A. Verbraeck
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1
Coordinated Infrastructure Improvements for European Military Mobilisation
A Network Analysis approach to optimise corridor‑level infrastructure upgrades for European military mobilisation
The ability of armed forces to rapidly deploy personnel, equipment, and supplies across Europe has become increasingly important in response to the deteriorating international security environment, following Russia's large-scale invasion of Ukraine in 2022. Despite renewed political attention and investment, large-scale military mobility through Europe remains constrained by a combination of fragmented governance and inadequate transport infrastructure. Rather than evaluating individual infrastructure objects in isolation, this thesis develops a network-based approach to identify and prioritise infrastructure upgrades according to their contribution to overall network efficiency, under military mobility constraints. The results show that improving military mobility is not about spending more, but about making smarter choices. With only a limited number of targeted interventions, Europe can greatly strengthen its military mobility, and with it its deterrence and security. In the end however, the challenge lies not in reinforcing the infrastructure itself, but in prioritising investments in such a way that strategic necessity algins with political reality.
...
The ability of armed forces to rapidly deploy personnel, equipment, and supplies across Europe has become increasingly important in response to the deteriorating international security environment, following Russia's large-scale invasion of Ukraine in 2022. Despite renewed political attention and investment, large-scale military mobility through Europe remains constrained by a combination of fragmented governance and inadequate transport infrastructure. Rather than evaluating individual infrastructure objects in isolation, this thesis develops a network-based approach to identify and prioritise infrastructure upgrades according to their contribution to overall network efficiency, under military mobility constraints. The results show that improving military mobility is not about spending more, but about making smarter choices. With only a limited number of targeted interventions, Europe can greatly strengthen its military mobility, and with it its deterrence and security. In the end however, the challenge lies not in reinforcing the infrastructure itself, but in prioritising investments in such a way that strategic necessity algins with political reality.
Inferring earlier system-state pathways from a target condition
Developing, implementing, and evaluating a DBN-inspired backward inference methodology using a reduced residential EV charging case study in Norway
Infrastructure planning problems are typically guided by long-term target states, but it is not always clear which earlier system conditions are necessary to reach those targets. Backcasting methods enable policymakers to reason backward from a desired future target state back to the present. Although backcasting literature is well established, the actual backward inference step is often the least formalised, or carried out in a non-formalised or intuitive way. As a result, policymakers lack methodological support to infer earlier system-state pathways in a systematic and reproducible way. This thesis addressed this problem by developing, implementing, and evaluating a DBN-inspired backward inference methodology for reduced infrastructure system. It starts from a specified target condition, and works backward to infer admissible earlier system-state pathways. The proposed methodology is operationalised through a reduced case study of residential electric vehicle charging in Norway. The results show that the methodology can produce multiple admissible pathways in a structured and explicit manner. However, the results are limited to the context of the reduced case study, and follow-up research is needed to test the methodological appropriateness in cases where no feasible pathways are identified, possibly on larger and more complex infrastructure systems with a longer time horizon and other inference techniques.
...
Infrastructure planning problems are typically guided by long-term target states, but it is not always clear which earlier system conditions are necessary to reach those targets. Backcasting methods enable policymakers to reason backward from a desired future target state back to the present. Although backcasting literature is well established, the actual backward inference step is often the least formalised, or carried out in a non-formalised or intuitive way. As a result, policymakers lack methodological support to infer earlier system-state pathways in a systematic and reproducible way. This thesis addressed this problem by developing, implementing, and evaluating a DBN-inspired backward inference methodology for reduced infrastructure system. It starts from a specified target condition, and works backward to infer admissible earlier system-state pathways. The proposed methodology is operationalised through a reduced case study of residential electric vehicle charging in Norway. The results show that the methodology can produce multiple admissible pathways in a structured and explicit manner. However, the results are limited to the context of the reduced case study, and follow-up research is needed to test the methodological appropriateness in cases where no feasible pathways are identified, possibly on larger and more complex infrastructure systems with a longer time horizon and other inference techniques.
Tracing Pathogen Through Airport Wastewater Sampling
Designing Airport Wastewater Sampling Networks for Infectious Disease Detection through a Mixed-Integer Optimisation Model
Airports are critical entry points for infectious diseases, and wastewater sampling offers a scalable way to monitor pathogens carried by arriving travellers. At Amsterdam Airport Schiphol, sampling currently takes place only at the wastewater treatment plant, which pools all arriving flights and cannot attribute a detected pathogen to a specific flight or origin. While the literature has shown that aircraft and terminal sampling can provide origin specificity, no study has formalised the selection of sampling points within a single airport. This thesis addresses that gap through the research question: How can the wastewater sampling network at Amsterdam Airport Schiphol be designed and optimised to maximise the detection probability of infectious diseases while supporting attribution to flights and staying within operational and resource constraints?
The problem is formalised as a generic mixed-integer non-linear programme that selects among four node types (aircraft tanks, lavatory trucks, concourses, and the treatment plant) to maximise attribution-weighted detection capacity across all arriving flights subject to a budget constraint. A probability chain connects the epidemiological situation at each flight's origin to a detection event, combining per-node probabilities through a conditional noisy-OR formulation. The model is implemented in Python with Gurobi and applied to Schiphol using data from a representative busy day under three disease scenarios, with an extensive sensitivity and robustness analysis.
The central finding is that there is no single optimal network. The configuration that maximises detection capacity depends on how disease risk is distributed across arriving flights. For the broadly distributed SARS-CoV-2 base case, maximum coverage through concourse and lavatory truck sampling is optimal: detection capacity reaches 40.07 at budget 15,000 using all seven concourses and all 140 truck nodes, with no aircraft nodes selected. Spending the first 15,000 cost units on trucks and concourses gains 38.63 in detection capacity compared to only sampling the treatment plant, while adding aircraft nodes between budget 25,000 and 75,000 gains only 0.74. For the rare and geographically concentrated Hantavirus scenario, with a single at-risk flight from Argentina, the strategy differs. A single aircraft tank node targeting that flight achieves 5.08 x 10^-6 at budget 10,000, a 33% improvement over the combined truck, concourse, and treatment plant configuration. The same model, applied to the same airport on the same day, recommends opposite strategies depending only on the disease.
A comparison against a greedy benefit-cost heuristic shows that exact optimisation adds value precisely where it matters most: for SARS-CoV-2 the two approaches produce identical configurations through saturation, but exact optimisation outperforms greedy by 33% for Hantavirus and 7% for a concentrated outbreak. The optimal configuration is also robust across most sources of uncertainty, remaining invariant to a x0.1 to x10 scaling of infection probability and stable across six peak-season days. The main limitation is that the model evaluates a static 24-hour window and therefore optimises detection probability rather than detection timing. Extending it with a temporal layer, forward-looking prevalence estimation, a finer haul classification, and a multi-pathogen formulation are the most valuable directions for further research.
The findings translate into concrete guidance for surveillance practice. Moving beyond the current treatment-plant-only setup does not require large investment: adding all seven concourse nodes raises detection capacity from 1.44 to 9.46, a 6.6x improvement, at a cost of only 71 relative units and with no airside access. For routine surveillance of a broadly distributed disease, lavatory truck sampling should form the backbone of the network, and aircraft sampling is not cost-justified. Aircraft sampling should instead be held ready as a targeted capability for rare or geographically concentrated threats. The optimal network is not a fixed installation but a configuration that should be switched by scenario, so the operational value lies in retaining the flexibility to reconfigure it. Because the configuration is sensitive only to the cost ratios between node types, establishing the true relative cost of node types at a given airport is the parameter most worth validating before deployment. ...
