During offshore monopile installation, the crane boom must be lowered onto its boom-rest while the installation vessel remains afloat. This final landing operation is highly sensitive to wave- and wind-induced disturbances, which can excite oscillatory motion of the crane boom about the slewing axis. In severe cases, the disturbances may excite resonant behaviour, leading to uncontrolled boom oscillations. These uncontrolled oscillations compromise the reliability and efficiency of monopile installation, as well as the operational safety of vessel crew members. Boom landing has historically relied on slow manoeuvring and the skill of the crane operator. However, as offshore cranes continue to grow in scale, maintaining safe and reliable operation through manual control alone becomes increasingly demanding under harsh and unpredictable environmental conditions. This thesis investigates whether such oscillations can instead be actively mitigated using the existing slew drive infrastructure, without requiring additional hardware, structural modification or additional manual interference.

In this thesis, a control-oriented dynamic model of the coupled slew drive, slewing platform, and crane boom is developed using the Euler-Lagrange method, capturing the dominant structural flexibility of the boom while assuming small angular deflections. The model is extended with physically derived disturbance inputs representing first- and second-order wave-induced loading and wind loading, based on representative North Sea sea states. Based on this model, a sliding mode controller is designed, in which the sliding surface is formulated to mitigate both the oscillatory and biased components of the boom motion. The switching control component is derived using a physically motivated bound on the combined offshore disturbances, and the resulting design is validated through a Lyapunov-based stability analysis. The controller is implemented and tuned through a combination of theoretical control authority analysis and systematic simulation-based parameter identification. The resulting design is subsequently validated against a realistic lower-level slew drive motor model in Simulink, confirming that the demanded performance is physically possible within the slew drive motor’s bandwidth, speed and torque limitations.

The proposed sliding mode controller substantially reduces slewing-axis oscillations across all evaluated operating conditions. Under nominal offshore operating conditions, representative of typical sea states encountered during monopile installation, the controller reduces the peak boom tip displacement from 0.65 m in open loop to 0.13 m (80.0%). The RMS displacement is reduced from 0.219 m to 0.045 m (79.5%), while the steady-state bias displacement is reduced by 49.3%. Under harmonic excitation at the boom’s natural frequency, the controller achieves its strongest performance, reducing the peak displacement by 94.9%. In all evaluated scenarios, the slew drive motor tracking ratio remains above the required 95% threshold, confirming that the commanded motor speeds are physically realizable by the existing slew drive.

The controller is benchmarked against an existing pole placement controller developed in collaboration with Huisman Equipment. The comparison shows that the sliding mode controller achieves higher oscillation reduction across all evaluated scenarios and excitation frequencies. Compared with the pole placement controller, the sliding mode controller is especially effective at rejecting bias disturbances. However, the pole placement controller requires less control effort and has a more intuitive tuning process and controller structure. Frequency response and sensitivity analyses for variations in the model parameters show that the sliding mode controller is more robust to bounded disturbances and modelling uncertainties.

These results demonstrate that slewing-axis oscillations during crane boom landing can be substantially and reliably reduced through active slew drive compensation by sliding mode control, offering a viable path toward safer, more reliable, more efficient, and less weather-dependent offshore crane boom landing operations. ... Read more

Dynamic Positioning systems are used to keep a vessel at a desired position and heading while it is exposed to environmental disturbances. In this thesis, the potential of adding predicted wave-induced disturbances to a Model Predictive Control framework is investigated. The main idea is that the controller can react more anticipatively to wave loads, instead of only reacting after the vessel has already moved away from the reference position.

A numerical simulation framework was developed for a model-scale vessel in 3 Degrees of Freedom. The vessel motions in surge, sway and yaw were included, together with a thrust allocation system and an MPC-based controller. Irregular waves were generated using a wave spectrum, while the hydrodynamic wave loads were calculated using Response Amplitude Operators and Quadratic Transfer Functions from Nemoh. First-order wave forces were used to describe the oscillatory wave-frequency excitation. The second-order wave loads were used to determine the mean wave drift force, which was included as predicted disturbance in the wave-aware MPC.

