Due to poor working conditions caused by emissions, heavy workloads, and significant staff shortages, airlines and airports are turning towards automation for a solution. While many automation developments focus on turnaround management and scheduling, research on the automated execution of turnaround operations is lacking, especially within arrival and departure operations and on the usage of multi-operation vehicles. This study aims to determine whether combining multiple operations into a single autonomous platform is operationally and financially beneficial. Task dependencies and interconnections were modelled, allowing the simulation of interactions and propagation of delays. A financial model was then developed to quantify the impact of changes in turnaround durations on delay-related costs. Based on net present value, reliability, flexibility, and technological readiness, different automated concepts developed for each operation were assessed and compared with multi-operation automated platforms. Additionally, the effects of automation on the scheduling, spatial management, and communications during aircraft turnarounds were analysed, accompanied by a risk analysis. Findings from this study indicate that the automation of arrival and departure operations can provide operational gains and positive financial returns, provided reliability performance meets the required thresholds. Multi-operation platforms enhance flexibility significantly, but may underperform in operational efficiency and financial viability. The results from this thesis provide an informed approach in automating aircraft turnarounds, supporting decision-making on automation concepts and accelerating the transition to an environment that ensures occupational health and safety for ground staff.

The maritime industry faces increasing pressure to cut greenhouse gas emissions, yet traditional ship design methods rely on static assumptions and rarely exploit the growing availability of operational data. This paper proposes a Digital Twin (DT)-aided design framework to integrate such data into early-stage ship design. The framework covers data acquisition, modeling, and verification, ensuring that operational insights inform decision-making.
A case study on bulk carrier engine room configurations demonstrates the approach. Using industrial engine datasets and operational profiles derived from Bunker Delivery Notes, a rule-based model generates feasible configurations that are assessed for fuel consumption and CO2 emissions. Results indicate that operational data enables insights into performance forecasts and more informed configuration selection compared to traditional methods.
While the application remains a digital model rather than a full twin, the study shows the potential of DT-aided frameworks to support IMO decarbonization goals and guide future ship design.

Reliable container-tracking depends on the quality of estimated time-of-arrival (ETA) data, yet existing logistics platforms offer little guidance on how trustworthy those timestamps really are. This thesis proposes a fit-for-use data-quality (DQ) framework for Digital Container Shipping Association (DCSA)-compliant event logs that flags ETA records likely to deviate from actual time of arrival (ATA) by more than one calendar day.

Event logs from $\sim$90\,k transport legs were preprocessed into records capturing origin-destination pair, carrier, publisher type, and timing information. Four supervised models, namely Linear Regression (LR), Random Forest, XGBoost, and a Neural Network, were trained to predict leg duration. A prediction that placed ATA \(>1\) day from the published ETA labeled that record \textit{low-quality}. Model outputs were evaluated with a precision-oriented \(\mathrm{F}_{\beta}\)-score, where a false alarm is 50 times more costly than a missed detection (\(\beta \approx 0.141\)).

The simplest model prevailed: standard LR achieved the highest overall \(\mathrm{F}_{0.141}\)-score (68.5 \%), balancing few false positives with robust recall, while more-complex tree-based and neural models produced excessive false alarms. When the analysis was narrowed to early-stage ETAs published by carriers (arguably the least reliable yet most operationally valuable subset) LR’s score rose to 72.0 \%. These findings highlight that careful feature engineering and data curation outweigh algorithmic complexity for this task.

The study delivers the first systematic, event-data-only method to quantify DQ in container tracking, enabling near-real-time plausibility checks without AIS feeds. Limitations include a three-month observation window and absence of exogenous factors such as weather or port congestion. Future work should extend the temporal scope, integrate AIS-derived and environmental features, and explore meta-learning techniques to adapt to disruptions. It could also use process-mining to uncover anomalous event sequences to take a different approach in dataquality assessment within container-eventlogs.