The problem is formalised as a generic mixed-integer non-linear programme that selects among four node types (aircraft tanks, lavatory trucks, concourses, and the treatment plant) to maximise attribution-weighted detection capacity across all arriving flights subject to a budget constraint. A probability chain connects the epidemiological situation at each flight's origin to a detection event, combining per-node probabilities through a conditional noisy-OR formulation. The model is implemented in Python with Gurobi and applied to Schiphol using data from a representative busy day under three disease scenarios, with an extensive sensitivity and robustness analysis.
The central finding is that there is no single optimal network. The configuration that maximises detection capacity depends on how disease risk is distributed across arriving flights. For the broadly distributed SARS-CoV-2 base case, maximum coverage through concourse and lavatory truck sampling is optimal: detection capacity reaches 40.07 at budget 15,000 using all seven concourses and all 140 truck nodes, with no aircraft nodes selected. Spending the first 15,000 cost units on trucks and concourses gains 38.63 in detection capacity compared to only sampling the treatment plant, while adding aircraft nodes between budget 25,000 and 75,000 gains only 0.74. For the rare and geographically concentrated Hantavirus scenario, with a single at-risk flight from Argentina, the strategy differs. A single aircraft tank node targeting that flight achieves 5.08 x 10^-6 at budget 10,000, a 33% improvement over the combined truck, concourse, and treatment plant configuration. The same model, applied to the same airport on the same day, recommends opposite strategies depending only on the disease.
A comparison against a greedy benefit-cost heuristic shows that exact optimisation adds value precisely where it matters most: for SARS-CoV-2 the two approaches produce identical configurations through saturation, but exact optimisation outperforms greedy by 33% for Hantavirus and 7% for a concentrated outbreak. The optimal configuration is also robust across most sources of uncertainty, remaining invariant to a x0.1 to x10 scaling of infection probability and stable across six peak-season days. The main limitation is that the model evaluates a static 24-hour window and therefore optimises detection probability rather than detection timing. Extending it with a temporal layer, forward-looking prevalence estimation, a finer haul classification, and a multi-pathogen formulation are the most valuable directions for further research.
The findings translate into concrete guidance for surveillance practice. Moving beyond the current treatment-plant-only setup does not require large investment: adding all seven concourse nodes raises detection capacity from 1.44 to 9.46, a 6.6x improvement, at a cost of only 71 relative units and with no airside access. For routine surveillance of a broadly distributed disease, lavatory truck sampling should form the backbone of the network, and aircraft sampling is not cost-justified. Aircraft sampling should instead be held ready as a targeted capability for rare or geographically concentrated threats. The optimal network is not a fixed installation but a configuration that should be switched by scenario, so the operational value lies in retaining the flexibility to reconfigure it. Because the configuration is sensitive only to the cost ratios between node types, establishing the true relative cost of node types at a given airport is the parameter most worth validating before deployment. ...
Airports are critical entry points for infectious diseases, and wastewater sampling offers a scalable way to monitor pathogens carried by arriving travellers. At Amsterdam Airport Schiphol, sampling currently takes place only at the wastewater treatment plant, which pools all arriving flights and cannot attribute a detected pathogen to a specific flight or origin. While the literature has shown that aircraft and terminal sampling can provide origin specificity, no study has formalised the selection of sampling points within a single airport. This thesis addresses that gap through the research question: How can the wastewater sampling network at Amsterdam Airport Schiphol be designed and optimised to maximise the detection probability of infectious diseases while supporting attribution to flights and staying within operational and resource constraints?
The problem is formalised as a generic mixed-integer non-linear programme that selects among four node types (aircraft tanks, lavatory trucks, concourses, and the treatment plant) to maximise attribution-weighted detection capacity across all arriving flights subject to a budget constraint. A probability chain connects the epidemiological situation at each flight's origin to a detection event, combining per-node probabilities through a conditional noisy-OR formulation. The model is implemented in Python with Gurobi and applied to Schiphol using data from a representative busy day under three disease scenarios, with an extensive sensitivity and robustness analysis.
The central finding is that there is no single optimal network. The configuration that maximises detection capacity depends on how disease risk is distributed across arriving flights. For the broadly distributed SARS-CoV-2 base case, maximum coverage through concourse and lavatory truck sampling is optimal: detection capacity reaches 40.07 at budget 15,000 using all seven concourses and all 140 truck nodes, with no aircraft nodes selected. Spending the first 15,000 cost units on trucks and concourses gains 38.63 in detection capacity compared to only sampling the treatment plant, while adding aircraft nodes between budget 25,000 and 75,000 gains only 0.74. For the rare and geographically concentrated Hantavirus scenario, with a single at-risk flight from Argentina, the strategy differs. A single aircraft tank node targeting that flight achieves 5.08 x 10^-6 at budget 10,000, a 33% improvement over the combined truck, concourse, and treatment plant configuration. The same model, applied to the same airport on the same day, recommends opposite strategies depending only on the disease.
A comparison against a greedy benefit-cost heuristic shows that exact optimisation adds value precisely where it matters most: for SARS-CoV-2 the two approaches produce identical configurations through saturation, but exact optimisation outperforms greedy by 33% for Hantavirus and 7% for a concentrated outbreak. The optimal configuration is also robust across most sources of uncertainty, remaining invariant to a x0.1 to x10 scaling of infection probability and stable across six peak-season days. The main limitation is that the model evaluates a static 24-hour window and therefore optimises detection probability rather than detection timing. Extending it with a temporal layer, forward-looking prevalence estimation, a finer haul classification, and a multi-pathogen formulation are the most valuable directions for further research.
The findings translate into concrete guidance for surveillance practice. Moving beyond the current treatment-plant-only setup does not require large investment: adding all seven concourse nodes raises detection capacity from 1.44 to 9.46, a 6.6x improvement, at a cost of only 71 relative units and with no airside access. For routine surveillance of a broadly distributed disease, lavatory truck sampling should form the backbone of the network, and aircraft sampling is not cost-justified. Aircraft sampling should instead be held ready as a targeted capability for rare or geographically concentrated threats. The optimal network is not a fixed installation but a configuration that should be switched by scenario, so the operational value lies in retaining the flexibility to reconfigure it. Because the configuration is sensitive only to the cost ratios between node types, establishing the true relative cost of node types at a given airport is the parameter most worth validating before deployment.
The problem is formalised as a generic mixed-integer non-linear programme that selects among four node types (aircraft tanks, lavatory trucks, concourses, and the treatment plant) to maximise attribution-weighted detection capacity across all arriving flights subject to a budget constraint. A probability chain connects the epidemiological situation at each flight's origin to a detection event, combining per-node probabilities through a conditional noisy-OR formulation. The model is implemented in Python with Gurobi and applied to Schiphol using data from a representative busy day under three disease scenarios, with an extensive sensitivity and robustness analysis.
The central finding is that there is no single optimal network. The configuration that maximises detection capacity depends on how disease risk is distributed across arriving flights. For the broadly distributed SARS-CoV-2 base case, maximum coverage through concourse and lavatory truck sampling is optimal: detection capacity reaches 40.07 at budget 15,000 using all seven concourses and all 140 truck nodes, with no aircraft nodes selected. Spending the first 15,000 cost units on trucks and concourses gains 38.63 in detection capacity compared to only sampling the treatment plant, while adding aircraft nodes between budget 25,000 and 75,000 gains only 0.74. For the rare and geographically concentrated Hantavirus scenario, with a single at-risk flight from Argentina, the strategy differs. A single aircraft tank node targeting that flight achieves 5.08 x 10^-6 at budget 10,000, a 33% improvement over the combined truck, concourse, and treatment plant configuration. The same model, applied to the same airport on the same day, recommends opposite strategies depending only on the disease.
