements in efficiency for the airline’s trucking network.

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.

This research investigates how the obstacles to hydrogen adaptation impact the projected distribution and frequency of hydrogen-powered flights in Europe by 2050. The prioritized obstacles in this research are economic constraints and airport capacity limitations. For the economic constraints two different cost scenarios are analysed, one where hydrogen-aircraft just become competitive with respect to conventional aircraft, and one where hydrogen-aircraft are favoured with respect to conventional aircraft. When considering airport capacity limi- tations, for example due to availability of green hydrogen and infrastructure modifications, a constraint is put on the maximum amount of hydrogen-powered aircraft allowed in the network. In this research, a European Hub & Spoke network for an airline is analysed. In the scenario where hydrogen-powered aircraft are favoured, the variable cost of conventional aircraft is significantly increased in the future. Then, more hydrogen-powered aircraft are de- ployed and these are particularly medium-range hydrogen-powered aircraft. Moreover, under different traffic growth scenarios, the higher the traffic growth, the more routes are flown by hydrogen-powered aircraft. When comparing these two results, the varying of the variable costs of future aircraft is more sensitive to the deployment of hydrogen-powered aircraft than the sensitivity of the traffic growth. When considering the implementation of a fleet constraint for hydrogen-powered aircraft, only in a scenario with high traffic growth from 2025-2050 and a favorable cost for hydrogen-powered aircraft, the capacity constraint is met. Across all scenar- ios, despite varying conditions, the airline’s profit remains reasonably consistent and almost all demand is captured. This study emphasizes that hydrogen-powered aircraft adaptation is highly sensitive to cost dynamics. At policy level regulatory entities should implement mechanisms that create financial incentives for hydrogen adoption and Original Equipment Manufacturers should prioritize cost-efficient design.

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.

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.

Decarbonizing the aviation industry has become a major focus of attention, which still is 99.7% reliant on fossil-based fuel. A promising solution is the production of Sustainable Aviation Fuel (SAF) from secondary biomass feedstocks such as forestry residues. For the Netherlands, studies estimate a substantial supply of forestry residues for bioenergy purposes by 2050. This study addresses the question under which conditions SAF production from forestry residues can become economically feasible in the Dutch context. Thermochemical pathways including Gasification Fischer-Tropsch (GFT) and Hydrothermal Liquefaction (HTL), followed by dedicated SAF refinery processes, offer potential conversion routes. However, a comparative techno-economic analysis for this context is lacking, and existing studies often neglect the effects of technological learning and CO2 capture and storage on performance outcomes. This study evaluates feasibility by comparing the techno-economic performance of GFT and HTL. Process designs for both pathways are developed and these are side-by-side assessed on technical performance, cost structure, sensitivity to key parameters, and two potential scenarios. Results show that HTL achieves 47% higher SAF yields (18.3%) and a lower minimum fuel selling price (MFSP) of €1.03/L compared to GFT (10.2% yield and €1.49/L MFSP). Nonetheless, under the base scenario both pathways remain uncompetitive with fossil kerosene (€0.50/L). However, under the optimum progressive scenario, HTL achieves competitiveness with an MFSP of €0.30/L, while GFT reaches €0.50/L, positioning HTL as the more promising pathway in the Dutch context. However, supportive policy frameworks are needed to accelerate deployment of these technologies.

This work presents a heuristic framework for value-driven three-dimensional bin packing for air cargo operation under practical constraints such as batch arrivals, box fragility, and stability requirements. The method builds on the extreme-point placement heuristic, enhanced with a two-stage box selection strategy that first filters boxes by value density and then sorts them with attention to geometric alignment. Stability checks including supporting surface area, corner support, and padding tolerance are introduced to ensure realistic placements, along with a dedicated procedure for safely placing fragile items. A batch-handling mechanism allows bins to be packed progressively as boxes arrive, using volume-based thresholds to lock or release partially filled bins. Performance is evaluated using several types of box sets that differ in shape regularity and distribution. Results show that relaxing stability conditions improves both value and volume metrics, and that sorting strategies which consider box dimensions outperform value-only sorting. The framework also highlights the limitations of packing overly uniform boxes and the quality of solutions decreased as the number of bins increases. The method aims to balance space usage and value considerations while accounting for constraints commonly found in logistics settings.

Disruptions in airline operations significantly decrease passenger satisfaction, and have a large influence on the profits of airlines, accounting for an estimated cost of 5 to 10% of yearly revenue. At the same time, disruptions are inevitable and difficult to predict, so should be solved as efficiently as possible when they occur. Solving disruptions and restoring the network to a feasible state requires options for Operations Control of an airline. Options include reserve aircraft and crew, Free Fleet Space (FFS), or swapping opportunities. When designing the timetable, these recovery options should be built in the schedule, and this research aims to quantify the number of reserve aircraft, and the FFS required for robust operations. This was done by building a tool that simulates the effects of extra reserves end FFS. Additional FFS and reserves at the hub airport decreases the total delay times in case disruptions occur at the hub. There is no notable effect of FFS and reserves at outstations. Other airline Key Performance Indicators (KPI’s) show little improvement in this research.

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.