By demonstrating that a transparent LR baseline can reliably surface dubious ETAs, the thesis provides a practical blueprint for logistics platforms seeking to bolster trust in their tracking data and to prioritise corrective action where it matters most.

This research presents a novel approach for real-time stress state estimation in steel bridges using Fiber Bragg Grating (FBG) sensors and image recognition techniques. The methodology involves creating a digital model of the bridge, comprising a global finite element model (FEM) and detailed sub-models of critical areas. A database of precomputed load cases is generated, and real-time sensor data is matched to this database using the developed fingerprinting method. Image recognition is employed to detect multiple load scenarios, enhancing the accuracy of stress estimations and ensuring linear scalability for multi-load situations. The accuracy of the developed model was tested using a scaled setup using a 3 meter long aluminium bridge, proving its effectiveness in real-world conditions. The results demonstrate the feasibility of this approach, with reasonable accuracy achieved in both single and multi-load scenarios. Future work should focus on improving model accuracy, enhancing image recognition algorithms, and optimizing computational performance for large-scale applications.

Segregation is a significant factor that can affect the uniformity of a mixture. In order to address and mitigate segregation, accurate data is necessary to characterize particles throughout the process. However, obtaining data in a manner that does not impact the mixture can be challenging, particularly in non-laboratory settings where conditions may not be as controlled.
In laboratory settings, stable lighting and coloured particles can be used to aid in differentiation. However, this approach may not be feasible for all materials. Therefore, the development of a tool that can identify particles without the need for colouring or consistent lighting is highly valuable.
This study focuses on a common mixture found in blast furnaces, consisting of coke, pellet, and sinter. The similarities in colour schemes and overlapping sizes of coke and sinter present challenges for particle recognition. Unstable lighting further complicates the differentiation process.
The proposed method is applied to a mixture containing all three components to demonstrate its capabilities. This method effectively distinguishes between the various particles, providing information on both the material and area of each particle. The measurement data is utilized to assess material and size segregation within the mixture. Material segregation is evaluated using a square grid and ring configuration, while size segregation is determined by categorizing particles into three groups based on diameter, including Feret diameter and area-equivalent diameter.

This thesis presents a thorough study into the development and implementation of a control method for autonomous unmooring, trajectory tracking and mooring on an autonomous model-scale vessel.
Mooring and unmooring are vital processes in the operation of ships, as it is the system that secures and releases a ship to a terminal or multiple terminals. The process has remained relatively identical over the years, whereas autonomous shipping has been researched over time. This study addresses the lack of focus given to autonomous mooring and unmooring by offering a control strategy that leverages the vessel’s thrusters to perform these tasks, along with trajectory tracking. The study begins by reviewing existing research on trajectory tracking and maritime vessel mooring/unmooring, revealing the gaps in the integration of both procedures within autonomous operations.

To overcome these challenges, the study selects an applicable mathematical model for the vessel which will be controlled. This model covers kinematic and kinetic elements, as well as actuation and thruster allocation. This sets the groundwork for the development of a control strategy capable of precisely predicting and tracking the vessel’s position during unmooring, and trajectory tracking and mooring. Model Predictive Control (MPC) was chosen as an ideal control approach due to its ability to predict future states and effectively handle the complexities of marine operations. MPC makes it ideal for the difficulties of mooring and unmooring, where precise control is necessary to ensure safety and efficiency.

The control strategy was then developed and implemented in MATLAB/Simulink, with the approach modified to meet the model-scale vessel’s specific dynamics and operational requirements.
In addition, Key Performance Indicators (KPIs) to assess the effectiveness of the strategy were introduced. The strategy’s performance was assessed in three operational phases: unmooring, trajectory tracking, and mooring. The results show that the MPC controls the vessel’s trajectory well, with errors staying well below acceptable bounds. The largest deviation of the final trajectory during the mooring test was 9.8% of the length of the ship. A 1.0% deviation for the benchmark trajectories could also be observed. These results, in addition to others, validate the suggested strategy’s accuracy and dependability in practical situations.

