The production of carbon dioxide (CO2) is the largest contributor to global warming. The primary source of these emissions is petroleum-derived fuel used in transportation, specifically in internal combustion engine vehicles. In response, the European Union (EU) has implemented a low-emission mobility strategy, aiming to shift towards low-carbon transportation. This strategy includes ambitious targets for electric vehicle (EV) adoption with at least 30 million zero-emission vehicles expected to be on European roads by 2030. This push towards EVs is driving up demand for lithium-ion batteries (LIBs).
While efforts to improve efficiency in EV battery production and use are intended to reduce environmental impacts, they might potentially lead to unintended consequences known as rebound effects. Rebound effects occur when efficiency improvements result in increased consumption or production elsewhere in the system, negating the expected benefits. These effects can arise from both behavioural and systemic responses to efficiency gains. Current discussions and policy frameworks often overlook the potential rebound effects in CE activities, particularly concerning EV batteries. This oversight poses a significant risk to the effectiveness of initiatives aimed at reducing CO2 emissions and advancing CE practices. Therefore, it is crucial to investigate these rebound effects within the CE context for EV batteries to develop effective mitigation strategies.
There is a noticeable gap in the existing literature regarding the interconnected dynamics of CE practices and rebound effects in the context of EV batteries. While some studies have explored rebound effects in other sectors, limited research examines how CE practices for EV batteries contribute to rebound effects across different lifecycle stages. To ensure the sustainability of CE initiatives, it is essential to adopt a systemic view that considers these effects. By doing so, businesses can develop strategies that not only focus on recycling and efficiency but also address broader systemic impacts, ensuring that increased demand does not negate the benefits of circular practices.
The research employs qualitative SD to examine potential rebound effects in the CE system for EV batteries. This approach allows for a comprehensive understanding of the system's dynamics by identifying causal relationships between physical and behavioural components. The study uses CLDs to represent the interconnections and feedback processes within the circular economy of EV batteries, helping to identify reinforcing and balancing feedback loops that drive system behaviour. The initial phase involves identifying key variables that influence the system's behaviour. These variables include economic incentives, technological advancements, regulatory frameworks, consumer behaviour, and environmental impacts. The relationships between these variables are mapped to create CLDs, which are then iteratively refined based on expert feedback to ensure accuracy and relevance.
Expert interviews are conducted to validate and refine the constructed CLDs. Professionals with in-depth knowledge of the circular economy and EV batteries provide insights that help verify the model's assumptions, structure, and behaviour. This validation process includes discussions on potential oversights or nuances that the initial model may not fully capture. The methodology also involves developing Circular Business Models (CBMs) as strategies to mitigate rebound effects. A review of existing business model innovations in the CE context is conducted to identify patterns relevant to EV batteries. These business model patterns are categorized to align with the rebound mechanism categories, facilitating a cohesive analysis.
The study identifies key mechanisms within the EV battery lifecycle that can lead to rebound effects, particularly during the usage phase. Three significant mechanisms—Income, Substitution, and Demand Adjustment by Efficiency—were found to be most prominent in this phase. To mitigate these effects, the research proposes various strategies. Dynamic Pricing emerges as the most effective strategy, capable of addressing all three mechanisms by adjusting prices in response to real-time market conditions, thereby preventing excessive consumption and production. Alternative strategies such as Pay per Use and Subscription models also show promise in mitigating rebound effects by promoting efficient use and reducing the need for outright ownership.
The research highlights the importance of understanding these mechanisms and selecting suitable strategies to mitigate rebound effects. Firms must focus on the identified mechanisms and integrate appropriate strategies into their business models to ensure sustainable practices. Furthermore, the study underscores the necessity of engaging a diverse range of stakeholders, including consumers, policymakers, and industry practitioners, to develop comprehensive and inclusive CE strategies. Future research should continue to refine these strategies and explore the dynamic interactions within the EV battery lifecycle to enhance the effectiveness of CE initiatives.

This study introduces HydroWizard, an innovative framework addressing critical challenges in water resource modeling through enhanced transparency, efficiency, reproducibility, and extensibility. Integrating a YAML-based Model Specification Language with a sophisticated Execution Engine, HydroWizard enables accessible modeling of complex water systems.

Applied to the Lower Omo-Gibe River Basin in Ethiopia, the study employs Evolutionary Multi-Objective Direct Policy Search to identify 283 Pareto-optimal policies. Findings reveal nuanced trade-offs: irrigation-optimized policies eliminate demand deficits but reduce environmental flows by up to 48%, while environmentally-focused policies show opposite effects. Notably, mean power generation remains relatively consistent across policies, challenging assumptions about water resource allocation trade-offs.

HydroWizard introduces novel visualization techniques, including animated rule curves and system state graphs, enhancing strategy interpretability. Its versatility is demonstrated through application to diverse water systems, including the Zambezi River Basin.

This research marks a significant advancement in water resource modeling, offering an open-source, accessible tool for complex water system analysis. It contributes valuable insights for sustainable water management and sets a new standard for global water resource management studies, emerging as an innovative solution to intensifying water management challenges worldwide.

