Lead time reduction of military supply chain (MSC) processes is essential for well-functioning military organisations. MSCs are complex and uncertain systems, including interdependencies between actors and processes. Flexibility is needed for MSCs to deal with complexity and uncertainty. Furthermore, reducing lead times contributes to the flexibility of supply chains. Primary and secondary supply chain processes are highly interrelated. Therefore, reducing lead times in primary and secondary SC processes is relevant.

The current scientific literature is lacking related to lead time reduction in MSCs. Besides this, supply chain management must be improved to minimise lead times in MSCs. However, insufficient empirical research exists on information sharing within supply chain management literature. Information sharing is essential for effective MSCs. Thus, the relevance of information sharing for improving SCM, combined with the lacking scientific literature on lead time reduction in MSCs, indicates a new specific research area that contributes to reducing lead times in MSCs by focusing on information sharing.

This research focuses on formal information sharing, defined as the sharing of structured and traceable information in a formal setting. Inadequate formal information sharing results in the scattering of formal information throughout a multi-actor system referred to as 'information fragmentation’. Since information sharing is a crucial component of SCs, information fragmentation may influence the lead times of SCs. However, the issue of information fragmentation in terms of lead times in MSCs is not apparent, and neither is how to solve information fragmentation problems in MSCs.

This research contributes by developing and applying an information fragmentation perspective towards lead time reduction in MSCs. A case study at the Dutch Ministry of Defence (the invoice case) is used to analyse whether the perspective is valuable in practice. The invoice case is a financial supportive MSC process encountering problems related to lead times and formal information sharing. Additionally, two types of formal information are distinguished: process in practice and process design information.

Three main results are obtained in this research. Firstly, this research contributes with an information fragmentation perspective towards lead time reduction for MSCs. Secondly, identifying a significant problem in the invoice case shows the effect of information fragmentation on MSC lead times due to non-value-adding activities. Lastly, exploring two boundary objects - a track and trace system (integrating formal process in practice information) and an integrated process design overview (integrating formal process design information) is a first contribution to the practical support of boundary objects to reduce information fragmentation in MSCs.

In further research, the proposed boundary objects can be further developed. Moreover, the effect of these boundary objects on information fragmentation and reduction of lead times can be quantitatively analysed. Furthermore, the impact of informal information sharing on lead times can be incorporated.

Climate change is an omnipresent issue in politics, industry, science, and society. Scientist knew of its existence for several decades, but only recently has the perception of it shifted from a solely environmental problem to a matter of international security. Climate change is thought to impact the likelihood of extreme weather events, intensity of droughts, rising sea levels and more. In this way climate change contributes to ongoing conflicts around the world by destroying resources or forcing people out of habitats, creating tensions among groups of people.

As the impacts of climate change are being felt more directly, calls for bolder climate action are getting louder. At the same time, there is also resistance by climate skeptics in industry, government, and among voters. Although most people appear aware and concerned with the development of climate change, the attitude towards climate action is extremely broad. Political rhetoric and disinformation in the media compound the disagreement on climate change, causing polarization. Although some polarization is beneficial for democratic deliberation and for mobilizing voters, if polarization divides the electorate into mutually distrustful groups, it can trap societies in a stifling gridlock or gradually erode democratic institutions. These pernicious effects are what necessitates closer study to monitor societal polarization, prevent conflict and improve climate action efforts.

Social media offers huge amounts of textual data to study discourse on climate change, which this study leverages to make first steps in understanding what topics make up the public discourse, how it
varies across linguistic regions (English, German, Dutch) and how it changes considering climate-related events, namely floods and a climate protest. Using both topic modelling and sentiment analysis
on Twitter data, it could be identified that activity spiked around major events: namely the 2019 Climate Strike and the 2021 Flood in Western Europe. However, reaction to events appears to be
mainly regional with reaction to floods seen only in the Dutch and German tweets. The English Twittersphere seemed to be mainly skewed towards events in America. Polarization appeared low as
most tweets were neutral. Examining only tweets with high sentiment, a different behavior was detected in terms of news coverage as there existed more high negative sentiment tweets, but almost
no high positive sentiment tweets. During the 2019 Global Climate Strike, extreme sentiment tweets went both positive and negative, possibly indicating more intense discourse and a more polarized
atmosphere.

Overall, results show that conflict-triggering climate events are very likely to be domestic events. Thus, from a security perspective, it suggests on the short-term to focus on the monitoring of domestic
actors. On the long-term however, policies need to be implemented which address underlying factors that make society susceptible to polarization, such as socioeconomic inequalities and exclusion,
disinformation in media or corruption. Lastly, from an academic perspective, more research on other dimensions of polarization needs to be conducted. Polarization is a complex phenomenon, whose full
implications cannot be captured by the sentiment metrics. Thus, research moving forward should strive towards a more refined multi-method approach to further investigate climate change polarization and its conflict potential. A better understanding could benefit practitioners in many areas such as security and social policy, for strategic planning regarding climate change.