In summary, this thesis provides a comprehensive control approach for autonomous unmooring, trajectory tracking and mooring for an autonomous model-scale vessel, bridging the gap between theoretical study and implementation in simulation. While the research identifies several limitations that present the potential for additional study, it also lays a solid foundation for future developments in autonomous maritime technology.

The accessibility in far offshore wind farms during unplanned maintenance is reported to be insufficient. This system largely consists of daughter craft, small vessels that increase the multitasking capabilities of an SOV. The goal of this research is to improve the accessibility to ultimately increase turbine availability, which is proven in this work to be necessary. The research is carried out in cooperation with Siemens Gamesa, a major wind turbine supplier. This research builds off the basis laid by Brans et al., who applied a needs analysis on the daughter craft system. This research applies the next step in the systems engineering sequence: concept exploration. The scope of this research extends beyond that of the daughter craft to any system that can improve the unplanned maintenance of far offshore wind farms.

Because the subject matter is relatively little covered in the scientific field, this research lays great emphasis on the context of the problem. The status quo of the sector is described in terms of equipment, operations, regulations, forms of limitations, financial context, trends, and different stages performed in unplanned maintenance. By performing an analysis of alternatives a high potential for system improvement is found.

A set of performance requirements for the accessibility system is developed to structurally assess the system and possible improvements. These performance requirements are used to determine which alternative system holds the most potential for accessibility improvement. Increasing daughter craft dimensions is chosen as the most potent alternative. A feasibility study is performed on deploying CTV-sized vessels far-offshore for two weeks thereby significantly reducing transfer time and distance. These vessels are called far-offshore transfer vessels (FOTV).
Different configurations are tested for storing the FOTVs far-offshore when not in operation and interfacing with the SOV. Two principal concepts are identified: Enlarged daughter craft, where the FOTV is stored on the SOV, and the exposed principals, where the FOTV remains in the water. One configuration of the enlarged daughter craft principal is deemed feasible: The lifting launch configuration. Two configurations of the exposed principal are deemed feasible: The connected to the SOV configuration and the moored to designated platform configuration. Furthermore, a model is constructed to assess the logistic and economic merit of different combinations of FOTV, wind farm, and configuration.

This research concludes that the deployment of a FOTV combined with a lifting launch configuration is most profitable for any wind farm. Nonetheless, the exposed principal concepts outperform the current system significantly. The choice of FOTV depends mainly on the accessibility performance and day rate of the vessel but also on the wind farm. The model predicts that in the current market, the highest-performing CTVs are the most profitable options for FOTV deployment. Finally, the results show that the effects of improving accessibility vessel performance relates inverse exponentially to profit performance.

Global supply chains currently are inefficient and unsustainable, due to their overlap and lack of interconnection. The Physical Internet (PI) proposes a paradigm shift in the way we move, store, supply, realize and use goods. The goal of the PI is to interconnect global supply chains, amongst others, on a digital level. For this, information systems are needed. Seaports, being drivers of global supply chains, therefore also need information systems on an integral level. In certain ports, Port Community Systems (PCS) serve as port’s information systems. They facilitate information exchange between stakeholders to better connect port processes. This research explores the potential role of PCS in PI ports. It does so by performing a Design Science Research (DSR). The artefact that is designed, is a conceptual information system that addresses key (PI) port functions. A case study analysis at Portbase, the Dutch PCS, has revealed what information is exchanged between which stakeholders. PI literature provides grip for PI information system requirements. The integration of the PCS analysis and the PI requirements has led to a design of an information system that shows the potential of a PCS in a PI port. The design shows that, according to the current offer of PCS services, PCS’ potential role focuses mainly on compliance related processes in PI ports. It also shows that the offer of logistics related services that are needed in PI ports, is limited. It is therefore recommended that PCS focus on an extension of logistics related services that are needed in PI ports. Future research may address the potential of PCS in a federated network of platforms.