Climate change demands a transition to a carbon-neutral energy system. In the Netherlands, this shift involves electrifying sectors, predominantly by increasing energy sources like solar PV and wind power. This comes with flexibility challenges during times with insufficient variable Renewable Energy Sources and extended periods with wind droughts, or a dunkleflaute which is a period of more or less two weeks with insufficient wind and a cold front. Electricity generation from molecule based fuels can play this flexible role to balance the system in the future. Hydrogen is a versatile energy carrier that can play this role in a low carbon manner. Hydrogen can be stored in large volumes underground in salt caverns and in depleted gas fields. To ensure security of supply in different scenarios it is necessary to understand what volume of hydrogen should be stored in storage facilities to withstand a dunkleflaute. What is also necessary is to understand with what speed this hydrogen should be injected and produced from storage facilities, in other words: in how many days the storage can empty and fill. The production of green hydrogen with which the storage is filled is produced through the process of electrolysis. There are different types of technologies to produce green hydrogen. This research also focuses on the trends in developments regarding electrolysers and their impact on the system. As well as on the policy goals set by European Union and the Dutch government. The method in which the research was set up is by determining the system characteristics and uncertainties and going through the motion of the modelling cycle multiple times. This research is conducted by simulating the Dutch energy system in Linny-R, a mixed integer linear programming software tool. The model is made for the years 2035 and 2050. A sensitivity analysis is done to determine the most influential keys in the system. Based on literature and this sensitivity analysis, a scenario analysis was determined. Many scenario’s were then tested in the model to determine their impact. The system is measured with the following KPI’s: Carbon emissions, Loss of Load Expectation and Expected Energy Not Supplied, System costs and Production cycles. The conceptualisation in this thesis details the intricacies of the Dutch energy system and its simulation within the Linny-R model, offering a foundational understanding essential for later analyses. This chapter dissects the components and operational settings of the model, emphasizing their significance in shaping outcomes. The Dutch energy system’s transition from fossil fuels to renewable energy sources is mapped within Linny-R, highlighting the interplay of various energy forms and the constraints inherent to the system. The setup includes explanations of Linny-R’s key entities and discusses the model’s execution over a yearly timeline with hourly steps to capture dynamic system behaviours. Special attention is given to the settings impacting computational efficiency and the incorporation of historical weather data to account for variability in renewable energy production. After discussing the system a framework is presented, detailing the external factors, policy levers, system relationships, and performance metrics that guide the analysis. The analysis focuses on verifying the model, conducting a sensitivity analysis, and beginning scenario discovery to test the model’s performance. Verification ensures the model accurately represents real-world systems by examining storage facilities’ behaviors and hydrogen-to-power conversion during low variable renewable energy supply (vRES) periods. Sensitivity analysis tests how changes in input parameters impact outputs, identifying critical inputs that influence the model’s performance and ensuring robustness. Key findings show that hydrogen and electricity demand parameters significantly affect system costs and efficiency. Scenario discovery assesses the model’s resilience under various conditions, including extreme weather years, to identify potential bottlenecks. This chapter establishes the model’s reliability and guides improvements in its predictive capabilities. The results show the findings from the scenario analysis aimed at evaluating the potential of underground hydrogen storage in the future energy system. These experiments investigate various performance parameters to inform decision-making for effective integration of underground hydrogen storage. Key findings include the impact of electrolyser capacity on hydrogen production and system efficiency, ii iii revealing that increased electrolyser capacity significantly reduces carbon emissions, though it must scale parallel to storage capacity to be effective. The analysis of different storage types highlights the importance of sufficient electrolysis capacity to fully utilize storage facilities. Concluding their dependence on one another. Additionally, scenarios exploring battery capacity, gas power plant use, and demand variations provide insights into optimizing the energy system while achieving policy goals. The results underscore the complex interplay between various components of the hydrogen storage system, emphasizing the need for balanced and integrated approaches to meet future energy demands and sustainability targets. The discussion interprets the research findings, emphasizing the anticipated demand for underground hydrogen storage and the required production capacity for 2035 and 2050. The study reveals that while there will be significant storage needs in 2035 due to hydrogen shortage, the demand will decrease by 2050 as renewable energy sources and electrolysis capacity increases. Economically and functionally, a combination of salt caverns and gas fields is recommended, with optimal configurations identified for injection and production capacities. The research also highlights the critical role of electrolysers and their efficiency. Limitations of the study are discussed and recommendations for future research directions suggest exploring blue hydrogen, integrating hydrogen flow dynamics, and considering regional energy needs. The study’s findings have practical implications for policy decisions for Dutch hydrogen storage

Underground Pumped Hydro Storage is considered as power storage asset in Zeeland. Using Linny-R, a cost-driven model is made representing the power flows in the high-voltage grid of Zeeland. This model includes power production by nuclear power, fossil power generators, solar PV and windturbines, power demand from industrial companies, agriculture, mobility and dwellings, and the current market functioning using the day-ahead price and marginal costs. Modelling various scenarios, the impact of UPHS on the high-voltage grid is analysed, and discussed in a broader perspective of societal added value.

The large-scale deployment of offshore wind farms (OWFs) in the North Sea, targeting 300 GW by 2050, presents opportunities for carbon-neutral energy but challenges conventional grid connection methods. Offshore bidding zones (OBZs), characterised by variable generation and limited dispatchable demand, require innovative imbalance management to maintain energy balance. This thesis models the bidding behaviour of balancing responsible parties (BRPs) in sequential electricity markets using Linny-R, examining the impact of production uncertainty, price risks, and gate closure times on imbalance volumes. Results highlight trade-offs between shortages and curtailment under varying imbalance penalties and gate closure times, with reduced gate closure times significantly mitigating imbalances. Policy recommendations emphasise the need for flexible demand, risk mitigation mechanisms, and coupled imbalance pricing to enhance market efficiency and energy balancing incentivise in OBZs.

The transition to a carbon-neutral power system has become a global imperative, ever more driven due to recent events such as the energy crisis resulting from the Ukraine invasion. The Netherlands, devoted to the large-scale development of offshore wind and solar PV, see great potential in the use of hydrogen as a gas replacing energy carrier. A knowledge gap has been identified on what this role of electrolysis will be in the future energy system and more specifically, in what quantities electricity can and should be converted to hydrogen. This research aims to address these uncertainties and provide insights into the prudent capacity for green electrolytic hydrogen production in the Netherlands by 2030 and 2040.

To this end, a model of the combined electricity and hydrogen system of the Netherlands is constructed in Linny-R, an executable graphical representation language for Mixed Integer Linear Programming (MILP). A vast array of output data from forecasting scenarios is gathered and consolidated to establish the spectrum of input variables. By systematically assessing the uncertainty, based on these ranges of input variables, and the impact, based on a sensitivity analysis, an experiment design is composed to encompass the most complete reflection of all uncertainties within computation limits. Numerous experiments are then conducted to generate outcomes, determine prudent capacity, identify influential uncertainties, and evaluate the impact of large-scale hydrogen storage on electrolysis potential.

The research results demonstrate that the potential for electrolysis increases from 8-12 GW in 2030 to 8-44 GW in 2040, indicating the cruciality of flexibility for the Dutch energy system in the coming years. The most prudent capacities for 2030 and 2040 are identified as 12 GW and 38 GW, respectively, with 9 GW being identified as the no-regret capacity to be installed by 2030. Therefore, the research recommends stimulating additional investments beyond the initial targets of 3-4 GW for 2030 to foster the Dutch hydrogen economy. Moreover, there is substantial risk of overinvestment associated with the prudent capacities, especially as a result of the growing uncertainty in 2040. Deploying additional flexible system capacity to prevent electrolysis from acting as a loss of load mitigation measure can lower the prudent capacity and reduce the risk of overinvestment. Furthermore, policy measures should focus on stimulating influential uncertainties such as hydrogen demand, peak residual load and hydrogen storage. Every additional 100GWh of underground hydrogen storage capacity enables 500MW of electrolysis potential. Moreover, the last few gigawatts of the prudent capacities will primarily serve as peak production plants with limited operational hours and margins starting at 11.00 EURO/kg required to recoup the investments.