Communicative diseases have put a burden on societal well-being throughout history. The global advent of the COVID-19 virus made the world painstakingly aware of the impact a pandemic can have on all levels of society. To combat the spread and impact of a pathogen, policymakers turn to specialized tools to craft policy. One of these tools is mathematical modeling and simulation. By using the agent-based modeling paradigm, it is possible to evaluate the impact of policy measures on the viral spread and healthcare burden. An agent-based model always contains a set of agents. Agents are the digital representations of a studied entity, for example, a person, but agents can also be an organization or a vehicle. One of the characteristics of agent-based modeling is the reliance on data to create a valid agent population. However, data on the level of individual people, also known as microdata, can be difficult to acquire and work with due to GDPR regulations. Aggregated population data, also known as open data, may serve as a substitute for microdata. Both open and microdata may be used to synthesize a population of agents, this process is known as synthetic population generation. Although both of these forms of data can be used to synthesize an agent population, it is currently understudied how the results of the agent population synthesis process are affected by the type of used population data. This research project focuses on this gap in knowledge and systematically analyzes the differences between an open-data-based and a microdata-based agent population. The populations that are synthesized are part of the HERoS model. The HERoS model is an epidemiological agent-based model that maps the spread of COVID-19 in the city of The Hague. The agents in the HERoS model are digital individuals, each characterized by a set of attributes. These attributes define the behavior of an agent within the model. Examples of agent attributes in the HERoS model are age, household size, and social role. Two agent populations for the HERoS model are synthesized, one using a sample-free algorithm that uses open data, and one using an algorithm that converts microdata to an agent population. The differences between the agent populations are quantified by introducing two new metrics, the modified Freeman-Tukey statistic, and agent matching. It was found that the agent populations show similar characteristics on a city level, but show significant differences at the neighborhood level. Furthermore, it is shown that the quality of an agent population is both dependent on the number of attributes that are synthesized, as well as the type of attributes. Moreover, the relationship between the quality of an agent population and the outcome a model produces is also understudied. This relationship is investigated by using the open-data-based and microdata-based agent populations as input for the HERoS model. The model outcomes are then compared on a global and neighborhood level, using phase comparison and the quantification of the precision of the model. It is found that the effect of using open data instead of microdata is multifaceted. The model outcome curves show similar characteristics, albeit they differ in a numerical sense. Using open-data increases the number of hospitalizations, ICU occupants, and deaths in the HERoS model. Furthermore, the precision of the model decreases when open data is used. Finally, it observed that neighborhoods that significantly differ in input population also significantly differ in model outcomes, showing the importance of the quality of an agent population for smaller resolutions. Conclusively, this research project shows that open data can serve as a viable alternative to microdata when it comes to synthesizing agent populations for epidemiological agent-based models.

The Dutch Ministry of Defence fulfils a crucial role in keeping the Dutch kingdom safe. From missions overseas to providing aid in the case of natural disasters, correct and efficient information-flow is critical for their operations. When a situation occurs, everything is dropped to make sure that they can handle it. The Royal Dutch Army, Navy, Airforce and Marechaussee make up the armed forces part of the military. Besides the branches of the armed forces, there are two support branches. Under one of them, Defensie Ondersteuningscommando, the Air Transport Unit of the Dutch military is located. This unit plans and advises on all forms of air transport for the Ministry of Defence, a unit where correct and timely information is flow critical.
This thesis attempts to answer the following research question: What are the effects of formal network structures within public organisations on their information-flow quality? Attempting to fill gaps in literature surrounding the effect of formal network structures on information-flow quality. The study defines information-flow which is used for the KPIs. The effects of formal network structures on information-flow quality are studied using an ABM model developed in this thesis. The model is tested on the Air Transport Unit network and a diverse set of randomly generated networks. In this case, key findings from the model results indicate that in the case hierarchy, a more hierarchical structure positively affects their data correctness. However, this more hierarchical structure negatively affects their information timeliness. Employee business does not seem to be affected by hierarchy. Other findings relating to the number of employees whose function it is to control data for mistakes, have significant effects on improving data correctness while at the same time, negatively affecting the timeliness of information. Indicating that the optimum, hierarchical, or less hierarchical organisation lies with the ambitions and goals of an organisation.