A comparison against a greedy benefit-cost heuristic shows that exact optimisation adds value precisely where it matters most: for SARS-CoV-2 the two approaches produce identical configurations through saturation, but exact optimisation outperforms greedy by 33% for Hantavirus and 7% for a concentrated outbreak. The optimal configuration is also robust across most sources of uncertainty, remaining invariant to a x0.1 to x10 scaling of infection probability and stable across six peak-season days. The main limitation is that the model evaluates a static 24-hour window and therefore optimises detection probability rather than detection timing. Extending it with a temporal layer, forward-looking prevalence estimation, a finer haul classification, and a multi-pathogen formulation are the most valuable directions for further research.
The findings translate into concrete guidance for surveillance practice. Moving beyond the current treatment-plant-only setup does not require large investment: adding all seven concourse nodes raises detection capacity from 1.44 to 9.46, a 6.6x improvement, at a cost of only 71 relative units and with no airside access. For routine surveillance of a broadly distributed disease, lavatory truck sampling should form the backbone of the network, and aircraft sampling is not cost-justified. Aircraft sampling should instead be held ready as a targeted capability for rare or geographically concentrated threats. The optimal network is not a fixed installation but a configuration that should be switched by scenario, so the operational value lies in retaining the flexibility to reconfigure it. Because the configuration is sensitive only to the cost ratios between node types, establishing the true relative cost of node types at a given airport is the parameter most worth validating before deployment.
Dredging the Future
Assessing the Impact of Alternative Uses of Dredge Sediment in the Port of Rotterdam
Ports face the dual challenge of keeping waterways navigable through continuous dredging while treating most dredged sediment as waste. Across Europe, only about 1% of the 200 million m³ dredged annually is put to beneficial use. This thesis addresses this challenge for the Port of Rotterdam, where offshore disposal remains the dominant practice because of its low cost and operational simplicity but provides no resource recovery and creates environmental impacts.
The research investigates whether participatory logistical modelling can support better decision-making on sediment reuse. An open-source simulation model (OpenCLSim) was developed to represent dredging, transport, and processing chains for three strategies: (1) continued offshore disposal, (2) land raising via truck or pipeline, and (3) reuse in concrete after dewatering. Stakeholders from the port authority, government agencies, contractors, and researchers were engaged through interviews, surveys, and workshops to co-define evaluation criteria and weigh their relative importance. Model results were combined with a multi-criteria decision analysis to rank alternatives.
Findings show that while offshore disposal remains the most cost- and time-efficient solution, pipeline-based land raising approaches cost parity at high volumes and delivers co-benefits for flood protection and habitat creation. Concrete reuse shows strong circular economy potential but is limited by dewatering costs, logistics, and regulatory barriers.
The study concludes that sediment management should be treated as a portfolio challenge, combining conventional disposal with targeted reuse projects. Participatory modelling proved valuable for exploring trade-offs, testing “what-if” scenarios, and building consensus—providing a transferable framework for more sustainable sediment management. ...
The research investigates whether participatory logistical modelling can support better decision-making on sediment reuse. An open-source simulation model (OpenCLSim) was developed to represent dredging, transport, and processing chains for three strategies: (1) continued offshore disposal, (2) land raising via truck or pipeline, and (3) reuse in concrete after dewatering. Stakeholders from the port authority, government agencies, contractors, and researchers were engaged through interviews, surveys, and workshops to co-define evaluation criteria and weigh their relative importance. Model results were combined with a multi-criteria decision analysis to rank alternatives.
Findings show that while offshore disposal remains the most cost- and time-efficient solution, pipeline-based land raising approaches cost parity at high volumes and delivers co-benefits for flood protection and habitat creation. Concrete reuse shows strong circular economy potential but is limited by dewatering costs, logistics, and regulatory barriers.
The study concludes that sediment management should be treated as a portfolio challenge, combining conventional disposal with targeted reuse projects. Participatory modelling proved valuable for exploring trade-offs, testing “what-if” scenarios, and building consensus—providing a transferable framework for more sustainable sediment management. ...
Ports face the dual challenge of keeping waterways navigable through continuous dredging while treating most dredged sediment as waste. Across Europe, only about 1% of the 200 million m³ dredged annually is put to beneficial use. This thesis addresses this challenge for the Port of Rotterdam, where offshore disposal remains the dominant practice because of its low cost and operational simplicity but provides no resource recovery and creates environmental impacts.
The research investigates whether participatory logistical modelling can support better decision-making on sediment reuse. An open-source simulation model (OpenCLSim) was developed to represent dredging, transport, and processing chains for three strategies: (1) continued offshore disposal, (2) land raising via truck or pipeline, and (3) reuse in concrete after dewatering. Stakeholders from the port authority, government agencies, contractors, and researchers were engaged through interviews, surveys, and workshops to co-define evaluation criteria and weigh their relative importance. Model results were combined with a multi-criteria decision analysis to rank alternatives.
Findings show that while offshore disposal remains the most cost- and time-efficient solution, pipeline-based land raising approaches cost parity at high volumes and delivers co-benefits for flood protection and habitat creation. Concrete reuse shows strong circular economy potential but is limited by dewatering costs, logistics, and regulatory barriers.
The study concludes that sediment management should be treated as a portfolio challenge, combining conventional disposal with targeted reuse projects. Participatory modelling proved valuable for exploring trade-offs, testing “what-if” scenarios, and building consensus—providing a transferable framework for more sustainable sediment management.
The research investigates whether participatory logistical modelling can support better decision-making on sediment reuse. An open-source simulation model (OpenCLSim) was developed to represent dredging, transport, and processing chains for three strategies: (1) continued offshore disposal, (2) land raising via truck or pipeline, and (3) reuse in concrete after dewatering. Stakeholders from the port authority, government agencies, contractors, and researchers were engaged through interviews, surveys, and workshops to co-define evaluation criteria and weigh their relative importance. Model results were combined with a multi-criteria decision analysis to rank alternatives.
Findings show that while offshore disposal remains the most cost- and time-efficient solution, pipeline-based land raising approaches cost parity at high volumes and delivers co-benefits for flood protection and habitat creation. Concrete reuse shows strong circular economy potential but is limited by dewatering costs, logistics, and regulatory barriers.
The study concludes that sediment management should be treated as a portfolio challenge, combining conventional disposal with targeted reuse projects. Participatory modelling proved valuable for exploring trade-offs, testing “what-if” scenarios, and building consensus—providing a transferable framework for more sustainable sediment management.
Computational Reproducibility in Modelling and Simulation Studies
A Case Study on Data Assimilation Algorithms for Agent-based Modelling and Simulation
In recent times, the ‘reproducibility crisis’ has become a cause for concern in the scientific community. Many disciplines from psychology and neuroscience to machine learning and ecology have been promoting initiatives towards ensuring the replicability and reproducibility of the findings reported in research publications. However, ensuring reproducibility and replicability of research faces many challenges such as differing definitions of the terms across disciplines, lack of incentives towards reproducing/replicating already published work and no standard methods for assessing a successful reproduction or replication. Reproducible research is fundamental to the scientific process, helps to ensure the credibility of scientific research and facilitates the dissemination and advancement of scientific knowledge. This crisis is especially relevant in the field of Modelling and Simulation and other computational sciences which rely on computer simulations to support findings yet there is a dearth of adequately detailed documentation to facilitate successful reproductions.
This project focussed on investigating the computational reproducibility of a research publication in the field of data assimilation for agent-based simulations. Agent-Based Modelling and Simulation (ABMS) is a computational method frequently employed to study complex socio-technical systems. Data assimilation techniques for ABMS is an emerging research area that seeks to incorporate real-time data into the model to improve its predictive capabilities. However, due to its novelty, reproducibility studies of these experiments are lacking. As this is a young research field, with various new methodologies being published, it is important to support verification and validation processes to advance scientific developments in the field such that the methods can be suitably adopted by applied researchers for future studies.