Further research opportunities include incorporating geographical and grid aspects into the model, exploring profitable conditions for investments in the last few GW of electrolysis capacity, investigating the role of large-scale hydrogen storage, and studying the potential of electrolysis beyond national borders in the North Sea interconnected electricity generation hub.

The ever-rising greenhouse gas emissions move the European Union to transition towards a future decarbonized energy system. To achieve this, the future energy system will mainly comprise variable renewable energy generation sources. Energy production from these sources is susceptible to weather fluctuations. Uncertainty of future weather scenarios translates into energy system models that help decision-makers to design the future renewable energy system. Energy system models often use a single weather year to simulate weather behaviour. Thus, many energy system models fail to take weather fluctuations between various years into account. Identifying energy system designs that are robust to weather fluctuations, i.e. systems that are able to suffice demand regardless of the weather circumstances, is therefore key.

The research described in this thesis has aimed to develop and test a method to give insight to decision-makers into the composition of robust and efficient energy systems, given weather uncertainty. To achieve this, the SPORES methodology has been used and extended to identify energy system configurations that are both robust and efficient. For this, a decision option space for decision-makers has been created that has been diversified based on renewable energy generation and storage technologies. To test the developed method, the North Sea region has been used as a case, as this is the region thought by policy to have great potential to house renewable generation sources in Europe.

The method developed in this research systematically covers the decision option space over three weather scenarios (worst, typical and best). From these decision spaces, configurations that meet demand with installed capacities that exist across the whole weather options space have been selected as robust. Clustering was used to identify types of energy system configurations, having commonalities in the installed generation capacities. Energy efficiency has been identified as key for measuring energy system performance. This research, therefore, takes curtailment and energy system yield into account to quantify efficiency. Using a Pareto analysis, both robust and efficiency-wise high-performing energy system configurations were identified as most promising for decision-makers.

The no-regret decisions, visualized by the SPORE-core, are minimum capacities required across the whole decision space. Results showed that robust energy systems are typically comprised of balanced configurations, meaning that solar PV and wind power both have the largest capacity of energy generation sources. The balanced configurations also contain high transmission capacities and typically no storage capacities indicating energy is distributed rather than stored. The robust and efficient configurations need additional capacity investments on top of the no-regret decisions. Especially solar PV needs a large increase in capacity when robustness and efficiency are required. Combined heat and power from biofuels and electrolysis capacity are also key to robust and efficient configurations. Additional results showed that the majority of robust and efficient configurations utilised more offshore than onshore wind capacity.

The findings of this research are based on a case of the North Sea energy system with a high level of aggregation and are thus of limited use for precise designs of the North Sea energy system. The method created in this study can be adapted to contain more detail and offers space for researchers to include their own performance indicators. However, this research already used significant computational efforts, so adding more resolution and detail will mean the computational process can be restricting. Future research should focus on using the developed method to select promising and robust energy system configurations with higher levels of detail and conduct further weather scenario analyses on the selected configuration.

Following the climate goals of the Dutch government, a tremendous amount of vRES (variable Renewable Energy Sources) will be built. These energy sources will produce large amounts of power, which must travel great distances on the transmission grid. The Dutch TSO, TenneT, is responsible for the transmission grid's design, construction, and maintenance by European and Dutch law. The planning for transmission grids has become more complex since the liberalization of power markets. Because of this complexity, the planned expansions are published every two years. In energy systems, the ever-changing context of the system makes the possible future states of the system uncertain. In this research, we 1. identify the relevant uncertainties for the transmission system, 2. Identify the most vulnerable lines under these uncertainties, and 3. Look at the effect of the delays on the performance of the overall power system. This gives insights into how the transmission system must evolve to maintain the grid's performance.

Not only more power from vRES will be generated, but coal plants will also be phased out, and the demand for other energy sources (fuel, gas, etc.) will be electrified. More power flow will lead to congestion on the current grid. The Dutch Transmission System Operator (TSO) uses congestion management to alleviate acute congestion and expansion planning to prevent future congestion. To decide whether grid expansion should be used, Production Cost Models (PCMs) are used. To account for uncertainties in a system, the concept of deep uncertainty can be employed to map the uncertainty of a system. Exploratory modeling uses this concept to understand the consequences of deep uncertainty. Experimental modeling and analysis could thus be used to understand the effects of vRES on the power grid.

Direct Current Optimal Power Flow (DCOPF) is used for the formulation of the Production Cost Model (PCM) for the power network modeling. The chosen exploratory modeling framework is a simplified form of the XLRM framework, in which the policies in the model (L) is not altered. The data used as input for the model is selected from a wide range of sources, following the format of the DCOPF formulation. The tool used to build and simulate the DCOPF model is Scenario Analysis Interface for Energy Systems (SAInt) software. The EMA workbench is used to perform exploratory modeling and analysis. Two times 1,000 experiments for a week period have been run with these tools, one set in January and one set in June. To assess which lines in the transmission network might be susceptible to congestion, we analyzed the experiments' outcomes with a congestion-seeking algorithm. The adopted methodology has been verified using a 5-node model.


The results of these experiments are divided into two types of outcomes: the general system outcomes and the line outcomes; the latter has been split up based on the urgency of capacity expansion. The analysis tools by the EMA workbench were used to analyze the results of the experiments, especially the Patient Rule Induction Method (PRIM) algorithm. The experiments showed significant differences in congestion between June and January...