Big data analytics (BDA) and supply chain resilience are both present and important topics, but research on the relation between the subjects is limited. This also holds for the translation of the subject for a specific industry. This research therefore addresses this relation and translates it to a comprehensive partial resilience assessment tool that provides a benchmark for the Fast Moving Consumer Goods (FMCG) industry. The tool is based on a deterministic model that incorporated 14 resilience enablers and their corresponding interdependence. Results show that current industry BDA based resilience levels are, compared to a theoretical optimum, on average 48% and have a better practice of 66%. It is recommended that the tool is further implemented within the industry to gain more reliable and substantiated results.

Cash and Voucher Assistance (CVA), a type of humanitarian aid consisting of giving money instead of products, is being used more frequently because of its effectiveness and efficiency in helping people in need (Cash Learning Partnership, 2020b). The debate on using CVA is currently focusing on improving the quality by better incorporating ’voices’ (needs and preferences) of recipients and by enhancing targeting. In targeting it is a major challenge to quickly identify the individuals and families with the biggest needs, given the lack of data (Aiken et al., 2021). Research on ways of measuring impact on and satisfaction of recipients combined with research on demographic and behavioural characteristics of recipients could lead to deeper insights in recipients of trackable CVA modalities (evouchers and ecash). This research uses the marketing literature on customer segmentation combined with machine learning algorithms to come up with an innovative new approach of categorizing recipients of evouchers, using the case of a Red Cross project on Sint Maarten. The main research question is: How can recipients of cash and voucher assistance be categorized using the field of consumer segmentation by using machine learning methods? The objective of this research is to come up with new methods to better understand recipients of CVA. Theories on customer segmentation pointed to the use of data-driven clustering methods to categorize consumers. Combined with a framework of recency, frequency and monetary aspects, recipients of evouchers could be categorized effectively. A required addition to this clustering method is to use a dimension reduction technique to avoid the negative consequence of the curse of dimensionality. Therefore, a two-step approach of dimension reduction and clustering has been applied in this research. It has been found in this research that a factor-cluster approach can lead to insightful clusters using geo-demographic data and behaviour data. Factor analysis has been used to reduce the dimensions while the k-prototype algorithm has been used to cluster into five distinct groups of recipients. The geo-demographic variables that were the most determining in characterizing distinct clusters consisted of: the age of the main beneficiary, the different household compositions and of a constructed factor ’big families and big receivers’. The most distinguishable variables on behaviour were: the number of supermarket visits (frequency), the time between the first voucher was received and the first transaction (recency) and the variables on the amount of money that was spent with the vouchers (monetary). To be able include the ’voice’ of recipients (needs and preferences), a connection between the registra tion data, behavioural data and survey data is needed. In this research only an exploratory connection could be established, due to the lack of a common identifier between the survey data and the other datasets. However, one crucial finding of this research is that it seems like the combination of these data sources can give meaningful insights in the needs, preferences and behaviour of households of Sint Maarten. With these insights specific clusters can be targeted for additional assistance, based on their needs. Recommendations for future studies include studying the validity of the found cluster results with different validation indices on cohesion and compactness, and by using simulations to determine the cluster stability. Before this factor-cluster approach can be deployed in CVA projects, more research on the treatment of limitations of this approach needs to be conducted. This is critical in communi cating the conditions and constraints of this model to humanitarian aid workers in the field. Another recommendation is to improve the design of surveys to measure the needs of recipients. For insightful factor-cluster results on the needs of recipients, survey data should be linked to geo-demographic and behaviour data. More research on including clusters in retargeting methods using feedback loops have a large potential in minimizing targeting errors and more effectively meeting the needs of recipients. With this research, the humanitarian sector can benefit from new ways to understand the needs of the most vulnerable in need. Decision-makers should build upon the feedback of recipients and move towards a new era of humanitarian assistance.

Global climate change has results in a higher frequency of extreme disaster events and is therefore a serious challenge in disaster impact management. Disaster risk is composed of several components, such as vulnerability, susceptibility, exposure and the probability of occurence and intensity of a hazard. Vulnerability has become a topical issue due to the major role it plays in disaster risk reduction strategies. Hence, this research focuses on the development of a method to understand the dynamics of social vulnerability. The study area comprises Burkina Faso. Therefore the following research question was developed: 'how to calculate a social vulnerability index for Burkina Faso that characterizes the spatial and temporal dynamics of social vulnerability"?"

The results showed that despite drawbacks, principal component analysis provides good insight in de internal and externaly dynamics of social vulnerability. However, large differences are found in the ranking of the social vulnerability score of communes when other methods are used. Hence, it is deemed important to develop more research in the semantic meaning of social vulnerability an thus understand better which mathicmatical approach is the most suitable.

This research has found the highest social vulnerability in communes prone to conflict which are hosting many IDPs in the North, Centre-Nord, Sahel and East of the country. A statistically significant increase of social vulnerability was found from 2015 - 2017 in Boucle du Mouhoun, the Nord and the Centre-Nord.