Vessel platform automation developed notably categorizable in two behaviors: reconfiguration and collaborative motion control. This work explores integration of both behaviors in a single system. Fundamental system characteristics of both behaviors are analyzed to create understanding of the varying design approaches. A multi-vessel platform model approximation is proposed that expresses all module models in one generalized platform coordinate while retaining directional dependent effects. Automated reconfiguration and collaborative, coordinated dynamic positioning are implemented within a single framework. The multi-robot control stucture consists of guidance, navigation & control layers, rather than single systems, where topology changes with platform configuration.

An important step in determining the performance of a commercial ship is to determine the vessel fuel consumption in offdesign conditions. In previous studies, the vessel fuel consumption is obtained using machine learning algorithms which use navigational data and meteorological data to train the models. Due to the inaccuracy of the weather hindcasts, the accuracy of these machine learning techniques is not satisfactory for Compagnie Maritime Belge (CMB) which is the company for which this study is performed. Since the vessel motions are a direct result of the meteorological conditions an approach is investigated in this research to replace the meteorological data by motion data to train a machine learning algorithm and obtain accurate results. The vessel motions are captured by a motion sensor on board the vessel. Multiple machine learning techniques were tested in this research. The data used to train the best performing learning algorithm represent the motions by the first and second moment of the nonderivative motion and the second moment of the first derived motion. These moments are obtained from the motion distributions of a time window of 5 minutes. The obtained best parameters for representing the motions were obtained by testing if a spectrum must be provided, how many motion derivatives are required, which moments are needed and how large the time window must be. Although the accuracy of the developed learning algorithm is determined as satisfied no digital twin could be made and tested. Unfortunately, due to the limited amount of data available no separate algorithm could be made which uses the Extra Tree learning, as digital twin base, to determine the overconsumption of the vessel. Only data from one voyage was available. All this data was used to train the learning algorithm to have as much variance as possible in the data set such that a good learning algorithm could be developed.

This thesis describes the process of selecting different greenhouse gas emission reduction technologies for the application on a redesign of a multi-purpose heavy-lift vessel. Only technologies were considered which did not negatively influence the operability or arrangement of the redesign. The best performing combination of technologies was identified using performance indicators which measured the economic and GHG emission reduction performance. The best performing combination of GHG emission reduction technologies results in an emission reduction potential of 20.6% compared to the currently operated vessel. It is concluded from this study that when only considering technologies on a redesign of a multi-purpose heavy-lift vessel which do not negatively influence the operability or arrangement, the IMO GHG emission reduction goals cannot be met.

The current operational cost estimate applied by shipping management companies is insufficiently valid and accurate for determining the operational budget. Most shipping management companies include scheduled maintenance jobs in the operational cost calculation. Scheduled maintenance does not take unforeseen maintenance into account. In retrospect, unforeseen maintenance makes up approximately 8\% of the total operational costs. The estimation of the operational costs can be improved by including the unforeseen maintenance costs. Improved operational cost calculations lead to better substantiated maintenance policy decision-making. Therefore, the (unforeseen) maintenance costs over the lifetime of a vessel are modelled. This research focuses on maintenance cost calculations, based on failure behaviour. First, it is explained how the Maintenance Cost Model calculates and compares the costs of different maintenance policies. Thereafter, the model is validated in a case study. The achieved cost reductions resulting from the case study varied from 0\% to 70\%, with a conservative average of 16\%. Next, the case study conclusions are assessed in a sensitivity analysis. The model generates a, system specific, cost based ranking of the maintenance policies. The ranking can be used by shipping management companies, to obtain a better substantiated system specific maintenance policy. The developed model in this research is generally applicable and proves the concept. The research concludes with recommendations for future research, which includes further expansion of the Maintenance Cost Model and the possibilities to increase the likelihood of the number of replacements.