Electrification by means of renewable energy sources in electricity production (RES-E) is a key strategy to meet global climate goals established in the Paris Agreement. The increasing share of RES-E as a result of this has significant implications on the functioning of the electricity system and the adequacy of the electricity market design, as these technologies exhibit different characteristics compared to conventional generators. Their intermittent output combined with near negligible marginal costs has an effect on electricity price formation and will lead to an increased demand for flexibility services to level out these fluctuations. These trends will induce an increased investment risk for flexible generation capacity. This increased investment risk feeds into the risk-averse behaviour of investors that is already present due to market failures that exist in electricity markets. It remains unclear whether the liberalised neoclassical electricity market design provides sufficient incentives to invest in generation capacity in order to maintain system adequacy during the energy transition.
One possible market design option that is proposed to address this increased investment risk are Capacity Mechanisms. Capacity Mechanisms allow for an adequate remuneration of (flexible) generation capacity, by providing a steady income stream to firm capacity on top of revenues obtained from selling electricity. The purpose of this thesis is to evaluate the effectiveness of a capacity market - a specification of a capacity mechanism - in maintaining system adequacy in a system with an increasing share of RES-E. Additionally, it aims to fulfil the need for electricity system models that allow for performing robustness analysis as this it is seen as increasingly important to adequately account for uncertainty that is inherently related to the unfolding of the energy transition. This research was carried out by extending an existing quantitative model of the Dutch electricity system, referred to as Myopic Optimisation Detailed Operational (or MODO), with a capacity market. The methodology applied relies on myopic optimisation.
Conceptualisation of the capacity market model is based largely on the design of the NYISO Installed Capacity Market (NYISO-ICAP), as this design is relatively simple whilst being considered a successful capacity market. A thorough literature review on the NYISO-ICAP was performed to establish a profound understanding of real-world capacity market dynamics and how these can be translated to the model. The conceptualisation of the capacity market forms a basis for formalisation and is used to establish the formal rules of the capacity market model complying with the basic structure of linear programming problems. This serves as input for the implementation of the extension of the capacity market in MODO, and thus in Linny-R.
With the resulting model, the effectiveness of a capacity market in maintaining system adequacy was assessed on the performance of several Key Performance Indicators. These encompass the Supply Ratio, the average annual volume of Energy-not-Served [MWh], the average Electricity Price [€/MWh] and the total Consumer Spending [€]. A comparative analysis was performed between the performance of the energy-only market and the capacity market on the identified Key Performance Indicators in four pre-specified scenarios. The four scenarios are varied on key uncertainties underlying the unfolding of the energy transition to explore the robustness of results, such as weather conditions and the level of risk aversion of investors.
From this thesis, it can be concluded that a capacity market can be an effective and robust policy instrument to maintain system adequacy during the energy transition at a lower cost to consumers. This can specifically be seen in the increased number of investments in peaking generators compared to the energy-only market when forecasted revenues of the capacity market are sufficient, which has a positive effect on the electricity system's robustness to different weather conditions. Furthermore, when the level of a risk aversion of investors is high, a capacity market is more robust in ensuring system adequacy compared to an energy-only market.
Nonetheless, a capacity market can be prone to investment cycles which can have a negative influence on the effectiveness of a capacity market to maintain system adequacy at all times. These investment cycles are a result of the bounded rationality and myopia experienced by investors, ultimately leading to imperfect forecasting of the revenues of the capacity market. Results of this thesis have shown that in busts of investment cycles, investments in generation capacity can be insufficient to maintain system adequacy.
To conclude, a capacity market can be a viable option in order to maintain system adequacy in the energy transition. However, whether it is the most suitable option to maintain system adequacy is not clear. Novel flexibility options such as storage could potentially serve a significant role in maintaining system adequacy. If additional flexibility options have a lower perceived investment risk compared to flexible generation units, the electricity market can potentially continue to rely on its initial liberalised neoclassical market design whilst maintaining system adequacy. This requires further exploration in future research.
Furthermore, MODO and its capacity market extension fill the need for electricity system models that allow for performing robustness analysis, as it has a relatively low computation burden. Hence, an interesting avenue for future research is to evaluate the effectiveness of a capacity market in many possible futures.

Because of the climate crisis the world is facing, all sectors must move towards a more sustainable future. In the Netherlands, the industry sector emits large amounts of CO2 because of the heavy reliance on fossil-fuels and large electricity demand. The Paris agreement and the Green Deal forces the industry sector to rapidly transition towards more sustainable practices, which can be achieved if the correct synergies are established. The multi-actor nature and the technical and operational dependencies that industrial clusters have, makes optimal decision-making extremely important in working towards that net zero future. Innovations that re- duce the emissions are limited, but they exist and provide (intermediary) solutions to adhere with the imposed regulations. Furthermore, the industrial clusters are subject to numerous different factors that influences the behaviour of actors within the cluster. The issue faced is to understand how the transition of an industrial cluster is influenced by exogenous factors and how actors in such an integrated environment should invest, while keeping in mind that both the industrial clusters and the individual actors have to remain profitable and have differing investment behaviour. Current studies fall short in identifying, analysing and understanding how multi-actors in an institutional en- vironment relate to the technical options and the exogenous factors engaging with that system in an industrial setting. Identifying an optimal investment trajectory that such an industrial cluster should follow to adhere with the regulations whilst staying profitable with a multi-actor configuration requires different integrated methods and other tools. This thesis will address that problem through the following main research question: What is the effect of multiple exogenous factors on the optimal investment trajectories of indus- trial clusters in the Netherlands with multiple investment options? This question has been answered through exploratory research combined with a modelling approach. The first step in the exploratory research required attaining insights in what the current state of is multi-actor invest- ments. This in combination with looking at how they are structured contractually has been the main foundation for the literature review. A case study analysis has been conducted of an industrial cluster located in Geleen, called Chemelot. This allowed for analysing decarbonizing investment options and looking at what exogenous factors influence the industrial cluster. The investment options attempt to move Chemelot away from fossil-fuels as energy source and move towards lower emitting sources such as electricity. The information gathered served as an input to put together a methodology which captured the exogenous factors, interactions in the industrial cluster and the sustainable investment options in an optimization model. Using this, the optimization model was constructed using Linny-R, a Mixed Integer Linear Programming op- timization software developed by Dr. P.W.G. Bots. The model produced quantifiable results as well as serve as a proof of concept for the methodology that is presented to incorporate exogenous factors and sustainable investment options in the energy transition. The model also gives insights in what effect the exogenous factors have on the investment behaviour of actors within industrial clusters by showing cash flows, both individually and collectively and through an investment curve for the cluster. The economic performance is considered to be the leading metric in this research. Finally, using the results, the implications of implementing exogenous factors and sustainable investment options in an optimization model are discussed. The research outcomes show that CCS and electrification are favourable investments for Chemelot to remain profitable and cope with increasing prices of commodities and CO2. The model results has provided insights in the effect that exogenous factors have on the energy transition of an industrial cluster. More specifically, it showed that a cap on CO2 and increasing the price of CO2 emitted really accelerates the rate at which actors make investments in sustainable options. Added to that, increasing the price of other commodities such as natural gas and naphtha also forces actors to move away from those commodities and look for alternatives that provide the same product or energy without having to compensate the CO2 emissions related to them. Other factors such as limited infrastructure restrict industrial clusters in the amount of products they can produce and restrict them from innovating towards less emitting processes. Electrification of certain processes is possible, but increases the electricity demand with enormous amounts. With limited electricity infrastructure to provide those amounts, the innovation cannot be realized and therefore, halting the transition for an industrial cluster. The findings suggest that because of these exogenous factors, the actors will move towards electricity demanding innovations that do not make use of fossil-fuels and invest in CCS options. However, the methodology presented should be used as directing future research in looking at how industrial clusters should engage in the energy transition. The dependencies existing between actors in an industrial clusters and how different investments II may adjust these dependencies are identifiable using the methodology presented in this research. Ending with a general conclusion, the investment behaviour of industrial cluster changes because of multiple exogenous factors that influence the system. The increase in commodity prices combined with CO2 capping reg- ulations cause actors to invest more quickly into sustainable investment options to adhere with the regulations, but it also causes them to compromise their production levels in some scenarios to remain profitable. Actors downstream are dependent on the investments made by actors upstream since they rely on the output of those upstream actors to develop the final products that they sale. This combination generates valuable insights from a systems perspective but also from a multi-actor perspective.