During the battle against COVID-19, sudden-onset disasters keep happening. These two consecutive events are different in nature and sometimes ask for counter-productive policy interventions: where the epidemic can be contained by ensuring social distancing practices, reducing the number of contacts, and offering hygienic conditions, the response to a natural hazard requires evacuation, which often leads to overcrowded shelters with a questionable hygiene. An exploratory agent-based model is constructed where the dynamics of these socio-technical systems are integrated to find high-level emergent behaviour over time during the response phase of a sudden-onset disaster. The study describes a stylistic agent-based model that combines three socio-technical systems, their interactions and system behaviour to find general trends and interdependencies. The three sub systems comprise of a COVID-19 component, based on the SEIR approach, a livelihood component, which mimics a microeconomic marketing mechanism and accounts for among others difference in occupation, and a hazard component, which is responsible for the evacuation process. The following research question is answered: What robust policy interventions can be identified that balance livelihood of rural communities and their exposure to COVID-19 during the response to a sudden-onset disaster in developing countries? The goal is to find robust policy interventions that can be executed by either local governments or humanitarian organizations and are suitable for poor rural communities in developing countries. Four policy interventions were compared: cash transfers, an awareness campaign, increasing the number of shelters, and varying the moment of imposing a lockdown. With the use of Exploratory Modelling and Analysis, an experimental design was constructed to identify uncertainties and model behaviour. Results show that using direct and unconditional cash transfers in combination with increased awareness have most beneficial effects for the average livelihood and also positively influence the COVID-19 trajectory. Increasing awareness shows best results for containing COVID-19, but is dependent on context factors such as regular testing and implementation before the the virus spirals out of control. Awareness also negatively influences the average livelihood. This research is a first step in modelling compound risk of COVID-19 and sudden-onset disasters. The modular way of building makes this research useful not only for searching trade-offs in developing countries, but can be used in other contexts as well.

Since the outbreak of COVID-19, far-reaching measures were introduced to isolate people and reduce the chance of transmission among people. In the Rohingya refugee camps in Bangladesh, the camps were locked down for months. As a result, economic dependencies between the refugees and host communities which were built in previous decades were cut-off for months. So far, no research has been published on how to release the burden of limited refugee-host interactions, which can be a vital source of livelihood generation. In this research, the implementation of different vaccine allocation strategies were analysed and compared, to analyse what prioritization strategy can best be introduced to most effectively revive these interactions and control the COVID-19 outbreak. Results show that either prioritizing elderly or high-risk transmission groups can be effective. Prioritizing elderly distinguishes itself as it most effectively reduces severely & critically symptomatic infections. Prioritizing transmission-groups shows maximize refugee-host interactions. When combining both strategies with a dynamic approach of opening/closing the camp from host communities, a more effective strategy is possible for protecting the vital economic functions of an open-system refugee camp. An implication of this research is reflected by the granularity of the model. As the model has a high resolution and is highly computational, insufficient model experiments could be conducted to cover a complete solution space of the model. However, as this research proposes unique study in the dynamics between vaccine allocation and open-system refugee camp, a foundation is grounded for further research.

Development Finance Institutions have been established to provide finance for long-term economic development in low and middle-income countries. However, these investments are exposed to climate-related risks. This means that Development Finance Institutions need to explore climate adaptation interventions, particularly in view of the fact that developing countries themselves have few resources for climate adaptation. This thesis aims to establish how, using an agent-based model, Development Finance Institutions can achieve long term investments by financing climate adaptation, while creating societal benefits and financial returns. This implies positively contributing to society, for example by creating jobs, generating a stable income, and supporting the resilience of households, while securing profitability for development banks. An investment by the Dutch Development Bank FMO in the cocoa sector in Ghana serves as a case study. Three main steps are followed in the study: (1) translating global climate scenarios at the local level; (2) capturing local behavior in a bottom-up model because understanding the specificity of the local context and individuals’ response is key to really make an impact; and (3) testing climate adaptation interventions. Downscaled climate scenarios are used as input in an agent-based model capturing the behavior of Ghanaian cocoa smallholders. Experimenting with climate adaptation shows that Development Finance Institutions have to make trade-offs in terms of priority, timing, and activity. This step-by-step approach could encourage DFIs to capture the impact of climate change and the dynamics at the local level, and to design tailored interventions. It is also a way to take a holistic perspective and look at the broader picture of economic and societal impacts of climate change, and the response across the whole value chain.