The wave-aware MPC was compared with a baseline MPC without wave prediction. The controller performance was evaluated using Mean Absolute Error, Root Mean Square Error, 95th percentile error and energy consumption. A seed analysis was also performed to investigate the influence of different irregular wave realizations.

The results show that the predicted mean second-order wave force can improve the mean station-keeping offset. In the seed analysis, the mean horizontal offset was reduced by approximately 26\% for the tested moderate and heavier sea states. This shows that the controller is able to use the predicted wave drift force for compensating the slowly varying drift component. However, the total station-keeping performance did not improve robustly. The total RMSE remained almost unchanged in the moderate sea state and became worse in the heavier sea state. The oscillatory part of the motion also increased on average.

It is therefore concluded that predicted wave-induced disturbances can improve Dynamic Positioning performance, but only to a limited and specific extent in the current implementation. The predicted mean wave drift force is useful for reducing the mean offset, but it is not sufficient to improve the complete vessel motion around the reference position. The developed wave-aware MPC should therefore be seen as a promising first step, but not yet as a generally better controller than the baseline MPC. ... Read more

Driven by the need to improve safety, the maritime industry is undergoing a transformative change with the development of autonomous surface vessels, which can aim to reduce the likelihood of accidents by incorporating risk awareness directly into motion planning and control to account for uncertainties in maritime operations. However, existing literature does not consider risk-aware control capabilities that can enable fail-safe actions. To address this limitation, this work proposes a Multi-Trajectory (MT) Risk-Aware Model Predictive Control (MPC) scheme designed to simultaneously optimize two distinct trajectories: a mission-driven nominal trajectory and a fail-safe contingency trajectory. The fail-safe trajectory is computed with a risk cost term in the MPC objective function to guide the vessel towards minimum-risk conditions. In addition, a terminal velocity cost term ensures that the velocity of the vessel can be reduced at each optimization step, to bring it to a near-stop at the end of the fail-safe trajectory. By enforcing a shared initial control input constraint, the system reduces the possibility that the nominal trajectory leads to a state from which an escape is impossible. A supervisory system monitors these trajectories, evaluating them for predicted grounding, the physical capability to bring the vessel to a stop and if the risk is predicted to increase along the nominal trajectory. If a hazardous situation which requires a fail-safe action is detected, defined as a predicted grounding event or a loss of stopping capability while risk is predicted to increase, the supervisory system activates the fail-safe action. The fail-safe action is activated by using the binary variable (B), which accordingly modifies the MT MPC objective function. Verification is performed in a simulation environment by comparing the MT scheme against a Single-Trajectory (ST) MPC scheme during no-fault and stuck rudder and loss of effectiveness fault scenarios in conjunction with drifting forces due to wind. Results demonstrate that the MT scheme is able to able to reduce the likelihood of grounding compared to the ST scheme. Finally, this enhanced safety imposes no penalty on travel time, energy consumption, or path-tracking accuracy during scenarios that do not require a fail-safe action. The only significant trade-off is a two- to three-fold increase in average solver time compared to the ST scheme. ... Read more

Crew transfer vessel (CTV) transits to offshore wind farms expose technicians to sustained vessel motions that can impair readiness and compromise safety upon arrival. Despite this, existing operational planning frameworks rely on threshold rules applied to bulk metocean parameters such as significant wave height, which contain no mechanism to assess crew physiological state. Analysis of an industry database comprising 27,465 CTV transits across multiple offshore wind farms shows that 14.8% of recorded transits exceeded the 20% Motion Sickness Incidence (MSI) threshold despite having been approved under conventional workability criteria.