In order for the Netherlands to reach CO2 neutrality by 2050, large investments in zero emission technologies are needed. These investments would comprise out of renewable energy generation, higher energy efficiency alternatives and electrification of end uses. Although climate mitigation has become an ever growing societal concern since the ratification of "het klimaatakkoord", progress in the industrial sector has been seriously lagging in the Netherlands. In 2021 the reduction of CO2 emissions in the Netherlands stagnated and the emissions of the industrial sector actually slightly rose. So too in the Port of Rotterdam, where the chlorine cluster is not showing any significant CO2 reductions. There are several potential explanations why there is a gap between what should be invested and what actually is invested. They define the gap as, ’the apparent reality that some technologies that would pay off for adopters are, nevertheless, not adopted’. So, why do decision makers under invest in zero emission technologies? The explanations of fall into two broad categories:
•Market barriers
•Behavioural barriers

Following the problem situation, this thesis focused on researching to what extent market & behavioural barriers contribute to the investment gap in the Port of Rotterdam Chlorine cluster, by incorporating both market and behavioural barriers in a quantitative investment model. The incorporation of these barriers in the model result in a range of investment types, some of them non-optimal and varying in perspectives on valuing the future. By simulating technology adoption under the assumption of this range of varying configurations of market and behavioural barriers, it is possible to determine the effect of those two categories of barriers on technology adoption at the PoR chlorine cluster. The obtained insights of this research feed into the larger study of quantitative decision models for the industrial sector. Where the following main research question is answered:
What is the effect of market and behavioural barriers on zero emission technology adoption at the PoR chlorine cluster?

To answer the main research question, the model represents the PoR chlorine cluster on a highly detailed level and bases it's technical system's configuration on a thorough plant-process-product & zero-emission technology inventarisation. These two inventarisations give the current and possible future configuration of the PoR chlorine cluster's technical system. Consequently, this makes it possible to explicitly model the technology stock at the chlorine cluster and determine technology adoption on an asset level. The market & behavioural barriers are represented by 8 evaluation types. These evaluation types represent 8 varying configurations of the market & behavioural barriers. Consequently, a scenario analysis was conducted with the model.
The scenario analysis resulted in transition pathways, total CO2 emissions and total cash flows between 2022-2050. The model results show that the incorporation of market and behavioural barriers lead to postponed adoption of zero emission technology adoption. This is reflected by the lower number of years that these alternatives are installed between 2022 and 2050. The lower adoption lead to an increase of 288% total CO2 emissions between 2022-2050, compared to the optimal solution. Underneath, the key findings from the model results are presented…

The urgency for decarbonization of the energy grid is ever increasing and fossil energy sources must be replaced by sustainable sources of energy to prevent detrimental levels of global warming. Renewable energy sources wind and solar show potential and contribute to decarbonization but are intermitted and require storage solutions that are exposed to further constrictions and costs. Nuclear fusion is regarded as the most promising new energy source since scientists first learned about it and in theory poses the ideal properties to meet the needs of the world's future energy system. Despite fusion’s potential as the ideal energy source for innovating and decarbonizing the energy system, the reality is that 70 years of research and development have not yet resulted in fusion energy on the grid. Overpromises by fusion scientists since the seventies have resulted in scepticism and cynicism towards fusion development with the longstanding quip that “Fusion energy is thirty years away…and always will be”. Nevertheless, 2021 saw many important breakthroughs and achievements in fusion research which resulted in it being titled “The best year in the history of fusion development” and led to revived optimism and claims that fusion development is moving from the lab to the grid and commercialization is closer than ever before.

In order to truly understand the process of commercializing fusion energy, the barriers need to be known and understood so that these can be addressed specifically, and the process can be accelerated. Adding to that is, that there are also numerous different technical approaches to fusion, each with their own characteristics. However, currently comprehensive knowledge of these barriers is missing. Information is highly scattered as it is focusses on specific topics of fusion development, mostly the scientific or technical barriers. Adding to that is that most information is on separate technologies or specific experiments. As a result, there is a severe lack of knowledge: it is unknown what all the barriers towards commercialization are, how these barriers differ in severity and how they differ amongst the numerous fusion technologies. In an attempt to tackle this knowledge gap and enable a better understanding of the fusion development, the objective of this research was to develop a comprehensive list of barriers and subsequently study and assess this list for the different approaches to fusion with the intention of increasing the understanding of the pathway for commercialization of fusion energy.

Before starting this endeavour, a conceptual analysis was performed to define the concepts “barrier” and “commercialization”. Using these definitions an extensive literature study was performed, alongside 23 semi-structured interviews with almost all the leading fusion institutes and companies. Using predefined selection criteria to deal with the vast amounts of information, a list of fifteen relevant barriers was identified. The barriers described in literature were complemented and extended by empirical experiences and practical examples obtained in the interviews, resulting in a manageable but comprehensive list of fusion barriers towards commercialization, including several barriers that have received very little attention to date.

In an attempt to gain further insight into these barriers and research how these are different for the various approaches to fusion, a methodology was developed to assess the barriers in a standardized way. Based on the principles of the Y-factor method developed by (Chappin et al., 2020), a customized framework was developed for the commercialization of fusion energy technologies. Each barrier was concisely described and subsequently the identified barriers were organized into five categories: Technology, Operation, Cost & Financing, Governance and Engineering. The framework assesses the barriers on a tripartite scale, scoring a value of 0 indicating no barrier, 1; indicating a potential barrier and 2; indicating a significant barrier. For every barrier the scoring criteria were detailed to allow for accurate scoring.