In 2018, the number of forcibly displaced people all over the world was higher than ever before. More than a third of this group are refugees, often living in refugee camps.  NGOs try to deliver all necessary aid to these camps, while facing limited access and resources. Therefore, they want to optimize the results of the aid they deliver. The structure and layout of a refugee camp cannot simply be adapted, as it has evolved over time. However, this path-dependent nature of the camp expansion has not been studied from the perspective of the refugees yet. This study combines the process of camp expansion with the location decisions for healthcare facilities in expanding refugee camps. In an agent-based model, the camp expansion is simulated according to the settling preferences of refugees. These preferences are proximity to roads, to other shelters and to healthcare facilities. The locations of healthcare facilities are determined using two optimization algorithms. By using predictions about future camp expansion based on the settling preferences of refugees, the healthcare providers can adapt their locating decisions to the settling preferences of refugees. Simultaneously, the placement of new facilities designed for expected camp expansion can affect the settling choices of refugees. The success of the optimization is measured in the resulting accessibility of healthcare facilities in refugee camps. First, it is found that the facility location optimization improves when future camp expansion is taken into account, if refugees can choose a location to settle upon arrival in a refugee camp. On the contrary, if refugees cannot choose a location to settle, predictions about camp expansion should not be included. Second, in case a space restriction of 45m² surface area per person is maintained in a refugee camp, it is recommended to locate healthcare facilities using an algorithm that minimizes the travel distance between refugees and healthcare facilities. However, if the available surface area per person is significantly lower than 45m², it is recommended to locate healthcare facilities in a refugee camp using an algorithm that maximizes the coverage ratio of shelters in the camp. Finally, when shelters are spread evenly over the camp, the possibility to locate healthcare facilities in strategic places enhances. This improves the accessibility of healthcare facilities for all refugees.

Access to clean energy has long been neglected within the humanitarian agenda. As a result, refugees are often locked into unsustainable and risky energy practices, especially in low-income countries. This study focuses on market-based interventions to deliver clean cooking fuels in refugee camps in protracted crises. The outcomes of market-based interventions are highly dependent on whether beneficiaries adopt and continuously use the products. However, clean cooking practices often face various adoption barriers. The aim of this study is to gain insights into the mechanisms and path-dependencies driving the adoption of clean cooking fuels in refugee camps and, by taking a modelling approach, to develop a method to analyse the effect of market-based clean cooking interventions. To this end, this study combines agent-based modelling with exploratory modelling techniques to capture social interactions, human decision-making behaviour, and deep uncertainties. The model formulation is grounded in a case study of a Rwandan refugee camp, and Diffusion of Innovation theory. Different interventions, including financial assistance for fuel purchase (cash transfers or vouchers), information campaigns, and maintenance activities, are simulated under a wide range of scenarios. Scenario discovery is applied to identify circumstances for success or failure of interventions. For both types of financial assistance, supporting information campaigns and maintenance activities are critical to create robust, timely, and long-term impact. Based on the findings, policy recommendations and guidelines for future interventions are derived.

