This thesis examines the integration of carbon-reducing technologies and distribution decisions within the cement industry's supply chain, with a focus on the impact of these decisions on emerging carbon pricing mechanisms, particularly the European Union's Carbon Border Adjustment Mechanism (CBAM). As the cement industry is a major contributor to global carbon emissions, there is significant pressure to reduce its environmental footprint. Despite the potential of carbon capture technology, its adoption within the cement industry has been slow and remains limited. Moreover, decision‐support tools to guide these decarbonisation decisions remain scarce. This thesis develops and validates an integrated framework that combines long-term demand forecasting, strategic investment in carbon capture technologies, and distribution planning under evolving carbon-pricing regimes.


First, an extensive data collection process assembles detailed information on global cement production facilities, bilateral trade flows, and regional emission factors, providing a robust empirical foundation for the analysis. A country-level consumption series for 1995--2023 is then reconstructed by combining historical trade flows with production capacity data, applying outlier detection and interpolation techniques to ensure trend consistency. Building on this foundation, a systematic forecasting exercise produces reliable country-level demand -- which is thereafter distributed over the three biggest populated cities in the country -- projections for 2025--2050, which are spatially disaggregated to define demand nodes for the optimisation model.


The core of the methodology is a Mixed-Integer Linear Programming (MILP) model that simultaneously optimises binary investment decisions in carbon capture retrofits and continuous cement flows across a 25-year planning horizon. The model’s objective function maximises profit by balancing expected revenues against production, transportation, emissions-related costs, storage and transportation costs for captured carbon, and capital expenditures for retrofit investments.


To evaluate model robustness and the influence of future policy environments, the study conducts comprehensive parameter sensitivity and scenario analyses. Sensitivity analysis systematically perturbs key input parameters -- such as production and transportation costs -- to identify which uncertainties most affect optimal investment and distribution strategies. Scenario analysis contrasts three carbon pricing pathways (STEPS, APS, and NZE), each evaluated with and without the CBAM, to investigate how different policy trajectories shape investment decisions and trade flows.


Results show that the CBAM consistently accelerates retrofitting investments and reshapes international cement trade flows across the STEPS and NZE carbon pricing pathways. Under more aggressive carbon pricing scenarios (APS and NZE), domestic European retrofit projects become viable even without the CBAM, although the mechanism continues to redirect marginal investments toward lower-cost regions. Sensitivity tests reveal that assumptions about regional production costs exert the strongest influence on investment outcomes, while variations in transport-related costs have comparatively limited effects.


This thesis makes three key contributions. Methodologically, it provides a validated, end-to-end decision-support framework that integrates comprehensive data preparation, long-term demand forecasting, mathematical optimisation, and detailed post-analysis. Empirically, it offers novel insights into how the CBAM implementation and alternative carbon pricing trajectories jointly determine the geography of carbon capture investments and global cement flows. Practically, it delivers a strategic tool enabling industry stakeholders and policymakers to plan effective long-term decarbonisation strategies under uncertainty, highlighting the importance of targeted support measures in higher-cost regions and the need to align regulatory designs with the economic realities of global supply chains.

The exponential growth in maritime trade during the 21st century has posed notable challenges for container terminals, resulting in congestion and operational inefficiencies. This study delves into the efficacy of Amphibious Automated Guided Vehicles (AGVs) as an innovative remedy to tackle these issues and facilitate the shift towards autonomous operations at container terminals. Motivated by the need to optimize spatial utilization and reduce reliance on conventional material handling equipment in port areas, this study employs an agent-based modeling approach. Subse- quently, the formulated model is applied in a case study focusing on major Ports of the World. This study undertakes a critical examination of the potential of Amphibious AGVs in alleviating congestion and operational challenges faced by container terminals in the context of an increasingly interconnected global trade setting. By conducting a thorough examination of existing literature and creating a generalised simulation model, this study aims to offer valuable insights that can influence the evolution of container terminal operations.