This thesis develops and validates a machine learning model for predicting MSI for CTV crews from sea-state information. The full two-dimensional directional wave energy spectrum is used as the primary environmental input, combined with vessel heading, transit duration, speed, and a vessel identifier. Among seven candidate model families evaluated through repeated cross-validation and multi-criteria decision analysis, Histogram-Based Gradient Boosting (HistGB) emerged as robustly dominant, achieving an RMSE of 3.062% and $R^2 = 0.790$ on a held-out test set covering a full annual cycle. Grouped permutation importance analysis confirms that the model relies on its inputs in a physically defensible way: frequency importance concentrates near the ISO~2631-1 $W_f$ sensitivity peak at 0.16,Hz, while energetically dominant low-frequency swell carries near-zero importance.

Cross-site transfer to a second North Sea offshore wind farm required site-specific retraining but no hyperparameter re-optimization, recovering regression and classification performance comparable to the development site. Operational validation embedded the MSI predictor as a constraint within a conjunctive feasibility framework alongside a physics-based slip probability model. On the 37 days the framework predicted infeasibility but operations proceeded regardless, crews experienced measured MSI significantly above the operational threshold (median 26% versus 13% on model-cleared days; Mann-Whitney $p = 2.01 \times 10^{-15}$), with the minimum recorded MSI exceeding the threshold on 83.8% of those days. Within the studied setting, adding the MSI constraint reduced trip-level accessibility by 8.8 percentage points relative to a safety-only baseline and eliminated 17 full operational days over the 182-day simulation period, while conventional significant wave height criteria overestimated accessibility by 16 percentage points relative to this human-centric reference. These findings demonstrate that a data-driven MSI predictor trained on spectrally resolved metocean input can function as a formally consistent and empirically grounded operational constraint within a broader feasibility framework, capturing a human-centric dimension of workability that is structurally absent from existing planning frameworks.
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Industry 4.0 technologies have the potential to transform logistics by enabling smarter, more autonomous warehouse systems. However, Shuttle-Based Storage and Retrieval Systems (SBSRS) still rely largely on centralized or hierarchical control architectures, limiting their adaptability, scalability and responsiveness. While existing research has focused on optimizing control policies within these conventional frameworks, the architectural layer itself remains underexplored. This study investigates the impact of control architectures on SBSRS performance by comparing a conventional hierarchical model with a hybrid control architecture that introduces local autonomy and inter-shuttle communication. A hybrid Discrete-Event and Agent-Based Simulation model was developed to evaluate both architectures, using a tier-captive SBSRS inspired by Vanderlande’s ADAPTO system. System Performance was assessed under varying system sizes and shuttle densities using throughput, normalized efficiency, scalability and robustness as key performance indicators. The results show that the hybrid architecture consistently outperforms the centralized approach in throughput, especially in large scale system, with up to 36\% throughput gain, up to 68\% efficiency gain (normalized throughput), improved scalability and reduced variability. These findings suggest that hybrid control offers a promising path for designing adaptive, Industry 4.0-ready SBSRS. The study contributes a conceptual design and simulation-based comparison to guide future control architecture development. ... Read more

This research addresses the need for advanced perception systems in offshore crane operations, driven by the global shift towards sustainable energy and the increasing complexity of offshore wind farms. Offshore crane operations are essential for the installation and maintenance of these structures, and currently rely heavily on the subjective judgement of operators. This dependence contributes significantly to human error, with approximately 60-80% of accidents linked to poor situational awareness and misperception. Huisman Equipment identified the potential for enhancing safety and operational efficiency by employing advanced sensing technologies, such as 3D-Light Detection and Ranging (LiDAR) systems.

A comprehensive review of existing technologies and methodologies for load detection and tracking highlighted the advantages of LiDAR sensors over cameras, radar, and radio-based sensors. Particularly in offshore environments, where visibility is often compromised, LiDAR’s ability to produce high-resolution, three-dimensional point cloud data, coupled with the stability of a fixed-mounted setup, ensures reliable and consistent monitoring of loads during crane operations.

To achieve accurate load detection using a single LiDAR sensor, the study incorporated techniques such as Statistical Outlier Removal (SOR) and voxel grid downsampling for effective data preprocessing. RANSAC and HDBSCAN were employed for robust background removal and clustering, respectively. These methods were seamlessly integrated into the detection architecture, demonstrating reliable performance against key performance indicators (KPIs) and achieving high accuracy under varying conditions.