As Needham (2015) concluded based on researching 700 storm surge events: “tropical cyclone induced floods are among the world’s deadliest and destructive natural hazards”. Due to this growing threat, there is a growing need for evacuation policies in case of an impending cyclone, in order to reduce the negative effects of a cyclone. The literature review in this research points out that there are many pre-disaster evacuation models, which focus mostly on either small scale evacuation of a city, or large scale evacuation with the use of motorized vehicles. However, in stretched out rural and developing areas, where access to motorized vehicles is limited or non-existent, residents need ample time to leave the area that is in danger of cyclone induced floods. This means that there should be enough time between the issue of the evacuation order and the moment the cyclone is forecasted to make landfall. However, the sooner the evacuation order is issued, the more uncertainty there is regarding the area that may be subject to the devastating consequences of the cyclone. That means that many residents would be unnecessarily evacuated. This is why the moment of the evacuation order should be at a point where the timeliness of the evacuation order and the uncertainty of the cyclone are optimally balanced. Currently, there does not exist a pre-disaster evacuation model for cyclone-induced floods that accounts for this trade-off. This research uses concepts of existing evacuation models and extends them with the trade-off between timeliness and uncertainty reduction in order to research the added value and applicability of this trade-off. This leads to the following research question: How to balance the trade-off between timeliness and uncertainty reduction in making shelter location decisions in rural and developing areas, under impending cyclone induced floods, while accounting for behavioral aspects of the vulnerable residents? The computer model that is developed to answer the research question consists out of three parts, which are called the building blocks of the model. The first building block translates discrete forecast reports about a cyclone into an area that is vulnerable and should be evacuated, because it will possibly be affected by the cyclone. These forecast reports form the decision points for an evacuation moment. Based on this vulnerable area, possible shelter locations are found in the surroundings of (horizontal evacuation), or on higher grounds within (vertical evacuation), the vulnerable area using a shelter searching algorithm. This algorithm uses a certain safety margin, which is the distance between the possible shelter locations and the vulnerable area to look for possible locations that can shelter the evacuees. The second building block is the optimization part. This sub model optimizes over the complete set of possible shelter locations and selects a given number of shelters that minimizes the weighted distance between the evacuees and the shelters. This weighted distance minimization is also known as the Minisum optimization model (Boonmee, 2017). The third and final building block simulates an evacuation using the previous generated data and captures the results in key performance indicators, so that the effect of the different policy levers can be compared and the balance between timeliness and uncertainty reduction can be found. The models are connected to each other using a Python interface. The model is applied to two different case studies, on which the results and conclusions are based. Three levers that define the shelter location decisions are found relevant in balancing the trade-off between timeliness and uncertainty reduction. The first is the evacuation moment. It shows that a later evacuation moment reduces the number of total evacuees, but it also reduces the evacuees that are saved from the impact of the cyclone. More precisely, the model shows a clear break point, which means that there is a point in time after which it is no longer possible to evacuate all evacuees in danger. The two case studies have shown that this break point is around two days in advance of landfall. This means that the balance in this trade-off lies before this break point. Too early evacuations however, result in a high number of total evacuees, which is not desired as well. This reduction in evacuees over time is not always linear and depends on how the cyclone is forecasted and the characteristics of the geographical area that is under threat. Therefore, it can be concluded that evacuation should happen before the break point, but the exact moment also depends on the forecast reports and the geographical terrain, and is also dependent on the other two levers. However, it is shown that vertical shelter locations significantly reduce the evacuation time and enable later evacuations or evacuations with less shelters. The second lever is the safety margin. This research concludes that a relatively high safety margin is advised in early evacuation moments, but in later evacuation moments it is advised to make use of vertical shelter locations, which means that a low safety margin should be used. The low safety margin is the only way, in later evacuation moments, to save as many evacuees as possible, but it also reduces the accessibility and security of the shelter locations. The third and final lever is the number of shelters. In early evacuation moments, there are many evacuees, which increases the need for sufficient shelters. Therefore, in early evacuations, it is shown that additional shelters have a relatively high reduction in travel distance and high increase of rescued evacuees when compared to later evacuation moments. However, the marginal benefit of an extra shelter is reduced with each additional shelter, which means that the cost of each additional shelter should be balanced against the reduction in travel time and the increase in safely evacuated evacuees. Furthermore, when a distance minimization model is used, the largest shelters will be located closest to the areas with the highest population density. Regarding the sizes of the shelters, later evacuation decisions often means there is need for more shelters. This means that those shelters tend to be smaller, but there will always be larger shelters because of the larger cities. In summary, three policy levers have been identified that define the shelter location decision and that have an impact on the balance between timeliness and uncertainty reduction. None of these levers can single-handedly define how the right balance, and they should therefore be used all-together to define the best balance the trade-off. However, it has also been found that in both case studies the cyclone evolved differently and the geographical area is far from identical as well, which also influences the right balance. This means that every answer about how to balance the trade-off, will also be different in every case. Additionally, it is concluded that the trade-off between timeliness and uncertainty reduction is especially relevant for evacuees who are evacuating by foot. When their travel speed increases, the relevance of the trade-off decreases. This confirms the hypothesis that most evacuation models with motorized vehicles do not account for this trade-off because evacuees have a higher travel speed. Furthermore, this research concludes that when a cyclone is advancing and there is no time to deploy an evacuation model, a heatmap of the population density, together with an elevation map, can give rough estimates of where the largest shelters should be located. The elevation map gives insights into the possible shelter spots because it will point out the elevated locations, either within or outside of the estimated vulnerable area. Those spots that are located closest to the most dense populated areas will probably prove to be suitable shelter locations. Furthermore, in both case studies it is shown that the latest evacuation moment is around two days in advance of landfall of the cyclone and that after that moment it is highly advised to make use of vertical shelter locations. To conclude, this research recommends that the evaluation of the model results will be changed from retrospective to prospective, which means that the model can be deployed in real-time disaster management. Only then, the real value of the model can be shown.