The five most developed and pursued technical approaches to fusion (Tokamak, Spherical Tokamak, Stellarator, Field Reversed Configuration and Inertial Confinement Fusion) were assessed using the designed framework. This was done by three sperate expert interviews. These respondents were selected because they all had high expertise of both fusion energy and experience within the fusion industry and hence contribute to the validity of the research. Analysis of the results lead to numerous interesting findings

• Barriers generally apply to all technologies: Although the difference between the fusion technologies were identified and acknowledged by the various respondents, this did not result in notable differences in the scoring of these technologies. Instead, most barriers apply in a similar severity for all technologies.

• Experts disagree on fundamental barriers: Two of the respondents disagreed strongly on the scoring of several barriers, such as “Plasma physics”, “Radiation shielding” and “Energy production”. The fact that these respondents both have a PHD in plasma physics, demonstrates the uncertainty of fusion development and underlines the complexity and difficulty of predicting the pathway of fusion technologies. In this particular case the differences mostly originated from the reasoning of the respondents; one argued more from a theoretical point of view while the other purely looked at results to date, exposing that the framework can be interpreted differently by different respondents.

• Barriers have a strong time element: The abovementioned disagreements can be partially explained by time. The application of the framework exposed that nearly all barriers are characterized by a strong time dependency and that the barrier value is heavily dependent on the timeframe it is evaluated in. Fusion technology is still under development and while an active effort was made to describe the scoring criteria as closely as possible during the synthesis of the framework, the time dependency and the interpretability that comes with it could not be eliminated

• Hierarchy within barriers: The application of the framework also exposed a certain degree of hierarchy within the barriers and found that there was an order of urgency within the barrier categories. A clear and logical pathway could be observed; firstly the “Technology” barriers must be resolved, afterwards the category of “Operation” barriers become most urgent and finally the “Engineering” category. This was substantiated by the scores as these categories received the highest scores. The remaining categories “Governance” and “Cost & Financing” are present throughout the entire innovation pathway.

All in all, the research has three main contributions. The first contribution is the identification of a comprehensive list of fifteen barriers that is validated by experts, can be used to assess all fusion technologies and captures the complete commercialization pathway. Secondly, the developed framework is the first tool that can be used to uniformly assess these barriers and compare them amongst different technologies. Finally, application of the framework increased understanding of the time-dependency and hierarchy of the barriers. Despite the limited value of the quantitative output, the qualitative findings have certainly increased understanding of the barriers and complexity of fusion energy development and showed that the use of the method can enable insightful discussions.

It should be noted that in spite of continuous attempts at safeguarding the validity of the research, there are a number of limitations that should be taken into account when interpreting the research and its results. The development of the scoring criteria is subjective and can be interpreted differently by different respondents, despite the effort to formulate these with a high accuracy and clarity. Simultaneously, because the barrier definitions and scoring criteria are newly designed, these are also constrained by the perception and interpretation of the researcher. Lastly it is important to note that the application of the designed framework was limited to only 3 respondents and the outcomes are therefore based on a small sample size. The overall results of applying and scoring the framework is greatly determined by the individual views and can’t be generalized.

This study deals with the modelling of the future global energy demand in
the transportation sector, how and in what extend this could be limited and
which low carbon fuels and technologies could contribute to the mitigation of
the energy demand. In the introduction of the current report, the greenhouse
effect, the global warming and the climate change are defined. Then, the
actions taken and the agreements made to deal with global warming and
climate change are presented and the need for combating them is highlighted.

After the introduction, a literature review is conducted in order to spot
the knowledge gap and formulate the research questions. In the literature
review, the ICCT’s and IEA’s studies are mainly discussed due to their high
quality research and the big amount of published reports. More specifically,
their models, scenarios, policies and results are discussed in detail in
order to accurately define the knowledge gap and formulate the research
questions. Thus, the goal and the main research question of this research
is to answer how could the future global energy demand be mitigated and
which low carbon fuels and technologies could significantly penetrate into
the transportation sector.

In the beginning of this research, the conceptual and the theoretical
frameworks are presented in order to assist with the outline of the thesis
and create the theoretical background for the development of the model.
Then, in order for the research questions to be answered, a forecasting model
calculating the transportation future energy demand by country, transport
mode, technology and energy carrier throughout the period 2015-2050 is developed.
The main features that distinguish this model from models used in
similar studies are the strong focus on the diffusion of low carbon fuels and
technologies, the use of a different diffusion model (the Bass s-curve) and
the country by country with one-year time increments approach.

After the model is verified and validated through comparison with similar
studies and a sensitivity analysis, results are presented for two different
scenarios. The first scenario is called Current Policies scenario and aims to
show a potential pathway of the future global energy demand in the transportation
sector that could happen if no more policies are applied after 2020
and the second scenario is called Accelerated Policies scenario and its target
is to represent a pathway that could happen if new policies are adopted and
stricter implementation is applied.

The results of the two scenarios show that it is possible to achieve a
bending of the energy demand and a diffusion enhancement of low carbon
fuels and technologies if new and stricter policies that motivate technology
improvements and fossil vehicles ban are applied. In particular, the results
indicate that the implementation of new and stricter policies, which could
lead to efficiency improvements, and to more effective diffusion of low carbon
fuels and technologies could achieve reduction of the energy demand after 2029. Moreover, according to the results, electricity is expected to dominate in the transportation sector, while biofuels, hydrogen and ammonia are also
expected to be highly used. However, without further policy action, the
global energy demand is expected to follow a constantly increasing trend
in the future while the penetration of low carbon fuels is expected to be
significantly lower.