Offshore wind farms are increasing in size and moving further from shore. Service operation vessels (SOVs) are used for offshore wind operation and maintenance (O&M) at these large far offshore sites. These large vessels typically have a smaller daughter craft (DC) on board that can assist them. This DC is however too small to provide the seakeeping capabilities needed at most far offshore sites, causing it to become essentially unusable. Previous studies at Siemens Gamesa Renewable Energy (SGRE) have looked into improving the capabilities of the DC while considering the constraints of the SOV, this was deemed insufficiently possible by Brans (2021). The second study looked at increasing the size of the DC, which saw significant improvements (Kamerbeek, 2022).

The work of Kamerbeek (2022) however raises new questions such as what is the optimum number of these larger DCs? Would another type of craft serve as a better mothership or DC? Is having the mothership perform maintenance the most efficient? SGRE is therefore interested in exploring mother-daughter concepts to perform offshore wind farm O&M activities at large far offshore wind farms, to see if these can outperform the status quo. A mothership is the home of the technicians offshore and the daughters are the craft that bring the technicians from the mothership to and from the turbines. The main research question is therefore:

What method can best be used to explore the design-space of mother-daughter
concepts for offshore wind farm O&M?

This research first focuses on understanding offshore wind farm O&M and finding the most important restrictions and challenges that need to be taken into account within a model. This has been done through a literature review and discussions with experts from SGRE. The work then focuses on selecting a modeling method and explaining the proposed method. This method is validated using a comparison with a real-life wind farm. A case study is done at the end using a dummy wind farm to demonstrate the workings of the method. The method uses a discrete-event simulation that simulates the transport of technicians to and from the turbines to estimate the performance of the concepts. Any wind farm, turbine failure rates, or fleet can be inserted into the model for analysis to ensure a wide range of applications. The performance of the fleets is assessed based on the estimated downtime/availability and emission estimates that the model produces. The financial and technical feasibility should be evaluated in the next stage when a selection of promising solutions has been made based on this first logistical analysis of the fleets. The visits are planned within the model based on the weather conditions, number of available technicians, craft availability, and the evacuation requirement.

The design space of mother-daughter concepts should be explored by running the model using the exploratory set of fleet configurations and inputting various wind farm layouts with varying realistic visit agendas and weather conditions. The output of each of these cases should then be analyzed by dividing all the fleet configurations into groups based on the craft each fleet contains. This grouping allows the performance of each type of fleet to be compared to one another, while the performance difference within the groups shows the effects of different transfer limits. The analysis should then focus on identifying cross-over points between different configurations and on selecting specific fleets based on performance and expected configuration cost.

The European Union has recently revealed a plan called ”Fit for 55.” The plan aims to reduce carbon dioxide emissions by 55% before 2030. It includes FuelEU Maritime, which will introduce new emission regulations for ships over 5000 gross tonnages. The regulations will require shipowners to reduce their carbon footprint. The European Union has decided not to wait for the International Maritime Organisation’s emission rules (IMO) and will enforce them for ships by 2024. By 2025, the rules will extend to offshore vessels with a gross tonnage of 400 or more and general cargo vessels carrying commercial goods between 400 and 5000 gross tonnages.
Efforts are being made to find a hydrogen carrier that closely resembles conventional oil-based products to comply with these regulations. All these fuels need to be produced with renewable energy sources, which have their efficiency losses. Renewable fuel production is only estimated to have a chemical efficiency of 50%.
Innovation in the shipping sector is necessary to reduce energy losses. The shipping sector fits the rules of rural society, where incremental innovations are preferred over radical changes. However, radical change is necessary to accomplish the energy transition in shipping. According to the DOI theory, innovators are the first group of adopters. Innovators are eager to try new ideas and have a cosmopolitan (global) network. These innovators will play a critical role in the energy transition in the shipping sector.
The study’s objective is to analyse if it is possible to influence a given adoption of the innovator. First, the research outlines the theoretical framework for the study. The literature search aims to determine a transition framework to answer the research sub-questions. The framework’s scope will be refined to the innovator group and the maritime sector. A case study will be conducted to test the defined framework, and factors outside the scope may be included if needed. The literature collection approach involves determining the philosophical framework before researching the sociological framework. The mainstream innovation and inclusive innovation frameworks have been identified from a philosophical perspective. The mainstream innovation framework focusing on radical and technological typology is more appropriate for the research study. Rogers’s sociological framework can be used to describe the adoption process. The Scopus search has been used to identify different theories, including spatial innovation frameworks, sectoral innovation systems (SIS), technological innovation systems (TIS), and path development. Finally, the study provides an overview of the innovation systems and their corresponding frameworks……