The main challenges of the reproduction process were identified as code quality and missing dependencies; ambiguous or missing specifications regarding the methodology and inconsistencies between textual descriptions and implemented code. Evaluation of reproducibility was also considered from the perspective of statistical metrics on one hand and qualitative reproducibility frameworks on the other hand. Furthermore, the experiment also highlighted the importance of computational provenance to connect the published results to the code or software used to generate them.
A series of practical steps to guide the workflow of future reproduction studies was drafted along with guiding questions to deduce computational workflows from publications and their code repositories when workflows to produce published results are missing.
A sensitivity analysis was employed to examine the influence of filter parameters including the number of particles, the resampling window, and the jitter standard deviation on the data assimilation algorithm’s estimation accuracy to verify the implementation and reproducibility of the particle filter algorithm used in the case study. From this experiment and based on literature, key elements that should be specified in future data assimilation for ABMS studies to ensure reproducibility of the research were identified.
In summary, this thesis project addressed the research gap in data assimilation for ABMS by conducting a reproducibility study of a research publication employing the Particle Filter technique. Key results from the original publication were reproduced and the original and reproduced results were compared. A reproducibility protocol was formulated to guide researchers in future reproducibility studies and with respect to data assimilation for agent-based simulations, a list of key parameters and considerations that should be reported for studies applying the particle filter to ABMS was devised.
...
This project focussed on investigating the computational reproducibility of a research publication in the field of data assimilation for agent-based simulations. Agent-Based Modelling and Simulation (ABMS) is a computational method frequently employed to study complex socio-technical systems. Data assimilation techniques for ABMS is an emerging research area that seeks to incorporate real-time data into the model to improve its predictive capabilities. However, due to its novelty, reproducibility studies of these experiments are lacking. As this is a young research field, with various new methodologies being published, it is important to support verification and validation processes to advance scientific developments in the field such that the methods can be suitably adopted by applied researchers for future studies.
The main challenges of the reproduction process were identified as code quality and missing dependencies; ambiguous or missing specifications regarding the methodology and inconsistencies between textual descriptions and implemented code. Evaluation of reproducibility was also considered from the perspective of statistical metrics on one hand and qualitative reproducibility frameworks on the other hand. Furthermore, the experiment also highlighted the importance of computational provenance to connect the published results to the code or software used to generate them.
A series of practical steps to guide the workflow of future reproduction studies was drafted along with guiding questions to deduce computational workflows from publications and their code repositories when workflows to produce published results are missing.
A sensitivity analysis was employed to examine the influence of filter parameters including the number of particles, the resampling window, and the jitter standard deviation on the data assimilation algorithm’s estimation accuracy to verify the implementation and reproducibility of the particle filter algorithm used in the case study. From this experiment and based on literature, key elements that should be specified in future data assimilation for ABMS studies to ensure reproducibility of the research were identified.
In summary, this thesis project addressed the research gap in data assimilation for ABMS by conducting a reproducibility study of a research publication employing the Particle Filter technique. Key results from the original publication were reproduced and the original and reproduced results were compared. A reproducibility protocol was formulated to guide researchers in future reproducibility studies and with respect to data assimilation for agent-based simulations, a list of key parameters and considerations that should be reported for studies applying the particle filter to ABMS was devised.
...
In recent times, the ‘reproducibility crisis’ has become a cause for concern in the scientific community. Many disciplines from psychology and neuroscience to machine learning and ecology have been promoting initiatives towards ensuring the replicability and reproducibility of the findings reported in research publications. However, ensuring reproducibility and replicability of research faces many challenges such as differing definitions of the terms across disciplines, lack of incentives towards reproducing/replicating already published work and no standard methods for assessing a successful reproduction or replication. Reproducible research is fundamental to the scientific process, helps to ensure the credibility of scientific research and facilitates the dissemination and advancement of scientific knowledge. This crisis is especially relevant in the field of Modelling and Simulation and other computational sciences which rely on computer simulations to support findings yet there is a dearth of adequately detailed documentation to facilitate successful reproductions.
This project focussed on investigating the computational reproducibility of a research publication in the field of data assimilation for agent-based simulations. Agent-Based Modelling and Simulation (ABMS) is a computational method frequently employed to study complex socio-technical systems. Data assimilation techniques for ABMS is an emerging research area that seeks to incorporate real-time data into the model to improve its predictive capabilities. However, due to its novelty, reproducibility studies of these experiments are lacking. As this is a young research field, with various new methodologies being published, it is important to support verification and validation processes to advance scientific developments in the field such that the methods can be suitably adopted by applied researchers for future studies.
The main challenges of the reproduction process were identified as code quality and missing dependencies; ambiguous or missing specifications regarding the methodology and inconsistencies between textual descriptions and implemented code. Evaluation of reproducibility was also considered from the perspective of statistical metrics on one hand and qualitative reproducibility frameworks on the other hand. Furthermore, the experiment also highlighted the importance of computational provenance to connect the published results to the code or software used to generate them.
A series of practical steps to guide the workflow of future reproduction studies was drafted along with guiding questions to deduce computational workflows from publications and their code repositories when workflows to produce published results are missing.
A sensitivity analysis was employed to examine the influence of filter parameters including the number of particles, the resampling window, and the jitter standard deviation on the data assimilation algorithm’s estimation accuracy to verify the implementation and reproducibility of the particle filter algorithm used in the case study. From this experiment and based on literature, key elements that should be specified in future data assimilation for ABMS studies to ensure reproducibility of the research were identified.
In summary, this thesis project addressed the research gap in data assimilation for ABMS by conducting a reproducibility study of a research publication employing the Particle Filter technique. Key results from the original publication were reproduced and the original and reproduced results were compared. A reproducibility protocol was formulated to guide researchers in future reproducibility studies and with respect to data assimilation for agent-based simulations, a list of key parameters and considerations that should be reported for studies applying the particle filter to ABMS was devised.
This project focussed on investigating the computational reproducibility of a research publication in the field of data assimilation for agent-based simulations. Agent-Based Modelling and Simulation (ABMS) is a computational method frequently employed to study complex socio-technical systems. Data assimilation techniques for ABMS is an emerging research area that seeks to incorporate real-time data into the model to improve its predictive capabilities. However, due to its novelty, reproducibility studies of these experiments are lacking. As this is a young research field, with various new methodologies being published, it is important to support verification and validation processes to advance scientific developments in the field such that the methods can be suitably adopted by applied researchers for future studies.
The main challenges of the reproduction process were identified as code quality and missing dependencies; ambiguous or missing specifications regarding the methodology and inconsistencies between textual descriptions and implemented code. Evaluation of reproducibility was also considered from the perspective of statistical metrics on one hand and qualitative reproducibility frameworks on the other hand. Furthermore, the experiment also highlighted the importance of computational provenance to connect the published results to the code or software used to generate them.
A series of practical steps to guide the workflow of future reproduction studies was drafted along with guiding questions to deduce computational workflows from publications and their code repositories when workflows to produce published results are missing.
A sensitivity analysis was employed to examine the influence of filter parameters including the number of particles, the resampling window, and the jitter standard deviation on the data assimilation algorithm’s estimation accuracy to verify the implementation and reproducibility of the particle filter algorithm used in the case study. From this experiment and based on literature, key elements that should be specified in future data assimilation for ABMS studies to ensure reproducibility of the research were identified.
In summary, this thesis project addressed the research gap in data assimilation for ABMS by conducting a reproducibility study of a research publication employing the Particle Filter technique. Key results from the original publication were reproduced and the original and reproduced results were compared. A reproducibility protocol was formulated to guide researchers in future reproducibility studies and with respect to data assimilation for agent-based simulations, a list of key parameters and considerations that should be reported for studies applying the particle filter to ABMS was devised.