Situation: A large portion of the beneficiaries of humanitarian aid have little to no proof of their identity, this is especially the case in African countries, in which humanitarian intervention is common. In order to facilitate aid to these individuals, humanitarian organizations (HOs) leverage their on-the-ground capacity to create identity profiles and risk assessments of these people. However, in-kind aid is being increasingly replaced by Cash Transfer Programs (CTPs). In CTPs, beneficiaries are provided with funding to self-procure their necessities. Complication: For efficient CTPs, these humanitarian identities need to be accepted beyond the boundaries of humanitarian aid. However, by relying on traditional identity management systems, HOs expose the beneficiaries to security and function creep risks. In order to share beneficiary identity information in a more responsible way, HOs have started to leverage Self-Sovereign Identity (SSI) systems. These SSI systems need to be scaled up to a more foundational nature in order to facilitate private-sector services. For this a collaboration with national public- and private stakeholders is required. In order to realize that, there is a need for a process design. Which among other things ensures support from crucial stakeholders. Approach: This study used a Design Science Research inspired approach, combined with a Systems Engineering perspective to explicate the problem, define support driving and constraining circumstances and conditions, generate principles for the humanitarian sector to create support driving circumstances and conditions in Kenya and validate these principles using the input of industry experts. This research approach had a focus of practical insights over theoretical insights. Results: A set of five validated support nurturing principles were established with which humanitarian organizations can nurture support for a more foundational humanitarian SSI system in Kenya. Additionally, a framework has been composed with which local circumstances and conditions in a country can be assessed. Next steps: Further research should focus on establishing a more complete process design for a collaboration process, which also deals with participation of stakeholders, structures commitment and defines process rules for different phases of the process. Additionally, further research should explore the capacity and willingness of beneficiaries to control their own identity. And finally, the effect of international innovation initiation on the willingness of national stakeholders should be explored.

During the next few decades, a significant increase in the use of intermittent renewable energy sources is expected in the Netherlands as well as a general increase in electricity consumption. Due to the increased demand as well as a more uncertain, volatile supply, substantial upgrades and redesign of the current Dutch electricity grids are needed. These upgrades will inevitably require large investments as the design and installation of electricity grids is costly. Additionally, the investments are lumpy and irreversible. It is therefore important that the investments will be made in such a way that the future grids will operate successfully, supplying consumers with the demanded electricity at sufficient quality with a low rate of interruptions. Recently, the Netherlands has been divided into 30 energy regions, which allows the Netherlands to work on its climate agreements both from a regional and from a national level. These regions will work on generation and consumption of electricity and heating as well as on the energy infrastructures needed to supply this energy. This aim of this research paper has been to create a method that can be used to design suitable electricity distribution networks for the energy regions in the Netherlands. One approach to designing electricity grid topologies is with the use of graph theory heuristics, which has shown to be a useful way of approaching the electricity design problem by discretising plots of land into a graph. The research paper has shown that by taking into account spatial constraints specific to the Dutch regions, more valid networks can be created. This further leads to increased implementability of the final networks in addition to a reduction in the possibility for unforeseen costs related to building on certain plots of land. The proposed method aims to minimise the investment costs of the future regional electricity distribution networks in the Netherlands, taking both cable lengths and capacities into account. A radiality constraint is applied, ensuring that the network is connected but does not contain any cycles. A flexible way of ensuring that the final networks do not overlap with unavailable land is thereafter applied and demonstrated. A heuristic method aiming to minimise network length is applied before assigning the required capacities to the network. An improvement procedure is performed in order to further reduce investment costs. The cost function is formulated as a non-linear function, incorporating the characteristic that savings can be made by combining lines in order to create a shorter, high-capacity network instead of a longer, low-capacity network. The proposed method has thereafter been verified with respect to the problem formulated and demonstrated using a case study on the energy region Goeree-Overflakkee. Experimental results have also been generated in order to assess the effectiveness of the method. In comparison to an alternative simultaneous topology and production optimisation, it has been found that the proposed method leads to a shorter final network that additionally leads to lower total investments costs.  