As the complexity of global supply chains increases, so does the turbulence and disruption they are suffering from. The medical equipment after-sales service supply chain is closely related to the healthcare industry. Therefore, the ability to provide high levels of customer satisfaction with after-sales services affects not only the performance of the medical equipment companies but also the lives and health of patients. This motivated us to study supply chain resilience in order to help managers in the medical equipment industry improve the resilience of after-sales service supply chains. Through literature research, we found that there is no research specifically on the resilience of the after-sales service supply chain of medical equipment. To fill this gap, we formulated the main research question for this thesis as: “How to assess the resilience of the after-sales service supply chain in the medical equipment industry?”. To answer this research question, we design a supply chain resilience framework by using a mixed-method approach to help managers assess the resilience of medical equipment after-sales service supply chains. And we opted for a mixed-method approach in two phases: (1) Framework development: We identified and synthesize the elements of existing supply chain resilience frameworks found through literature research into a single resilience framework applicable to the after-sales services supply chain for medical equipment. This framework identifies the enablers that need to be maximized and the vulnerabilities that need to be minimized to enhance supply chain resilience and operationalizes these elements as observable indicators. (2) Framework validation: Considering the strong growth of the medical equipment market in China, and the Chinese government ambition to provide safe, effective, convenient, and affordable healthcare services to both rural and urban residents by 2020, we have validated the proposed framework by interviewing professionals working in medical equipment companies located in China. We used questionnaires and semi-structured interviews to determine how participants perceived the importance of the identified elements and indicators. We expect that the resilience framework will help managers to better deal with turbulence and disruptions. The resulting framework identifies 11 enablers and 5 vulnerabilities, operationalized by 43 indicators. As the proposed supply chain resilience framework has not been tested empirically by applying it within a medical equipment company, a first recommendation is to perform such tests. By analysing and summarizing the results of the actual application, the framework can be improved further. Moreover, the expert validation was limited due to research time constraints. The reasons why participants considered some enablers and vulnerabilities to be unimportant could be investigated further, and the indicators can still be operationalized further. Finally, the relative importance of enablers and vulnerabilities could be investigated by using a multi-criteria decision-making method, so as to help managers select only the more important elements to improve the resilience of their supply chain.

The Dutch energy-intensive industry relies heavily on fossil-fuelled energy sources for production processes, being responsible for one third of CO2 emissions in the Netherlands. The road to CO2 neutrality by 2050 requires a massive transformation of the industrial sector: sustainable supply and (re-)use of industrial heat, CO2 capture and storage and improved efficiency and circularity are some of the routes that the transition of industry may follow. In the light of the European Green Deal, a rapid mobilization of investments is required in order to reap their benefits before 2050. In this context, new forms of cooperation within energy-intensive industrial clusters have the potential to accelerate the delivery of innovative solutions. Multi-actor and technical dependencies offer synergetic possibilities that make collaborative decision-making of great importance in a situation of profound transformation. The challenge is to understand how to systematically manage and govern such inter-firm collaborations to favour the transition of industrial clusters, while balancing actor profit and cluster welfare. Formal governance mechanisms such as bi- and multilateral contracts play a critical role in this respect, regulating the distribution of costs and benefits of investment projects, while consolidating the exchange of the resulting products to reduce the uncertainty of flows. As a result, the adoption of different forms of contracts needs to be carefully considered when evaluating investment options for decarbonization. Although the transition of the energy-intensive industry has been studied from different angles, current approaches fall short in analysing and understanding the relation between the technical (hardware) level and multi-actor (and institutional) context of an industrial complex. However, integrated methods and tools are needed to identify optimal transition paths, while enabling a dynamic evaluation of investments decisions under diverse multi-actor configurations. This thesis addresses the problem by means of the following main research question: “What are the implications of implementing contractual structures in optimization models to support optimal investment decisions in the decarbonization of industrial clusters?” Answering this question requires an exploratory research design combined with a modelling approach. The initial exploratory steps involve gaining insight in the field of contractual governance using elements of Transaction Costs Economics and Game Theory. A case study analysis of the industrial cluster located in the Port of Rotterdam is conducted alongside the literature study, analysing key investment projects for decarbonization and the underlying contractual network. As part of the hard-to-electrify (and therefore hard-to-abate) sectors, the Rotterdam cluster attempts to transition to a new energy system by switching from fossil fuels to electricity, low-carbon hydrogen and green hydrogen. Thus, a focus on hydrogen as fuel and for power generation is established with respect to investments’ evaluation. As a final step resulting from the exploratory approach, hypotheses on the impact of contracts on the transition of industrial clusters are formulated, together with the experimental setup required for hypotheses’ testing. The information gathered in the exploratory steps serves as input to develop a novel methodology to incorporate contractual structures in optimization models. The output of these research steps is translated into an integrated optimization model of the Rotterdam cluster using Linny-R, a software tool developed by Dr. P.W.G. Bots that applies Mixed Integer Linear Programming (MILP) optimization. Besides the quantification of results of hypothesis testing, the model provides a proof of concept of the developed methodology to include contractual structures in the analysis of investment decisions for the energy transition. In addition, the model provides measurable insights in the effects of contractual structures on the transformation of industrial clusters, specifying the cash flow of the total cluster and its distribution among individual actors as the main metric for the evaluation of the economic performance. Optimal investment curves and resulting CO2 emissions are finally analysed as determinants of the cash flows obtained from the model experiments. As a result, implications of the implementation of contracts in optimization models are observed and discussed. As part of the research outcomes it is found that, without an explicit implementation of contractual agreements, results obtained from MILP may be characterized by a distorted allocation of cash flows, in which sacrifices of some actors in the form of negative cash flows are compensated by higher profits of other cluster’s members. These kind of results are clearly misleading in the identification of an optimal transition path. Thus, the proposed methodology adds value to MILP-based approaches. The model results show that contracts do have an effect on the transition of the industrial sector. In fact, adopting different contractual structures shifts investment decisions overtime, affecting the timescale of investments and the associated CO2 reduction. Bilateral contracts seem to yield the highest benefits for the modelled system, both from an economic and environmental perspective, as contract terms are tailored to the specific needs of the contractual parties. This makes off-takers less constrained than with multilateral contracts, whose advantages can be fully observed in thick markets. Although the findings suggest that there may not be a universal, one-size-fits-all approach in establishing a set of contracts that best supports investment projects under all possible conditions, the proposed methodology is effective in providing direction for the next steps to make decarbonization projects an investable prospect in the energy-intensive industry. More precisely, barriers identified via the implementation of contracts specify areas of need for targeted policy action, addressing the creation of greater economic incentives for the integration of investment options. With specific respect to hydrogen applications, regulated revenue support in combination with policies recognising the higher social value of low-carbon/green hydrogen products may be relevant options to take into consideration. From a managerial perspective, the methodology allows to identify and address dependencies among investment options which may strengthen or jeopardize each other. Relevant examples for hydrogen integration are the need for increased CO2 savings and/or the need to complement green hydrogen with additional measures to capture refinery fuel gases to improve the business case of such projects. Other implications consist in the need to carefully rethink the relationship between producers and off-takers to gradually adapt it to market developments. As a general conclusion, implementing contractual structures in MILP enables a more accurate representation of the behaviour of rational actors, allowing for a dynamic analysis of the system’s behaviour. Not only the incentives arising for each contracting party are taken into account in selecting optimal transition paths, but also synergies and externalities arising from the combination of technical options are optimized. This generates more valuable insights from a multi-actor and system perspective. This research suggests several research avenues that can be pursued. First and foremost, further research should be conducted to provide understanding of possible consequences of combining diverse policy interventions with contractual structures. It is possible that contracts will play a greater role in determining the revenue generation of investment options when more policies that provide specific economic incentives for decarbonization are introduced. Moreover, besides incorporating a higher level of detail and improving input data, adjustments to the proposed model could be performed to simulate a shift in contractual structures that follows the (hydrogen) market developments. Moreover, additional model expansions could allow to examine the consequences of blue and green hydrogen integration on the incumbent grey hydrogen suppliers. Finally, building upon the results of this research, the performed study could be replicated for clusters in different locations. A cross-case analysis would then reveal whether location significantly influences the identification of optimal contractual structures to support the energy transition.