The importance of natural environments with rugged deformable terrain (marshes, mangroves, rainforests, coastlines) from biodiversity, carbon capture, and coastal protection to economic livelihood is significant. However, the current systems available for robots to explore those ecosystems are either large, expensive and intrusive, not application focused or consist of custom components that are difficult to integrate with existing robotic mechanisms. This thesis proposes a novel soft adaptable wheel suited for such ecosystems. The mechanism operates as a fluidic elastomer actuator constructed with Dragon Skin 30 able to change its form depending on the task at hand. Various designs were simulated in MuJoCo to test its ability to overcome rigid obstacles and terrain forms (from ramps, steps, smooth undulating terrain and terraced undulating terrain). The design's structural feasibility was then tested and further improved using Ansys with the final result having a loading capacity of 1.25N (at 0kPa) and a maximum blocked force of 1.98N (at 35kPa). Finally, the wheel was tested at 3 distinct operating configurations (neutral, partial and fully inflated) in 4 types of deformable terrain (non-compressible dry, compressible dry, non-compressible sticky, compressible sticky) using EDEM Altair. This evaluated its performance and validated the need for different forms according to the rheological properties of the terrain. The design proposed in this paper is intended to be more application-focused, with its simplicity facilitating integration into robotic systems, using off-the-shelf components, and ultimately reducing the time required to be used in environmental applications.

Transport and storage operations in a steel coil manufacturing industry make up a large part of a factory’s operating costs. With the advent of automated forklifts, unmanned transport and storage operations are becoming increasingly viable. Automated forklifts have the potential to lower operating costs, reduce the required number of employees, minimize transport damages and eliminate over-processing. Nonetheless, transitioning from manual forklifts to a fully operational automated transport and storage system requires addressing a range of complex decisions. These include the flow path layout, fleet sizing, vehicle dispatching, storage location assignment, and other relevant subjects.

This thesis presents an integrated approach to how the flow path layout design influences the material flow effectiveness, incorporating vehicle scheduling and storage location assignment policies. Based on a real-world case study, analyzed through Discrete-Event Simulation (DES) software, this study addresses the following research question: What in-plant system design facilitates effective material flow by implementing automated transport in a steel coil manufacturing plant?

First, applicable literature is investigated, thereafter, the system is analysed, and design alternatives are generated. The design alternatives vary in the use of manual and automated forklifts, and the flow path layout considers both conventional and zone-based flow approaches. The experiments test the influence of dispatching policies and fleet sizing on all alternatives. Furthermore, battery management, idle-vehicle positioning, unit-load selection and case-specific system constraints are integrated. The storage location assignment is based on the order identification number and the fill level of the storage parks.

By capturing the dynamic and variable nature of a stochastic production system, the DES evaluates the impact of different configurations on performance, costs, and other performance indicators. The cost-performance relations are plotted, resulting in a Pareto front consisting of a set of non-dominated system design configurations. A preferred automation alternative is selected from this set. Conclusions regarding the most effective transport system design and the integrated system design process are drawn.