Human vs. machine. Navigating the automation trade-offs
A methodology for evaluating automation level trade-offs in production line design
Manufacturing companies today face increasing global competition, labor shortages, and pressure to maximize efficiency. Automation is a key strategy for maintaining competitiveness, but determining the optimal level of automation in production line design is complex. Companies must weigh the strengths of human operators, such as flexibility and problem-solving, against the consistency and efficiency offered by machines. Although these decisions are crucial, there is currently no comprehensive methodology that allows companies to systematically evaluate the trade-offs between different levels of automation. This evaluation should integrate both quantitative and qualitative factors during the early design stages of a production line. To achieve a comprehensive understanding, the following key factors have been identified to systematically assess the trade-offs associated with automation options: production performance, cost, quality, flexibility and work environment.
This research adopts a design science methodology to develop and refine a stepwise evaluation framework for automation trade-offs in production line design. The methodology begins with a thorough documentation of the production context, requirements, and constraints. It then systematically generates low, medium, and high automation scenarios for each production step, considering both cognitive and physical tasks. These scenarios are conceptualized using structured models (IDEF0 diagrams) and layout maps, followed by the development of dynamic models, to quantitatively assess system dynamics, throughput, and bottlenecks. Each scenario is evaluated across the five key factors using both model outcomes and expert review sessions, enabling a holistic comparison. The methodology is validated through a practical test case at Quooker, a company developing a new production line, and refined through feedback from industry experts and academic reviewers.
The final version of the designed automation evaluation methodology provides a structured and comprehensive approach for evaluating automation trade-offs in early stage production line design. Its main strengths include the integration of both quantitative and qualitative assessments, the explicit consideration of five key factors, and the ability to generate actionable insights that broaden perspectives beyond intuitive thinking. Application to the Quooker test case demonstrated that the methodology supports informed, data-driven discussions and enables the identification of balanced automation scenarios. Feedback from industry and academic experts confirmed its practical relevance and alignment with industry needs. However, limitations include the time-intensive nature of dynamic modeling, the dependency on expert input, and challenges in quantifying certain factors at early design stages. Despite these constraints, the methodology offers a robust foundation for making informed automation decisions and structuring early design discussions in manufacturing environment. ...
This research adopts a design science methodology to develop and refine a stepwise evaluation framework for automation trade-offs in production line design. The methodology begins with a thorough documentation of the production context, requirements, and constraints. It then systematically generates low, medium, and high automation scenarios for each production step, considering both cognitive and physical tasks. These scenarios are conceptualized using structured models (IDEF0 diagrams) and layout maps, followed by the development of dynamic models, to quantitatively assess system dynamics, throughput, and bottlenecks. Each scenario is evaluated across the five key factors using both model outcomes and expert review sessions, enabling a holistic comparison. The methodology is validated through a practical test case at Quooker, a company developing a new production line, and refined through feedback from industry experts and academic reviewers.
The final version of the designed automation evaluation methodology provides a structured and comprehensive approach for evaluating automation trade-offs in early stage production line design. Its main strengths include the integration of both quantitative and qualitative assessments, the explicit consideration of five key factors, and the ability to generate actionable insights that broaden perspectives beyond intuitive thinking. Application to the Quooker test case demonstrated that the methodology supports informed, data-driven discussions and enables the identification of balanced automation scenarios. Feedback from industry and academic experts confirmed its practical relevance and alignment with industry needs. However, limitations include the time-intensive nature of dynamic modeling, the dependency on expert input, and challenges in quantifying certain factors at early design stages. Despite these constraints, the methodology offers a robust foundation for making informed automation decisions and structuring early design discussions in manufacturing environment. ...
Manufacturing companies today face increasing global competition, labor shortages, and pressure to maximize efficiency. Automation is a key strategy for maintaining competitiveness, but determining the optimal level of automation in production line design is complex. Companies must weigh the strengths of human operators, such as flexibility and problem-solving, against the consistency and efficiency offered by machines. Although these decisions are crucial, there is currently no comprehensive methodology that allows companies to systematically evaluate the trade-offs between different levels of automation. This evaluation should integrate both quantitative and qualitative factors during the early design stages of a production line. To achieve a comprehensive understanding, the following key factors have been identified to systematically assess the trade-offs associated with automation options: production performance, cost, quality, flexibility and work environment.
This research adopts a design science methodology to develop and refine a stepwise evaluation framework for automation trade-offs in production line design. The methodology begins with a thorough documentation of the production context, requirements, and constraints. It then systematically generates low, medium, and high automation scenarios for each production step, considering both cognitive and physical tasks. These scenarios are conceptualized using structured models (IDEF0 diagrams) and layout maps, followed by the development of dynamic models, to quantitatively assess system dynamics, throughput, and bottlenecks. Each scenario is evaluated across the five key factors using both model outcomes and expert review sessions, enabling a holistic comparison. The methodology is validated through a practical test case at Quooker, a company developing a new production line, and refined through feedback from industry experts and academic reviewers.
The final version of the designed automation evaluation methodology provides a structured and comprehensive approach for evaluating automation trade-offs in early stage production line design. Its main strengths include the integration of both quantitative and qualitative assessments, the explicit consideration of five key factors, and the ability to generate actionable insights that broaden perspectives beyond intuitive thinking. Application to the Quooker test case demonstrated that the methodology supports informed, data-driven discussions and enables the identification of balanced automation scenarios. Feedback from industry and academic experts confirmed its practical relevance and alignment with industry needs. However, limitations include the time-intensive nature of dynamic modeling, the dependency on expert input, and challenges in quantifying certain factors at early design stages. Despite these constraints, the methodology offers a robust foundation for making informed automation decisions and structuring early design discussions in manufacturing environment.
This research adopts a design science methodology to develop and refine a stepwise evaluation framework for automation trade-offs in production line design. The methodology begins with a thorough documentation of the production context, requirements, and constraints. It then systematically generates low, medium, and high automation scenarios for each production step, considering both cognitive and physical tasks. These scenarios are conceptualized using structured models (IDEF0 diagrams) and layout maps, followed by the development of dynamic models, to quantitatively assess system dynamics, throughput, and bottlenecks. Each scenario is evaluated across the five key factors using both model outcomes and expert review sessions, enabling a holistic comparison. The methodology is validated through a practical test case at Quooker, a company developing a new production line, and refined through feedback from industry experts and academic reviewers.
The final version of the designed automation evaluation methodology provides a structured and comprehensive approach for evaluating automation trade-offs in early stage production line design. Its main strengths include the integration of both quantitative and qualitative assessments, the explicit consideration of five key factors, and the ability to generate actionable insights that broaden perspectives beyond intuitive thinking. Application to the Quooker test case demonstrated that the methodology supports informed, data-driven discussions and enables the identification of balanced automation scenarios. Feedback from industry and academic experts confirmed its practical relevance and alignment with industry needs. However, limitations include the time-intensive nature of dynamic modeling, the dependency on expert input, and challenges in quantifying certain factors at early design stages. Despite these constraints, the methodology offers a robust foundation for making informed automation decisions and structuring early design discussions in manufacturing environment.
Navigating Through Very Low-Level Airspace
Designing Risk-Aware UAV Operations in Dutch Airspace via Infrastructure-Aligned Corridors
This research investigates the influence of socio-technical factors on the evacuation performance of university buildings. While prior studies have examined individual factors affecting evacuation, this thesis adopts a comprehensive socio-technical systems perspective, by considering the interactions of social, structural, and technical components within the complex context of university building evacuation systems.
To explore this, an agent-based simulation model was developed using NetLogo. The model simulates evacuation scenarios in two structurally different campus buildings at TU Delft: the Applied Sciences building and the Civil Engineering & Geosciences building. The key variables studied were familiarity with the building layout and exits, social influence behaviour, egress width, and signage. Evacuation performance was assessed using three metrics: 75% evacuation time, mean density, and exit choice.