In recent decades, logistic markets havebeen changing. Ecommerce is growing steadily, and the recent Covid19 pandemicgave an extra boost to that. Besides, customers are seeking more flexibility inthe logistic services. Parcel delivery is changing, yet this leads to variousnegative externalities including congestion, air, and noise pollution. Theseeffects let companies seek innovative solutions for their parcel transport. Oneof these innovations is ’crowdshipping’,parcel delivery done by the crowd instead of conventional delivery companies.By making use of existing passenger transport rather than a speciallydispatched driver to ship parcels, parcel shipping should be economically and environmentallymore sustainable. Crowdshipping is a service that has shown potential in pilotsand smallscale researches. However, strategic analyses of the impact ofcrowdshipping on all actors in the transport systems are still lacking. Thegoal of this research is to explore the interactions between travellers and parcelshipments in various strategic crowdshipping contexts and assess their impacton transport use. To achieve that, the following main research question will beanswered: ’How could sustainable crowdshipping impact freightand passenger transport use?’. A simulation model is built using agentbased modelling to explorebehaviour and simulate possible effects. This model consists of interactionsbetween four agents in the system; the customers,crowdshipping platform, travellers and occasional carriers. First, thecustomers place their orders at the platform. When travellers make their trip,they could consider carrying a parcel along their way. They notice their plannedtrip to the platform, which will calculate the optimal parcels for them. Thetravellers could opt for one of the parcels and turn into occasional carriers.The impact of crowdshipping is assessed by calculating the detour theoccasional carriers travel to deliver their parcels. Other outcomes are the providedcompensation and percentage of matched parcels, to determine the viability ofthe platform. The spatial demarcation of the simulation is most of the provinceof South Holland in the Netherlands. In this study area, 2.3 million peoplereside who order over 220,000 parcels each day. Furthermore, 4.1 million tripsare made daily by car and bicycle. Taking the willingness of both customers andtravellers into account, 13,000 parcels and 750,000 traveller trips enter themodel. Four experiments are performed to inspect system behaviour in variouscontexts. The results show that implementing crowdshipping in this study areacould be viable. The average provided compensation is lower than the priceconsignors currently pay for conventional delivery. Besides, the delivery degreeseems acceptable to get a decent level of service. Through crowdshipping, thetravelled distance in the passenger transport system will increase because ofthe detours taken by occasional carriers. This increase subsequently leads to adecrease in freight transport distance through a decreased demand inconventional parcel demand. However, the passenger transport increase exceeds thefreight transport decrease. The crowdshipping platform could limit the takendetours by making strategic choices in their implementation. This might be atthe expense of their delivery degree. It is advised for the public authority toset boundaries for the platform and stimulate strategic matching choices basedon these possible externalities. When interpreting these results, cautionshould be taken. The approach has some shortcomings regarding the spatialdistribution of detours, costs of platform’s viabilityand first leg distances for parcel transport. Furthermore, limitations could befound in the assumptions made for the simulation model. This includes theabstraction that travellers do not deviate from their planned trips andmodalities, and travellers could only carry one parcel. Another simplificationis made in the matching strategy by the platform which might have led tosuboptimal drivers for the parcels. Other limitations are caused by flawed datause. Travellers’ and customers’willingness could therefore be unreliable. Also, pedestrian and publictransport travellers are not considered due to data deficiency. Furtherresearch could be done in three ways within this field of study. First, moredata can be gathered to solve the abovementioned limitations. This includesdata on preferential routes for occasional carriers and willingness data fortravellers in all modes. Secondly, other conceptual choices can be made tooptimise the detours per parcel with forecasted travellers, or to conceptualisethe collaboration between conventional and crowdshipping delivery. Finally,research can be done to study intervention methods and corresponding legalpossibilities for the public administration to limit travellers’ detours.

The brownfield container terminal at the Maasvlakte aims at realizing a zero-emission vehicle fleet to strive for its aim to be zero-emission by 2040. Therefore, the research question is as follows: “What are the consequences for costs and operational performance of implementing mature renewable energy carriers for AGVs at the container terminals of ECT before 2040?” Literature research is carried out to find the definition for energy carriers and to find out what possible energy carriers exist to transition a fossil fuel-based vehicle fleet into a zero-emission vehicle fleet. The list of possible energy carriers is reduced by a screening, consequently, resulting in the lithium-ion battery and hydrogen as possible candidates for the vehicle fleet of ECT. Consequently, discrete event simulation is carried out to find the operational performances of these two energy carriers. Afterward, a cost calculation is done and a possible phased transition is shown.

Internet and mobile phones have become essential in our lives. Without the internet and our smart phones, large parts of our society would stop functioning or even collapse completely. It is therefore of critical importance that the service is of the highest quality and that the delivery of service is not interrupted. Telecommunication providers are responsible for exploiting and maintaining the infrastructure necessary for delivering mobile and internet connections. It is not an easy market to survive in. Competition is fierce and the clientele demands constant service of the highest quality. This is a big challenge for telecommunication providers. In order to be able to deliver the best quality, maintenance is necessary. But maintenance is often the cause of service interruption. Combined with the fact that technology is evolving at such a pace that upgrades are coming faster and faster, the demand for maintenance has never been higher. Efficiently organising these maintenance activities has therefore become a priority.