The frequency of occurrence and the size of natural disaster is increasing, with more people to beaffected in the coming years. The impact is huge, with major infrastructures being damaged andaccess to basic needs cut off for the disaster struck regions. The affected regions and communitiesare dependent on the delivery of disaster relief aid, by governments and non-governmental orga-nizations. The delivery can be difficult, while airports, harbours and other infrastructure is oftendamaged from the disaster.The timely delivery of relief aid to the affected communities is a major challenge of today. Theairports used for the distribution of relief items are often unable to handle the shear size of theinflux of these items. The resources are damaged and personnel might not be able to show up forwork. In order to reduce the overexertion of the local airport, an airbridge can be made with aregional airport. The regional airport can then be used as a buffer, for bundling and storing reliefaid, until it can be delivered to the local airport. The system in which the regional airport receivesthe international contribution of relief items and sends them in a moderated flow to the local airportis called the airport relief chain.The relief chain is subject to several bottlenecks, but due to scarcity in research, the influenceon airport relief chains had not been identified previously. Reducing these bottlenecks and im-proving the relief chain can be achieved by improving the processes on individual airports, as wellas by altering small features in the structure of the relief chain itself. The research question isconsequently:How can the impact of bottlenecks on an airport relief chain system be decreased,considering the airport operations in a fixed network design?To be able to confirm the bottlenecks and investigate possible solutions, a simulation model wasbuilt for the airport relief chain system. To obtain relevant outcomes, data of a real world casestudy was applied to the simulation model. The case study of choice is the earthquake followed bya tsunami in September 2018, that struck the island of Palu, Indonesia. In this disaster, an airportrelief chain system was created, in which the delivery to Mutiara on Palu, serving as the local air-port, was provided through Sepinggan airport on Kalimantan, serving as the regional buffer airport.To this end three bottlenecks were identified for the airport relief chain. namely: (1) The air-craft arrival process at the regional airport, (2) The cargo handling after unloading of the aircraftat the regional airport, and (3) the airbridge connection between the local and regional airport. Thebottleneck at the airbridge is the most stressing, absorbing most of the cargo. Even when the otherbottlenecks are resolved, without improvement in the airbridge the performance will eventually belimited.With the airbridge as a major constraint in the airport relief chain system, the delivery of re-lief items can not be larger than the throughput of the airbridge. The bottlenecks identified at theaircraft arrival and the regional airport processes reduce the flow of relief items even further. Thepolicies investigated are meant to maximize the performance up to the airbridge, creating maximalperformance within the constraints of the system.The performance was measured through the performance indicator, namely the timely delivery.ii
The timely delivery consists oftotal deliveyand theshare of priority demand delivered. The perfor-mance of the system as a whole can as such be evaluated, yet it lacks detail for the implementation ofpolicies. In order to improve the system, the bottlenecks had to be identified, for which the bottle-neck identification measures were introduced. These consist ofidle cargoandthroughput time. Theidle cargo is generally a notification for the occurrence of a bottleneck, with high average idle cargomeaning a bottleneck occurs. The throughput time here indicates whether it was very impacting,as high numbers for throughput time generally mean less impact on the overall component over time.The individual policies were ineffective to truly improve the airport relief chain as a whole. Severalpolicies proved to be quite effective at reducing bottlenecks, thus improving the timely delivery.However, only small improvements were seen in the total delivery, due to the downstream occur-rence of other bottlenecks. Theshare of priority demandalone could be raised effectively throughprioritization policy. Those policies would not provide further system improvements, making theiruse even more case specific.In order to improve on the timely delivery indicators, which includes the total delivery, combina-tions of inventory management policies and airport policies were examined. Based on the individualperformancesdestination prioritizationthrough use of off-airport storage anditem prioritizationon the most stressing relief type were chosen as the inventory management policies. These, if pairedwithadditional workerspolicies, were able to absolve the bottlenecks in the system up to the air-bridge. The combinations as such performed at the optimal range, considering the final constrainton the system and are preferable for implementation.The prioritization policies were the most useful, but require staff and their equipment to fullyperform. As such the processes in an airbridge system for the provision of relief items should con-sider the capacity at the regional airport and optimize the processes accordingly up to the airbridgeitself.

In an emergency, humanitarian organisations share information to prevent redundant data collection and avoid gaps and overlap in the relief activities that they undertake. An analysis of hygiene kit distribution in the Bangladesh-Myanmar displacement crisis and consultation of both literature and humanitarian professionals led to the construction of a model on information diffusion in complex emergencies. This model proved to be able to evaluate strategies that have a level of complexity that could not be apprehended by existing models. Experimentation with this model leads to the conclusion that a locally sourced team, with an outward focused organisation that produces near real-time information products, is most effective in diffusing information.

Infectious diseases are responsible for about one quarter of all deaths worldwide and it is likely that major epidemics will emerge in the near future. Through the airline system, diseases can spread around the world rapidly and are more and more likely to do so. At the same time, air connectivity is essential for countries with inadequate land-based infrastructure, in particular during an epidemic when incoming humanitarian assistance is essential. This illustrates the trade-off of passenger airline operations during an epidemic: the likelihood of further spreading of the disease versus the role in combatting that same disease. Policies that prevent infectious diseases from spreading are in place, but knowledge of their effectiveness and impact on the passenger logistics is limited.

An agent-based modelling study, which shows the impact of predefined policies on a stylized case study of Syria.

Airports are a special critical infrastructure in a disaster response from a resilience point of view. They must overcome two types of shocks when they are affected by a disaster. They are not only hit by a disaster, which decreases their performances. Airports also need to act as a humanitarian and logistic hub for the disaster response which leads to an influx of aircraft. This influx requires the airport to increase their normal performance. In this research a mesoscopic model is built to compare different policies that can improve the resilience of the operations of airports during the immediate post disaster response.

To compare the effect of the policies on the level of resilience of the system, this study proposes a new resilient measurement approach that incorporates the bounce back and bounce up capacity of a system. This approach divides resilience into three aspects (1) absorption capacity, (2) adaptive capacity and (3) recovery time. This resilience measurement approach can be used when a system has the following two characteristics: (1) dynamic required service levels over time and (2) internal system changes.