Following a full factorial simulation experiment of 54 different scenarios per building totalling 10800 individual runs, a standardised ranking was created to equitably rank the scenarios based on their relative evacuation time, density, and exit choice, to determine the performance of the factors. A general linear model was created to determine the effect size of both the individual factors and all possible interactions.
The simulation results demonstrate that familiarity and egress width had the most significant impact on evacuation efficiency. In buildings with limited exits, egress width outweighed the effect of familiarity. While signage and social influence showed modest or statistically non-significant impacts overall, signage became more effective in low-familiarity contexts. The effect of social influence appeared to be sensitive to its level of formalisation in the model, underscoring its context-dependent nature.
Strengths of this study lie in the multi-metric perspective on evacuation performance, the use of multiple buildings to test effects in different structural environments, the possibility to include all possible interactions through a full factorial design, and the adaptability of the simulation model. Also, this study has several limitations, namely the balance between model correctness and performance, the difficulty to detect behavioural patterns, and software-based limitations.
From a policy perspective, the findings suggest that improving occupant familiarity with building layouts, through orientation, drills, or signage, can substantially improve evacuation performance. Furthermore, structural adjustments such as widening exits can mitigate congestion in critical zones, although its effect is dependent on hallway width. Although advanced signage technologies offer some improvements, their individual effectiveness is limited without complementary strategies.
The study highlights the importance of addressing evacuation preparedness as a multi-factorial challenge, especially in complex university settings where population heterogeneity and architectural diversity intersect. The model and methodology offer a flexible tool for future research and scenario testing. ...
To explore this, an agent-based simulation model was developed using NetLogo. The model simulates evacuation scenarios in two structurally different campus buildings at TU Delft: the Applied Sciences building and the Civil Engineering & Geosciences building. The key variables studied were familiarity with the building layout and exits, social influence behaviour, egress width, and signage. Evacuation performance was assessed using three metrics: 75% evacuation time, mean density, and exit choice.
Following a full factorial simulation experiment of 54 different scenarios per building totalling 10800 individual runs, a standardised ranking was created to equitably rank the scenarios based on their relative evacuation time, density, and exit choice, to determine the performance of the factors. A general linear model was created to determine the effect size of both the individual factors and all possible interactions.
The simulation results demonstrate that familiarity and egress width had the most significant impact on evacuation efficiency. In buildings with limited exits, egress width outweighed the effect of familiarity. While signage and social influence showed modest or statistically non-significant impacts overall, signage became more effective in low-familiarity contexts. The effect of social influence appeared to be sensitive to its level of formalisation in the model, underscoring its context-dependent nature.
Strengths of this study lie in the multi-metric perspective on evacuation performance, the use of multiple buildings to test effects in different structural environments, the possibility to include all possible interactions through a full factorial design, and the adaptability of the simulation model. Also, this study has several limitations, namely the balance between model correctness and performance, the difficulty to detect behavioural patterns, and software-based limitations.
From a policy perspective, the findings suggest that improving occupant familiarity with building layouts, through orientation, drills, or signage, can substantially improve evacuation performance. Furthermore, structural adjustments such as widening exits can mitigate congestion in critical zones, although its effect is dependent on hallway width. Although advanced signage technologies offer some improvements, their individual effectiveness is limited without complementary strategies.
The study highlights the importance of addressing evacuation preparedness as a multi-factorial challenge, especially in complex university settings where population heterogeneity and architectural diversity intersect. The model and methodology offer a flexible tool for future research and scenario testing. ...
This research investigates the influence of socio-technical factors on the evacuation performance of university buildings. While prior studies have examined individual factors affecting evacuation, this thesis adopts a comprehensive socio-technical systems perspective, by considering the interactions of social, structural, and technical components within the complex context of university building evacuation systems.
To explore this, an agent-based simulation model was developed using NetLogo. The model simulates evacuation scenarios in two structurally different campus buildings at TU Delft: the Applied Sciences building and the Civil Engineering & Geosciences building. The key variables studied were familiarity with the building layout and exits, social influence behaviour, egress width, and signage. Evacuation performance was assessed using three metrics: 75% evacuation time, mean density, and exit choice.
Following a full factorial simulation experiment of 54 different scenarios per building totalling 10800 individual runs, a standardised ranking was created to equitably rank the scenarios based on their relative evacuation time, density, and exit choice, to determine the performance of the factors. A general linear model was created to determine the effect size of both the individual factors and all possible interactions.
The simulation results demonstrate that familiarity and egress width had the most significant impact on evacuation efficiency. In buildings with limited exits, egress width outweighed the effect of familiarity. While signage and social influence showed modest or statistically non-significant impacts overall, signage became more effective in low-familiarity contexts. The effect of social influence appeared to be sensitive to its level of formalisation in the model, underscoring its context-dependent nature.
Strengths of this study lie in the multi-metric perspective on evacuation performance, the use of multiple buildings to test effects in different structural environments, the possibility to include all possible interactions through a full factorial design, and the adaptability of the simulation model. Also, this study has several limitations, namely the balance between model correctness and performance, the difficulty to detect behavioural patterns, and software-based limitations.
From a policy perspective, the findings suggest that improving occupant familiarity with building layouts, through orientation, drills, or signage, can substantially improve evacuation performance. Furthermore, structural adjustments such as widening exits can mitigate congestion in critical zones, although its effect is dependent on hallway width. Although advanced signage technologies offer some improvements, their individual effectiveness is limited without complementary strategies.
The study highlights the importance of addressing evacuation preparedness as a multi-factorial challenge, especially in complex university settings where population heterogeneity and architectural diversity intersect. The model and methodology offer a flexible tool for future research and scenario testing.
To explore this, an agent-based simulation model was developed using NetLogo. The model simulates evacuation scenarios in two structurally different campus buildings at TU Delft: the Applied Sciences building and the Civil Engineering & Geosciences building. The key variables studied were familiarity with the building layout and exits, social influence behaviour, egress width, and signage. Evacuation performance was assessed using three metrics: 75% evacuation time, mean density, and exit choice.
Following a full factorial simulation experiment of 54 different scenarios per building totalling 10800 individual runs, a standardised ranking was created to equitably rank the scenarios based on their relative evacuation time, density, and exit choice, to determine the performance of the factors. A general linear model was created to determine the effect size of both the individual factors and all possible interactions.
The simulation results demonstrate that familiarity and egress width had the most significant impact on evacuation efficiency. In buildings with limited exits, egress width outweighed the effect of familiarity. While signage and social influence showed modest or statistically non-significant impacts overall, signage became more effective in low-familiarity contexts. The effect of social influence appeared to be sensitive to its level of formalisation in the model, underscoring its context-dependent nature.
Strengths of this study lie in the multi-metric perspective on evacuation performance, the use of multiple buildings to test effects in different structural environments, the possibility to include all possible interactions through a full factorial design, and the adaptability of the simulation model. Also, this study has several limitations, namely the balance between model correctness and performance, the difficulty to detect behavioural patterns, and software-based limitations.
From a policy perspective, the findings suggest that improving occupant familiarity with building layouts, through orientation, drills, or signage, can substantially improve evacuation performance. Furthermore, structural adjustments such as widening exits can mitigate congestion in critical zones, although its effect is dependent on hallway width. Although advanced signage technologies offer some improvements, their individual effectiveness is limited without complementary strategies.
The study highlights the importance of addressing evacuation preparedness as a multi-factorial challenge, especially in complex university settings where population heterogeneity and architectural diversity intersect. The model and methodology offer a flexible tool for future research and scenario testing.