This paper focuses on the effect of different storage policies on the performance of RoboticMobile Fulfilment Systems (RMFSs). The research is conducted under the instructions of the Technical University of Delft and CEVA Logistics the Hague. The aim of the research is to develop a decision support tool that can aid companies in the choice of the storage policy to use for their RMFS. RMFSs have multiple different levels of decision problems that need to be solved. The performance of a RMFS highly depends on the algorithms that are applied to solve these decision problems. This research focuses only on the storage policies of a RMFS. In this case storage policy refers to the decision in which pod items should be stored and where on the storage area the pod should be positioned. In order to gain a good understanding of the effect of such a policy on the overall performance of a RMFS, experiments should be performed. Physical experiments are however very hard and costly to perform. This research therefore makes use of a simulation study to test different storage policies in different scenarios. The simulation model used is an adaptation on an agent-based semi-open queuing network framework model by Merschformann et al. (2018a). In the experiments four different storage policies are investigated under three different storage layouts. The results of the simulations are analysed via the throughput, the pile-on and the distance travelled during the simulation. After this a score is given to
both the picking as well as the replenishment side of the system. It is important to investigate both sides of the system since the flaws on one side can negatively affect the other side. The scores of the replenishment and picking processes are then averaged to gain a final score which indicates the overall performance of the
policies. Finally a fifth policy has been developed where each pod can contain multiple different sized compartments. Unfortunately testing and verification of this policy was not possible in the given time frame. For this reason it has been left out of the experiments.

It is becoming increasingly clear that the effects of climate change should be decreased or even mitigated. Green alternative sources of energy are being explored, and wind energy emerges as an important option that can be exploited on a large scale. Wind turbines are placed more and more often offshore due to larger and more stable wind resources. The most common foundations for these turbines are monopiles. The future outlook for these turbines and their foundations is that they will become bigger. An important design characteristic of monopiles is the natural frequency of the pile. In order to keep dynamic effects to a minimum, excitation frequencies should not coincide with the natural frequency. The primary source of excitation of monopiles are bending moments. Therefore, this thesis will take a closer look at the dynamic bending capability of monopiles.

There is a lack of knowledge on dynamic effects of bending moments on steel cylindrical shells (monopiles). While the dynamic axial buckling capacity of cylindrical shells has been researched extensively, there is little known on the dynamic bending buckling capacity of such shells. This thesis investigates the influence of loading rate on the dynamic buckling capacity of monopiles subjected to bending moments. For this, a finite element model is constructed which is validated by analytical models. Later on, the finite element model is used to conduct a parametric study to investigate the effect of loading rate on buckling capacity.

The scope of this study excludes factors such as soil dynamics, fluid structure interactions, residual stresses, and lateral forces. The focus is solely on the cylindrical shell of monopiles, excluding secondary steel from consideration. Initial geometric imperfections are considered as local perturbations necessary to initiate buckling, while other factors that may affect lateral forces or overall structural capacity are excluded.

This study will show that different parameters play part in the dynamic bending buckling behavior of cylindrical shells. The natural frequency of the cylinder, as well as the non-dimensional length together with the yield stress of the material play an important role in the dynamic buckling capacity. This research concludes that cylindrical shells with higher natural periods are more influenced by dynamic bending moments than cylinders with shorter periods. Next, shorter, stocky cylinders exhibit higher dynamic buckling capacities than slender cylinders. Also, imperfections are found to decrease the buckling capacity of cylindrical shells, but this effect diminishes for increasing loading rates.

Keywords: Offshore wind energy; Cylindrical shells; Dynamic buckling; Loading rate; Imperfections; FEM;

This study entails the development of a planning model utilizing Centralized Model Predictive Control (CMPC) to optimize the flow of physical goods throughout a network of supply chain nodes, utilizing a Mixed-Integer Linear Programming (MILP) approach to determine the optimal decision variables. Specifically, a Current State CMPC model was created to reflect the current outbound logistic network at Heineken Zoeterwoude, where information asymmetries are known to impact the accuracy of the outbound logistic planning tool. The Current State model was compared against a Future State model, where real-time data is available, thereby eliminating the aforementioned information asymmetries. By assessing four key performance indicators, it was found that the Future State model enables considerably better performance of the logistic network, even during peak production.