The research identified suitable motion models for reliably tracking the movement of detected loads, including a constant turn model for horizontal motion and a constant velocity model for vertical motion. These models were integrated with the Unscented Kalman Filter (UKF) for tracking and the Joint Probabilistic Data Association Filter (JPDAF) for data association. Experimental results confirmed the system’s ability to accurately track load positions, maintain precision, and distinguish loads from clutter in dynamic offshore scenarios.

An experimental setup was designed to replicate real-world conditions and collect data to develop and test the proposed LiDAR-based methods. The setup, simulating the operational environment of a vessel-mounted LiDAR system, provided diverse datasets essential for validating the detection and tracking methods. Testing confirmed that the setup effectively replicated offshore conditions, supporting the refinement and evaluation of the system. The developed methods were rigorously evaluated for their accuracy, reliability, and performance in simulated offshore crane operations. The detection system consistently achieved accuracy exceeding 70% in high-performance scenarios, with effective clustering indices surpassing minimum thresholds. Tracking results demonstrated reliable load identification and positional precision, despite minor systematic errors. The methods proved robust and reliable, addressing key challenges in offshore environments.

In conclusion, this study successfully developed and validated a fixed-mounted LiDAR system for load detection and tracking in offshore crane operations. The research provides a practical, technology driven solution to improve safety and efficiency, while future work should explore enhancing the system’s capabilities under adverse weather conditions and integrating additional sensor technologies to further advance situational awareness. ... Read more

Autonomous terminal tractors (ATTs) are a current development as a driver-less alternative to terminal tractors (TTs). This paper focuses on the integration of these ATTs in a rubbertired gantry (RTG)-based container terminal, and more specifically looking at how strategies for ATT-only intersections could influence the productivity of the terminal. For this study, a discrete-event simulation model of an ATT, ATT-controller and intersection management system (IMS) were designed. Within the IMS, four intersection strategies were developed: One-Vehicle-at-aTime First-Eligible-First-Serve (One-FEFS), Multiple-Vehicles-at-a-Time First-Eligible-First-Serve (Multi-FEFS), Quay-Crane Destination Priority (QC-Prio) and Priority for Delayed ATTs with QC Destination (Delay-Prio). These intersection strategies were tested in a peak-load and off-peak-load scenario, on a model of an RTG-based terminal, with configurations of varying fleet sizes. Here was found that the intersection strategies had a small influence on the QC productivity in the peak-load scenario, and a negligible influence in the off-peak-load scenario. Utilising either the Multi-FEFS or the QC-Prio strategy leaded to the highest terminal performance. In the simulation model, the ATT performed significantly worse than the existing non-autonomous TT, in every configuration. ... Read more

The adaptation of battery-powered terminal trucks (BTTs) in rubber tired gantry crane terminals (RTG) will affect terminal productivity and costs. This research tests the performance of different charging strategies and compares costs. This is done with the use of a discrete-event simulation model including the energy consumption and charging of BTTs. Five charging strategies are presented: Out of Operations (OOP), Centralised Fixed Threshold (CFT), Decentralised Fixed Threshold (DFT), Decentralised Pre-Emptive (DPE) and Decentralised Opportunity Charging (DOP). The scenarios were tested in a terminal operations model of an RTG terminal, with configurations varying in charge lane location and numbers. Based on productivityOOP and DOP performed similarly to a diesel-benchmark of the same fleet size. It was found that charge location has little effect on overall terminal performance when the charge duration is large. Pre-Emptive charging has a positive effect on terminal productivity through high charge lane utilisation and little time loss associated with waiting on available chargers. Due to lower energy and maintenance costs, all battery-powered alternatives are more cost-effective than diesel-powered alternatives. ... Read more