Enabling International Electric Truck Transport by Integrating Megawatt Charging Infrastructure in The Netherlands
An Agent-Based Simulation Approach
Neonatal care in the Netherlands is under significant strain due to severe bed capacity and staffing shortages, particularly in the southwestern region, where 36% of hospitals have closed neonatal beds. The current neonatal care system, structured into three escalating levels of specialization—neonatal medium care, high care, and Neonatal Intensive Care Units (NICUs)—struggles to meet demand, resulting in high occupancy rates and frequent patient transfers. This study developed a discrete-event simulation model to analyze and address bed capacity shortages within these staffing limitations. The model, based on regional perinatal birth data, evaluated the impact of various interventions on bed occupancy and transfer rates across different care levels.
Key findings revealed that reducing the length of stay, adjusting admission rates, and altering patient pathways could alleviate capacity pressures. While individual interventions offered modest improvements, a combined strategy integrating multiple approaches significantly reduced occupancy rates and patient transfers. This research underscores the need for a multifaceted approach to address neonatal care challenges and highlights the potential of simulation modeling as a tool for informed decision-making in healthcare resource management. The study’s insights offer a pathway toward sustainable neonatal care despite ongoing constraints. ...
Key findings revealed that reducing the length of stay, adjusting admission rates, and altering patient pathways could alleviate capacity pressures. While individual interventions offered modest improvements, a combined strategy integrating multiple approaches significantly reduced occupancy rates and patient transfers. This research underscores the need for a multifaceted approach to address neonatal care challenges and highlights the potential of simulation modeling as a tool for informed decision-making in healthcare resource management. The study’s insights offer a pathway toward sustainable neonatal care despite ongoing constraints. ...
Neonatal care in the Netherlands is under significant strain due to severe bed capacity and staffing shortages, particularly in the southwestern region, where 36% of hospitals have closed neonatal beds. The current neonatal care system, structured into three escalating levels of specialization—neonatal medium care, high care, and Neonatal Intensive Care Units (NICUs)—struggles to meet demand, resulting in high occupancy rates and frequent patient transfers. This study developed a discrete-event simulation model to analyze and address bed capacity shortages within these staffing limitations. The model, based on regional perinatal birth data, evaluated the impact of various interventions on bed occupancy and transfer rates across different care levels.
Key findings revealed that reducing the length of stay, adjusting admission rates, and altering patient pathways could alleviate capacity pressures. While individual interventions offered modest improvements, a combined strategy integrating multiple approaches significantly reduced occupancy rates and patient transfers. This research underscores the need for a multifaceted approach to address neonatal care challenges and highlights the potential of simulation modeling as a tool for informed decision-making in healthcare resource management. The study’s insights offer a pathway toward sustainable neonatal care despite ongoing constraints.
Key findings revealed that reducing the length of stay, adjusting admission rates, and altering patient pathways could alleviate capacity pressures. While individual interventions offered modest improvements, a combined strategy integrating multiple approaches significantly reduced occupancy rates and patient transfers. This research underscores the need for a multifaceted approach to address neonatal care challenges and highlights the potential of simulation modeling as a tool for informed decision-making in healthcare resource management. The study’s insights offer a pathway toward sustainable neonatal care despite ongoing constraints.
Exploring potential debris removal strategies during the earthquake response phase
An agent-based modeling approach to improve the post-earthquake emergency response in urban areas
Earthquakes are among the most devastating natural disasters, causing widespread damage, particularly in urban areas. A critical but often overlooked component of post-earthquake emergency response is the role of debris removal in the initial response phase. While debris removal is typically treated as part of long-term recovery, its immediate integration could enhance access to casualties, enabling faster medical care. This study investigates the impact of different debris removal strategies on the effectiveness of emergency response in urban settings using an Agent-Based Model (ABM) developed on the NetLogo platform. The model simulates a virtual city, "Quakecity," with realistic urban features, including road networks, hospitals, building damage, and injured resident distribution.
Two primary debris removal strategies were tested: one based on population density and the other on proximity to hospitals. Additionally, the potential use of army vehicles for casualty transportation was examined. These strategies were evaluated under two earthquake scenarios, varying levels of resource availability for both ambulances and debris removal equipment, measured by assisted residents and unreachable residents.
Results demonstrate that ambulance capacity has a more significant impact on the number of assisted residents than debris removal alone, although both strategies improve response effectiveness, particularly in high-damage scenarios. The hospital-proximity strategy was most effective when resources were plenty, while differences between strategies diminished in resource-constrained conditions. The introduction of army vehicles as supplementary casualty transporters proved highly effective. This research highlights the importance of adapting emergency response strategies based on available resources and the potential benefits of incorporating debris removal into the immediate response phase. ...
Two primary debris removal strategies were tested: one based on population density and the other on proximity to hospitals. Additionally, the potential use of army vehicles for casualty transportation was examined. These strategies were evaluated under two earthquake scenarios, varying levels of resource availability for both ambulances and debris removal equipment, measured by assisted residents and unreachable residents.
Results demonstrate that ambulance capacity has a more significant impact on the number of assisted residents than debris removal alone, although both strategies improve response effectiveness, particularly in high-damage scenarios. The hospital-proximity strategy was most effective when resources were plenty, while differences between strategies diminished in resource-constrained conditions. The introduction of army vehicles as supplementary casualty transporters proved highly effective. This research highlights the importance of adapting emergency response strategies based on available resources and the potential benefits of incorporating debris removal into the immediate response phase. ...
Earthquakes are among the most devastating natural disasters, causing widespread damage, particularly in urban areas. A critical but often overlooked component of post-earthquake emergency response is the role of debris removal in the initial response phase. While debris removal is typically treated as part of long-term recovery, its immediate integration could enhance access to casualties, enabling faster medical care. This study investigates the impact of different debris removal strategies on the effectiveness of emergency response in urban settings using an Agent-Based Model (ABM) developed on the NetLogo platform. The model simulates a virtual city, "Quakecity," with realistic urban features, including road networks, hospitals, building damage, and injured resident distribution.
Two primary debris removal strategies were tested: one based on population density and the other on proximity to hospitals. Additionally, the potential use of army vehicles for casualty transportation was examined. These strategies were evaluated under two earthquake scenarios, varying levels of resource availability for both ambulances and debris removal equipment, measured by assisted residents and unreachable residents.
Results demonstrate that ambulance capacity has a more significant impact on the number of assisted residents than debris removal alone, although both strategies improve response effectiveness, particularly in high-damage scenarios. The hospital-proximity strategy was most effective when resources were plenty, while differences between strategies diminished in resource-constrained conditions. The introduction of army vehicles as supplementary casualty transporters proved highly effective. This research highlights the importance of adapting emergency response strategies based on available resources and the potential benefits of incorporating debris removal into the immediate response phase.
Two primary debris removal strategies were tested: one based on population density and the other on proximity to hospitals. Additionally, the potential use of army vehicles for casualty transportation was examined. These strategies were evaluated under two earthquake scenarios, varying levels of resource availability for both ambulances and debris removal equipment, measured by assisted residents and unreachable residents.
Results demonstrate that ambulance capacity has a more significant impact on the number of assisted residents than debris removal alone, although both strategies improve response effectiveness, particularly in high-damage scenarios. The hospital-proximity strategy was most effective when resources were plenty, while differences between strategies diminished in resource-constrained conditions. The introduction of army vehicles as supplementary casualty transporters proved highly effective. This research highlights the importance of adapting emergency response strategies based on available resources and the potential benefits of incorporating debris removal into the immediate response phase.
The increasing presence of vehicle automation is transforming freeways into environments of mixed traffic, where vehicles of varying autonomy levels interact. Before all vehicles become fully autonomous, a transition will be made that causes a high mix of those autonomy levels. Therefore, this thesis researches the impact of different levels of vehicle automation on traffic performance and safety on a multi-lane freeway with an on-ramp. Microscopic simulation is utilised to explore how varying levels of vehicle automation, while taking human driving factors into account, affect traffic flow, speed, density and dangerous car-following interactions.