The shipping industry contributes significantly to the global greenhouse gas emissions due to the use of cheap fossil fuels. The booming cruise ship industry is forced to reduce the emitted greenhouse gases and emission of carbon dioxide to be able to access remote areas with higher restrictions and to comply with upcoming regulations. Fuel cells are a promising alternative to conventional diesel-powered systems to reduce the emissions. Next to the physical implementation, fuel cells have additional operational limiting factors, which have to be matched with the power demand. Compared to conventional combustion engines, longer start-up times and a lower dynamic response ability can be expected. Typically, the energy demand is estimated for all electric power consumers with the load balance approach in the conceptual design phase. The actual power demand is calculated based on the absorbed power and additional predefined factors for specific conditions. This conflicts with the dynamic power demand of cruise ships in various operational conditions. This thesis discusses the used bottom-up approach for such an improved prediction of the total power and energy demand of an expedition cruise vessel for the early design stages. The focus is on identifying the peak loads and load changes under consideration of the passenger behaviour, environmental and operational conditions on the basis of predefined typical days of operations. The dynamic power demand is predicted for the different propulsion, auxiliary and hotel systems, as identified and grouped in the system breakdown. This is done by using separate prediction models for the defined groups of systems and electrical components. The required energy per day is calculated directly from a time-domain integration of the power prediction. The demand for a whole operational profile is obtained by adding different typical operational days. The highest power is required by the main propulsion in sailing mode. However, expedition cruise ships often operate at lower speeds below the design conditions resulting in a considerably lower load. The maximum load of the hotel systems always appears in the morning, when all passengers are on board and several components ramp up from the lower night mode. Comparing the total power demand shows that the load changes caused by the hotel systems gets smaller in relation to the changes within the propulsion system as soon as the speed varies. In port condition, the relatively constant auxiliary load, including the hotel systems, remains and describes the baseload throughout the day. Generally, the dynamic prediction clearly shows the potential to better estimate the required power and energy, if varying operational conditions are considered. The method supports a well-founded decision on the power supply configuration, if it comes to hybrid systems including different power supply components and their operational characteristics. A multi-criteria decision analysis on the power supply side can build up on the required energy and power demand of a certain part load. The required fuel cell size can be quantified based on the estimated maximal load and the fuel tanks by considering the predicted energy demand of the specific group of systems.

The supply for luxury yachts is growing which increases the competition between yachtbuilders. Additionally to lead time reduction, yachtbuilders are trying to maximise their output with the available space. This puts extra pressure on the logistics process. Waste in the logistics process reduces the efficiency and for that must be avoided. The available data of the logistics process of yachtbuilders can be limited since it is not their core-business. The objective of this thesis is to explain how a model of the logistics process of a yachtbuilder can be made and how it can be used to improve and predict the logistics performance. This thesis focuses on improving the interior logistics process at yachtbuilder Oceanco by reducing the non-value added movements waste.

In hospitals, the duration of surgical procedures is susceptible to a number a factors resulting in varying procedure durations of the same procedure. When these procedure times deviate from the predicted surgical duration, scheduling conflicts arise, leading to patient discomfort and inefficient use of hospital resources. Considering the aging population and the subsequent growing demand for healthcare, hospitals are under constant pressure to provide cost-effective care. Indeed, this is also the case for the interventional cardiology unit, where the expected increase of the prevalence of cardiovascular disease is expected to put a strain on hospital human resources. Improving the efficiency has been an area of focus in many hospitals. In fact, advanced methods such as surgical workflow monitoring have been employed to identify points of improvement especially in procedure scheduling. Up until now, this has not been employed in cardiac catheterization laboratories in the Netherland.
The purpose of this study, therefore is identify primarily the factors influencing variability of the procedure time of procedures of the cardiac catheterization laboratory of the Reinier de Graaf Hospital in Delft and secondarily the possible sources compatible with video-based workflow monitoring for phase segmentation of the procedures.
Retrospective data obtained from DoseWise was used to analyze the procedure times and associating factors of coronary angiograms and pacemaker implantation procedures. In addition, process models of the procedures were created and activity recognition identifiers were studied through observer-based data acquisition.
Analysis showed that in coronary angiograms variations of total procedure times are mostly due to variable preparation times, whereas for pacemaker implantation the lead positioning times were the main source for variability. In addition, more experienced operators were shown to complete the procedure in a shorter time (-4.35%|p=0.007) in comparison to less experienced operator.
Instrument and equipment use were identified as possible sources for activity recognition along with the hand kinematics, however further analysis of reliability is required.
The findings in this study provide a basis for future monitoring of the workflow of these procedures using real-time data acquisition methods to improve the efficiency in cardiac catheterization laboratories.