Abstract :A model has been developed for the health assessment of a chlorine compressor that is capable of constructing Health Indicators (HI). This model is an LSTM Autoencoder, which works as follows: an LSTM-based encoder maps a multivariate input sequence to a fixed-dimensional vector representation. The decoder, another LSTM network, uses this vector representation to produce the target sequence. The loss between the reconstructed sequence and the input sequence forms the basis for the HI. The model is trained exclusively with healthy data. The LSTM-AE is designed to reconstruct the measurements independently of the operating conditions, enhancing the model’s robustness in varying operational contexts. Health assessment is conducted by using the constructed HI to classify the health state of the compressor. The first instance of the compressor being marked as unhealthy triggers the start of fault identification. The proposed method is tested on real-world data through a case study on chlorine compressors, resulting in an identification accuracy of 73% and a precision of 56% for the three considered failure mechanisms. ... Read more

Accurate forecasting in Reverse Supply Chain (RSC) management is crucial for the semiconductor industry, particularly for companies like ASML, which must efficiently manage the return flow of defective machinery parts. This study addresses key gaps by developing and evaluating time series-based forecasting models tailored to ASML's RSC. Using a modified ABC-analysis, parts were categorized based on defect frequency and economic impact, focusing on the most critical components. The research applied and optimized models including SES, ARIMA, ARIMAX, and LSTM, using five years of historical defect data. The analysis showed that LSTM models excel in high-frequency (weekly) forecasts for parts with frequent early-life defects, achieving an average mMAPE of 26.32\%. ARIMAX models performed best for lower-frequency (monthly) data, particularly in sparsely represented classifications, with mMAPE as low as 4.31\% to 10.80\%. Despite a higher mMAPE of 85.42\% in one outlier, ARIMAX emerged as the most balanced model, offering a practical trade-off between accuracy and computational efficiency. Furthermore, the study highlights the computational efficiency of ARIMAX, which, although more demanding than SES, provided a favorable balance, with ARIMA and LSTM being more resource-intensive. These findings demonstrate ARIMAX's suitability for long-term forecasting and broader trend analysis, making it the preferred model for ASML's RSC. This research provides a robust, data-driven framework that enhances inventory management and capacity planning, making it possible to predict return flows with low error rates on a monthly basis, particularly in high-tech industries where many defective parts are returned. Future research should incorporate additional dynamic variables, explore hybrid models, and refine data splitting techniques to further improve predictive accuracy and support sustainable supply chain operations. ... Read more

This thesis presents a COLREG-compliant collision avoidance and detection algorithm for the safe navigation of autonomous surface vessels. The method builds upon the Velocity Obstacle algorithm and implements the concept of the ship domain and the ship arena into the framework. The International Regulations for Preventing Collision at Sea – the COLREGs – are analyzed individually and incorporated in the designed collision avoidance algorithm. The COLREGs were used to determine the preferred passing side and optimized motion for every possible encounter situation with an obstacle vessel. The ship domain and arena help to perform proactive alterations when required, improving on the apparent shortcomings of the VO algorithm. This framework leads to a more proactive collision avoidance algorithm that guarantees COLREG compliance and safe navigation in mixed-traffic environments. The robustness and wide adaptability of the developed algorithm are validated in different encounter situations, ranging from vessel-to-vessel batch simulations to complex, multi-vessel scenarios. ... Read more

With fluctuating market demands, the tugboat industry confronts the challenge of adapting its supply chain to meet customer customisation needs while managing low order predictability. This study examines the shift from a Make-to-Stock (MTS) to three alternative production configurations based on the Assemble-to-Order (ATO) and Make-to-Order (MTO) configuration, focusing on the tugboat industry’s need for flexibility in response to market changes and customer-specific requirements.

The core issue addressed is the trade-off between investment risk and customer satisfaction, as the proposed configurations increase delivery lead times. To quantify the costs associated with adapting delivery lead times, a Bill of Materials and Operations (BOMO) is utilised, combining the Bill of Materials (BOM) with the production sequence (Bill of Operations, BOO).

A mathematical algorithm is developed to calculate the financial effects of the configurations based on BOMO data. The model involves a three-step process: importing part data, merging BOM, BOO, supplier, and transport data into a BOMO dataset, and performing value analysis on the BOMO data to quantify risk exposure and financing costs over time.

