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.

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.

This thesis investigates a container drayage problem involving terminals, depots, and shippers. Each terminal operates a homogeneous fleet of trucks that start and end at their respective terminals, carrying either one 40ft or two 20ft containers, and can make multiple trips within a single planning horizon. Terminals handle full containers and maintain a limited stock of empty containers, while depots provide additional empty containers. Shippers have specific time windows for service, and requests include sending and receiving empty or full containers to designated terminals, with street turns of empty containers possible. An Adaptive Large Neighborhood Search (ALNS) algorithm is implemented to address this problem, outperforming traditional methods like CPLEX. Extensive computational experiments validate the ALNS algorithm's efficacy, highlighting the logistical benefits of optimal depot placement and the impact of street turns, emphasizing the practical implications for container logistics.

Research on hangar parking is scarce and either neglects or oversimplifies towing considerations. In this paper, a novel approach is presented that integrates towing operations as a fundamental aspect of the hangar parking optimization, and uniquely focuses on minimizing towing operations. For the method, two A* adaptations, Dynamic State A* and Meta A*, are developed extending the current considerations of linear maneuverability and individual movements. These adaptations enhance maneuvering freedom and enable coordinated towing sequences, ensuring consistent resolution of obstacles. To address the combined complexity of nesting and path planning problems, a neighborhood search strategy is adopted, adhering
to a strict no-overlap placement policy to ensure consistent solution feasibility. The No-fit Raster (NFR) and Inner-fit Raster (IFR) are employed to effectively manage non-overlap and containment for irregular aircraft shapes. Parking configurations are generated through a custom Minimum Individual Utilization (MIU) placement strategy. The permissible parking orientations were extended from 2 to 8, with respect to current research. To validate the novel approach, a case study was conducted, providing empirical evidence of partial minimization being achieved. Additionally, a general trend towards towing reduction and convergence
was observed. The results were reviewed by a towing expert, confirming the method’s potential for usability within real-world operational contexts. However, two primary limitations were identified: Deadlock-Inducing Placement (DIP), which may lead to simulation failures, and ’rotational jumps’ that misleadingly appear to avoid conflicts with stationary aircraft.

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.

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 rapid development of cross-border e-commerce logistics, how to efficiently load goods into Unit Loaders (ULDs) and ensure their on-time delivery has become a key issue in logistics systems. Based on Cainiao's actual logistics challenges, this paper proposes a comprehensive loading planning scheme in two phases: the first phase solves how each item can be efficiently packed into ULDs in 3D space, and the second phase solves when each ULD can be loaded on multiple parallel workstations. This design-oriented approach fine-tunes and integrates existing optimization techniques into a cohesive pipeline to tackle these interconnected problems systematically.

To tackle the 3D Bin Packing Problem (3DBPP), two approaches, Mixed Integer Programming (MIP) and Extreme Point Heuristic (EPH), are used in this paper. The MIP model maximizes space utilization through accurate optimization and is suitable for small-scale packing scenarios, while the EPH algorithm performs well in large-scale scenarios and generates high-quality approximate solutions in a short period. Although its space utilization is slightly lower than that of MIP, its solution efficiency is well suited to real logistics operations that require fast response time.

For the Build-up Scheduling Problem (BSP), a parallel machine scheduling model is used to optimize the assembly sequence and timing of ULDs to ensure that all ULDs can be loaded within a strict time window. Experimental results show that the model performs well in optimizing workstation load balancing and avoiding delays, which can significantly improve the scheduling efficiency of the whole system.

The research results in this paper are validated to show that the proposed two-stage framework has significant application value in improving space utilization, reducing cargo delays, and optimizing workstation scheduling in real logistics scenarios of Cainiao. Future research can introduce a feedback loop between the two phases, and combine real-time data with dynamic adjustment strategies, hybrid algorithms, and other methods to further improve the adaptability and efficiency of the model.

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 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.