To achieve significant reduction in greenhouse gas emissions in shipping and enable ship owners to phase out fossil fuels entirely, renewable fuels, such as ammonia, play a crucial role. Ammonia is considered a balanced solution in terms of volumetric energy density and renewable synthetic production cost compared to other renewable fuels. However, many questions still need to be answered to determine if ammonia as a fuel for ships is a feasible option. Therefore, the purpose of this project is to investigate how ammonia can be applied safely and effectively as a marine fuel. An ammonia carrier will be used to study the concept of using ammonia as a marine fuel. To study effective use of ammonia as a marine fuel the following options for power generation are investigated: the steam turbine, gas turbine, internal combustion engine (both compression and spark ignition) and fuel cells (both proton exchange membrane, alkaline and solid oxide fuel cells). The results of effective use of ammonia as a marine fuel are used to develop an ammonia fuel system diagram. This diagram is studied by means of a qualitative risk assessment to gain insight in safety of using ammonia as a marine fuel.

This project analyses the feasibility of the integration of an automation system for custom-made composite part production at the superyacht equipment manufacturer Rondal BV. An analysis of the current prepreg production is performed to understand the automation opportunities and production requirements. Nesting, kitting, protective film removal and the laminating of the plies as well as de-bulking preparation for the laminate were identified as potential automation areas. The analysis also considers the labour time and cost efficiency of the production.
Various automation techniques currently applied in other composite manufacturing industries have been considered and analysed for their feasibility to the Rondal product manufacture. The focus of the analysis was to keep the current manufacturing steps, while reducing labour hours and lead time. The proposed system has also been assessed in terms of its future advancement by considering its current state of development within the industry. Based on this analysis the most appropriate automation solution is the “Pick and Place (P&P) cell” concept. Its capabilities and flexibility recommend it as a suitable candidate to be implemented in the automated manufacture of the variety of Rondal products.
Challenges of each individual equipment within the P&P cell and their interactions are described in detail. Predictions for the automation equipment except for the film removal tool where able to be obtained through literature and interviews with experts in the industry. Due to the lack of available literature information fitting this specific application, on the protective film removal process, a large proportion of this project was dedicated to the development of this tool. In contrast to previous developments, this project uses shock cooling as a solution to detach the protective film from the prepreg.
Implementation of a P&P cell concept was further investigated by the ability of the robot to reach all equipment without affecting the work flow throughout the Rondal workshop. Regulations and other layout restrictions have been considered for the final layout proposal. In order to align the manual and the automation process effectively, common mechanized process steps have been integrated for the use of both approaches.
The effectiveness of the chosen automation system was demonstrated by analysing cost and time statistics of the on-site process, as well as observations of a computer-assisted process simulation. As a result, lead time saving of up to 5 work days per product type as well as labour time reduction of up to 50% were determined. However, the economic analysis showed that the investment into the P&P automation system is not feasible given the current volume of production. On the basis of these conclusions, recommendations have been made to Rondal, proving potential production changes that could lead to an effective integration of automation at their facilities.