The study’s findings indicate that while increasing delivery lead times, the MTO configuration significantly reduces the risk exposure and financing costs. The BOMO is a strategic tool for analysing material costs and delivery lead times, providing insights into the financial implications of different production strategies. The research concludes that the MTO configuration is viable for Damen’s tugboat production, balancing risk exposure, financing costs, and delivery lead times. ... Read more

Purpose - With the short term need to reduce direct CO2 emissions of trucks in distribution processes, this paper aims to provide an easy to implement solution approach for distribution processes of new cars from holistic perspective. Scarce emphasizes is provided on short-term alternatives in truck-based distribution processes and approaches lacks applicable for large scale problems including split delivery function.
Design/methodology/approach - The distribution process and model methods are analyzed using a literature study and interviews with experts, resulting in the development of a solution approach. Combined with an extensive field research, a solution approach enables the performance evaluation of the current state, and the policy implications. Future designs are used to validate the solution approach by calculating performance differences in multiple relevant evaluation domains.
Findings - The analysis of the current state has identified critical bottlenecks, leading to the development of two promising policies. The application of a new and validated prioritization strategy and permitting more stops per truck has successfully yielded a significant reduction in CO2 emissions. The performance of the solution approach demonstrates high precision on a small scale and yields results comparable to actual practices on a larger scale, suggesting the approach's effectiveness and potential for future application.
Research limitations/implications - This research provides a new solution approach for evaluating direct CO2 emissions of model different designs of distribution processes. Despite its narrow scope, the transportation sector has a significant environmental footprint, and offers the potential for substantial reductions in emissions. From modeling perspective, further research is suggested in integrating split delivery function without using dummy variables.
Originality/value - This paper contributes by identifying critical gaps in the understanding and implementation of system-wide efficient car distribution processes from distribution hubs to car dealers. It not only addresses potential improvements, but also proved efficiency gains of the system with a new solution approach, using a new combination of a state-of-the-art meta-heuristic and a proven split delivery method applicable for large-scale problems. ... Read more

Facing the critical challenge of reducing greenhouse gas (GHG) emissions in the maritime industry, this thesis explores the potential of smart control systems using Reinforcement Learning (RL) for autonomous sailing. Traditional controls for sailing fall short in navigating the complex, dynamic conditions of maritime environments. RL has shown to be effective for continuous control applications in these types of conditions, however, primarily in simulated environments. Therefore, this study aims to show the potential of RL for autonomous sailing control (ASC) by means of a small scale project. A fast-time simulation of an Optimist is used to train the sailing controls required to reach an upwind target. The controls are then transferred to a robotized Optimist in a real-world environment to test the transferability of the simulation trained controls. First, the reality gap, or modelling error, between the simulation and real-world environment is quantified to be able to assess the performance of the used techniques to bridge the existing gap. The sim-to-real techniques of Domain Randomization (DR) and the addition of observation noise (ON) are applied during the training process. To test the effectiveness of the trained RL controls, the best performing ones in the simulation are selected and tested in the real-world environment. The performance of the RL controlled Optimist is compared to state-of-the-art controls in robotic sailing. Their performances are measured and compared by means of success rate and a physics-based metric that calculates the efficiency of the sailboat to use the power of the wind to propel itself, called the energy ratio. The results show that the RL controls are highly successful in the sailing simulation, however, the transfer to the real-world remains a major challenge. DR does improve the sim-to-real transfer, resulting in an agent that is able to reach a 100% success rate throughout 12 runs in the real-world environment. ... Read more