Baggage handling operations in airports have become substantially more challenging due to the surge in air transportation demand witnessed in the last three decades combined with an even higher pressure on the operations following the COVID-19 pandemic. The intricate nature of checked baggage requirements, impacting resource allocation and personnel scheduling, necessitates an integrated approach for problem-solving. This research paper aims to enhance baggage handling operations by predicting the baggage factor (BF) for individual outbound flights. The BF represents the ratio of checked baggage items to the number of passengers aboard an aircraft. The objective of this study is to create a forecast model that predicts baggage factors for individual outbound flights over a time span of 7, 30, and 60 days, and subsequently construct a baseline model that leverages the forecasting outputs to optimise baggage handling processes and allocate resources effectively. A novel approach is proposed that employs historical flight data within gradient boosting models to forecast the baggage factor for future flights. Additionally, a case study is conducted by developing a Mixed Integer Linear Programming (MILP) model to minimise space utilisation within a baggage handling facility during the busiest period of the day, employing as few Make-Up Areas (MUAs) as possible. The results indicate that LightGBM, a gradient boosting technique, outperforms other gradient boosting techniques in terms of performance and computation time, achieving an accuracy score for the BF prediction ranging between 78-83% for the three forecast periods. Leveraging these predictions, the MILP model demonstrates that only 3 to 5 MUAs are required in an ideal situation in the baggage handling facility during the busiest period on various days.

This research presents a comprehensive modeling approach for the flight dynamics of a hybrid compound helicopter, employing classical mechanics methods. The derived non-linear mathematical model encompasses the individual components of the aircraft, including the rotor, propellers, wings, fuselage, and empennage, which are then integrated into a unified dynamics framework through the final Equations of Motion. Notably, the model incorporates a conventional first-order steady flapping motion and quasi-dynamic inflow modeling for both the rotor and propellers. The resulting model represents a complete 9 Degree-of-Freedom system, augmented with 6 mechanical ones and an additional 3 for the inflows of the rotor and the two propellers. As an over-actuated coupled system, it offers 7 available controls; rotor collective pitch, rotor longitudinal and lateral cyclic pitch, port-side and starboard-side propeller pitch, elevator deflection, and rudder deflection. The primary objective of this study is to identify operating trim points during forward flight, ranging from hover to the observed maximum airspeed of 255 knots. Achieving trim through numerical optimization involves a control allocation process utilizing a mapping function that prioritizes the auxiliary propulsion. The research findings demonstrate the successful implementation of a slowed-rotor strategy at high speeds, effectively mitigating compressibility effects, and a linearized mathematical system is derived, providing essential insights for future stability analysis and control system design. These outcomes contribute valuable information toward the advancement of hybrid compound helicopter technology.

Air cargo is vital for efficient global supply chains, enabling the rapid transport of valuable goods. Combination airlines, a significant player in the industry, use both dedicated full freighters and the belly space of scheduled passenger flights to transport cargo. This research develops a decision tool that combination airlines can use to optimize the routing and scheduling of full freighters. By incorporating anticipated cargo demand data as an input, the tool enables the most cost-effective routing and scheduling of full freighters, also taking into account the available cargo carrying capacity offered by passenger flights. The tool combines and leverages the intrinsic advantages of passenger flights (frequencies) and full-freighters (capacity). The decision tool relies on a Mixed Integer Linear Program (MILP) to minimize operational costs while adhering to operational constraints faced by a combination airline. Although this method performs well for small instances, it suffers from long computational times when applied to larger problem instances. To address this, modifications are introduced to shorten the runtime by reducing the problem size. Specifically, clustering is used to group cargo requests together, and column generation is used to efficiently select optimal routing decisions. These modifications lead to a shorter runtime, albeit with an acceptable increase in operating costs. The decision tool serves multiple purposes. In the short term, it can be used to route full freighters and identify non-profitable full-freighter origin-destination pairs, among other applications. Additionally, it can be used to analyze, simulate, and provide recommendations for future long term full freighter fleet planning decisions.