This research investigates the application of a digital twin in managing challenges of variability and complexity within systems by using a case study at KLM Engineering & Maintenance. Using the DMADV (define, measure, analyse, design and verify) methodology, the research evaluates the problem using a literature review, measures and analyses the current state of the Logistic Handling Area (LHA), designs a digital twin concept and verifies its value. The literature review highlights the variability in the Maintenance, Repair and Overhaul (MRO) industry, which enables the investigation of digital twin applications for dynamic coordination. The analysis phase reveals significant operational challenges arising from variability in inflows and processing times, enhanced by system complexity and integral coordination issues between departments. To address these challenges, a digital twin is designed that enables real-time monitoring of KPIs, testing of dynamic resource allocation and integral operational target setting. The value assessment shows that the digital twin can support process operators by managing variability through continuous monitoring and evaluation of resource allocation, ultimately achieving predictable and stable system performance in a complex and variable environment. ... Read more

This research focuses on the complexities of integrating routing decisions, inventory management and pressure control in the context of the cryogenic logistics of Liquefied Natural Gas (LNG). This research aims to address these complexities by developing a planning method that considers the transportation and nitrogen cooling costs. The study evaluates the developed planning method through a case study at Rolande, which is a company that operates LNG and Bio-LNG refuelling stations in the Netherlands, Belgium and Germany.

This study considers three refrigeration methods for controlling the pressure level at refuelling stations. These are nitrogen cooling, offload cooling and logistic trailer cooling. The identified key performance indicators that are used to evaluate the develop planning method are: Total Costs, Total Transportation Costs, Total Nitrogen Cooling Costs and Cost per Kilogram. Furthermore, the key constraints and variables for the integrated control of the LNG logistics are identified.

The planning methodology was developed through three steps. First a Full Mixed-Integer Linear Programming (MILP) model was developed. Second, the Full-MILP model was refined to a Simplified MILP model. This Simplified MILP model was improved with the rolling horizon approach and a pre-solve process. The rolling horizon approach divided the planning horizon into smaller manageable time blocks. The pre-solve process identified the critical stations for each day to reduce the number of nodes in the network.

The proposed planning methodology was experimented in the case study that involved a network of 19 refuelling stations in the Netherlands and Belgium. The sensitivity analysis indicated that the model was sensitive to the vehicle capacity. Therefore, the pre-solve process was extended with determining the supply of LNG. The case study results revealed that the model is able to solve a seven-day planning horizon, while maintain the inventory and pressure levels within the specified bounds. However, the model can become computationally complex for days with high number of critical stations and cool vehicles.

This research contributes to inland LNG logistics by addressing the integration of routing, inventory and pressure management. Future studies should focus on a comparative analysis with heuristics, experimenting the feasibility and computational time with soft inventory and pressure bounds, and model a non-linear offload cooling effect to improve the realism of the model.
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To support the deployment of future offshore facilities aimed at achieving net-zero greenhouse gas emissions by 2050, offshore logistics operators must adjust their fleet management and routing strategies to accommodate diverse facility types. Existing methods for determining optimal vessel fleet size and composition are primarily tailored to offshore wind farm activities, lacking integration across multiple offshore facility types. To address this gap, we propose a Mixed-Integer Linear Programming (MILP) approach to optimize vessel fleet configuration, leveraging traditional solution methods such as branch-and-cut with commercial solvers. This model presents a mixed fleet of crew transfer vessels, service operation vessels, and heavy lift vessels to meet the specific demands of future offshore operations. With a focus on hydrogen power plants and carbon capture and storage platforms in addition to offshore wind farms, it demonstrates how a strategically configured fleet can efficiently support the diverse activities involved in integrating these new facilities. ... Read more

Wind energy is growing to be an essential part of the transition towards sustainable energy sources. To facilitate this, it is crucial that wind farm operators can offer competitive energy prices compared to fossil fuel sources; effective and efficient wind farm operations are therefore imperative. However, many full-scale wind farms deal with wake effects, e.g. the disturbed air that travels downstream of a turbine and potentially ends up in other neighbouring turbines. The result of this lower-velocity, higher-turbulence flow field is both decreased power production and increased fatigue loads. One proposed solution for this problem comes in the form of 'wake steering': the yawing (rotating) of upstream turbines' nacelles to facilitate a degree of control over the deflection of wakes. By doing so, the problematic disturbed flow fields can be strategically guided between downstream turbines to maximise collective power production. However, the yawing action itself brings some adverse effects: the upstream turbines, now no longer directly facing the wind, feel decreased power production and increased fatigue loads themselves. These fatigue effects, accumulating over time, can eventually cause increased maintenance costs and nullification of any revenue gains through power optimisation. The problem thus becomes a farm-wide collective revenue optimisation task. This thesis investigates how long-term revenue in wind farms can be maximised, considering both profits through power optimisation and maintenance costs through fatigue-induced component failures due to wake steering control. First, a realistic wind farm simulation environment is constructed, based on a Graph Neural Network (GNN) surrogate wind farm simulation model, to facilitate efficient reinforcement learning training. Next, the environment is used to train fully centralised reinforcement learning agents based on a GNN architecture, resulting in agents that can generalise across all wind conditions and unseen wind farm layouts. Ultimately, the results show that an 'informed' agent that considers all profits and costs involved manages to significantly reduce the cost of energy compared to 'greedy' (power optimisation only), 'risk-averse' (damage minimisation only) and 'baseline' (zero-yaw) policies, furthermore considerably maximising long-term wind farm revenue by as much as 20%. Altogether, this thesis shows promising results in using graph-based reinforcement learning to train maintenance-conditioned, inflow-agnostic, and layout-agnostic wake steering controllers for wind farm revenue optimisation. ... Read more

With Schiphol Airport's flight numbers growing, working assets are essential to ensure on-time processes. The Passenger Boarding Bridge (PBB) is a critical asset in the airport's turnaround process. By ensuring that the asset is working properly, the operational processes can run efficiently. Currently, improving the reliability of the PBB when in use happens after the fault has occurred. With this maintenance strategy, the PBB data is not used to predict the future health state of the PBB. Literature shows that the PBB can be classified as a multi-component system. Research in the predictive maintenance strategy of multi-component systems is still in an early phase. Research until now is more theoretical than practical, and an investigation into applying theoretical knowledge in practice is needed. With the upcoming developments of Industry 4.0, a Cyber-Physical System (CPS) architecture is proposed for a multi-component system. This architecture has been applied to the PBB to develop and use a predictive maintenance strategy for this system. Based on the implementation of a simulation model, the output showed that the proposed CPS architecture enabled the development of a predictive maintenance strategy for the PBB. With this strategy, proactive maintenance is planned while the system's reliability is held on a preset level to ensure a working asset during in-time use. ... Read more

This research addresses the critical role of the Yard Utilization Rate (YUR) in the performance of container terminals, focusing on Rubber-Tyred Gantry Crane (RTG) container terminals. Existing literature reveals a gap in studies specifically targeting high YURs in RTG container terminals. The primary research question is, ”How to reduce Quay Crane (QC) productivity loss in RTG container terminals with highly utilized storage yards?” A Root Cause Analysis (RCA) identifies key factors affecting the loading and unloading processes of QCs, with a focus on the root causes "Too few TTs deployed", "Congestion", and "YUR too high". Design alternatives are proposed to address these root causes, and three design alternatives are tested through simulation: varying the number of Terminal Trucks (TTs), sacrificing driving lanes for additional storage space, and implementing an alternative shuffle policy with a different bay distribution. Results show a significant influence of the number of TTs on QC productivity, with an optimum at 8 TTs per QC. Sacrificing driving lanes for extra storage space proves counterproductive due to increased congestion. However, the alternative shuffle policy Multi-bay with Sets Shuffle Policy with a Clustered Bay Distribution shows promise, offering a potential solution to mitigate QC productivity loss in highly utilized RTG container terminals, with an average increase of 0.4 boxes per hour. In conclusion, the study successfully addresses the identified knowledge gap, providing valuable insights and proposing a practical solution for reducing QC productivity loss in RTG terminals with highly utilized storage yards. The suggested approach of Clustered Bay Distribution with a Multi-bay with Sets Shuffle Policy proves effective in maintaining terminal performance during periods of an increased YUR. ... Read more