Currently, the majority of vehicles are defined as level 0 vehicles. This does not mean that these vehicles have no automated features at all but the Advanced Driver-Assistance Systems (ADAS) only provide temporary support such as an emergency brake. This is different for level 1 vehicles where the car-following driving tasks are automated and for level 2 vehicles both the car-following and lane-changing tasks are automated to support the driver. Level 3 vehicles are conditionally autonomous where all driving tasks are automated. The study aims to fill the knowledge gap in understanding the impact of these mixed traffic conditions on overall traffic dynamics.
To simulate the varying levels of automation, the study utilizes OpenTrafficSim (OTS), a microscopic traffic simulation software that incorporates a mental model to realistically represent human driving behaviour. This allows the simulation to account for human factors such as reaction time, perception, cognitive workload, and distractions, which are crucial in differentiating human drivers from automated vehicles. Four automation levels (0, 1, 2, and 3) defined by the Society of Automotive Engineers are modelled for specific driving characteristics within the freeway environment. Model parameters are adjusted for each level based on literature findings and practical considerations.
Simulation results indicate that the introduction of level 1 and level 2 vehicles, characterised by larger headway values, can negatively impact traffic performance but also result in less dangerous car-following behaviour. The increased headway leads to disruptions in traffic flow and an earlier onset of congestion. However, as the penetration rate of level 3 vehicles increases, traffic conditions significantly improve, with higher mean speeds, reduced travel times, and increased traffic flow observed. These findings highlight the potential benefits of higher levels of automation in enhancing traffic performance and safety.
The study also examines the impact of driver distraction on traffic performance and safety. By simulating both in-vehicle and roadside distractions, the research demonstrates that higher cognitive workloads can lead to more disruptive driving behaviour. As automation levels increase, the negative effects of distraction are mitigated.
Overall, this research provides valuable insights into the complexities of mixed traffic with varying automation levels. It demonstrates that while the transition phase may present challenges, higher levels of vehicle automation can significantly improve both traffic performance and safety on multi-lane freeways. Special emphasis is given to accurately simulating human driver behaviour and suggestions are made for future research, including the need for a dual-perception framework for more accurate modelling of level 1 vehicles and further investigation into the impact of different distraction types.
...
Currently, the majority of vehicles are defined as level 0 vehicles. This does not mean that these vehicles have no automated features at all but the Advanced Driver-Assistance Systems (ADAS) only provide temporary support such as an emergency brake. This is different for level 1 vehicles where the car-following driving tasks are automated and for level 2 vehicles both the car-following and lane-changing tasks are automated to support the driver. Level 3 vehicles are conditionally autonomous where all driving tasks are automated. The study aims to fill the knowledge gap in understanding the impact of these mixed traffic conditions on overall traffic dynamics.
To simulate the varying levels of automation, the study utilizes OpenTrafficSim (OTS), a microscopic traffic simulation software that incorporates a mental model to realistically represent human driving behaviour. This allows the simulation to account for human factors such as reaction time, perception, cognitive workload, and distractions, which are crucial in differentiating human drivers from automated vehicles. Four automation levels (0, 1, 2, and 3) defined by the Society of Automotive Engineers are modelled for specific driving characteristics within the freeway environment. Model parameters are adjusted for each level based on literature findings and practical considerations.
Simulation results indicate that the introduction of level 1 and level 2 vehicles, characterised by larger headway values, can negatively impact traffic performance but also result in less dangerous car-following behaviour. The increased headway leads to disruptions in traffic flow and an earlier onset of congestion. However, as the penetration rate of level 3 vehicles increases, traffic conditions significantly improve, with higher mean speeds, reduced travel times, and increased traffic flow observed. These findings highlight the potential benefits of higher levels of automation in enhancing traffic performance and safety.
The study also examines the impact of driver distraction on traffic performance and safety. By simulating both in-vehicle and roadside distractions, the research demonstrates that higher cognitive workloads can lead to more disruptive driving behaviour. As automation levels increase, the negative effects of distraction are mitigated.
Overall, this research provides valuable insights into the complexities of mixed traffic with varying automation levels. It demonstrates that while the transition phase may present challenges, higher levels of vehicle automation can significantly improve both traffic performance and safety on multi-lane freeways. Special emphasis is given to accurately simulating human driver behaviour and suggestions are made for future research, including the need for a dual-perception framework for more accurate modelling of level 1 vehicles and further investigation into the impact of different distraction types.
...
The increasing presence of vehicle automation is transforming freeways into environments of mixed traffic, where vehicles of varying autonomy levels interact. Before all vehicles become fully autonomous, a transition will be made that causes a high mix of those autonomy levels. Therefore, this thesis researches the impact of different levels of vehicle automation on traffic performance and safety on a multi-lane freeway with an on-ramp. Microscopic simulation is utilised to explore how varying levels of vehicle automation, while taking human driving factors into account, affect traffic flow, speed, density and dangerous car-following interactions.
Currently, the majority of vehicles are defined as level 0 vehicles. This does not mean that these vehicles have no automated features at all but the Advanced Driver-Assistance Systems (ADAS) only provide temporary support such as an emergency brake. This is different for level 1 vehicles where the car-following driving tasks are automated and for level 2 vehicles both the car-following and lane-changing tasks are automated to support the driver. Level 3 vehicles are conditionally autonomous where all driving tasks are automated. The study aims to fill the knowledge gap in understanding the impact of these mixed traffic conditions on overall traffic dynamics.
To simulate the varying levels of automation, the study utilizes OpenTrafficSim (OTS), a microscopic traffic simulation software that incorporates a mental model to realistically represent human driving behaviour. This allows the simulation to account for human factors such as reaction time, perception, cognitive workload, and distractions, which are crucial in differentiating human drivers from automated vehicles. Four automation levels (0, 1, 2, and 3) defined by the Society of Automotive Engineers are modelled for specific driving characteristics within the freeway environment. Model parameters are adjusted for each level based on literature findings and practical considerations.
Simulation results indicate that the introduction of level 1 and level 2 vehicles, characterised by larger headway values, can negatively impact traffic performance but also result in less dangerous car-following behaviour. The increased headway leads to disruptions in traffic flow and an earlier onset of congestion. However, as the penetration rate of level 3 vehicles increases, traffic conditions significantly improve, with higher mean speeds, reduced travel times, and increased traffic flow observed. These findings highlight the potential benefits of higher levels of automation in enhancing traffic performance and safety.
The study also examines the impact of driver distraction on traffic performance and safety. By simulating both in-vehicle and roadside distractions, the research demonstrates that higher cognitive workloads can lead to more disruptive driving behaviour. As automation levels increase, the negative effects of distraction are mitigated.
Overall, this research provides valuable insights into the complexities of mixed traffic with varying automation levels. It demonstrates that while the transition phase may present challenges, higher levels of vehicle automation can significantly improve both traffic performance and safety on multi-lane freeways. Special emphasis is given to accurately simulating human driver behaviour and suggestions are made for future research, including the need for a dual-perception framework for more accurate modelling of level 1 vehicles and further investigation into the impact of different distraction types.
Currently, the majority of vehicles are defined as level 0 vehicles. This does not mean that these vehicles have no automated features at all but the Advanced Driver-Assistance Systems (ADAS) only provide temporary support such as an emergency brake. This is different for level 1 vehicles where the car-following driving tasks are automated and for level 2 vehicles both the car-following and lane-changing tasks are automated to support the driver. Level 3 vehicles are conditionally autonomous where all driving tasks are automated. The study aims to fill the knowledge gap in understanding the impact of these mixed traffic conditions on overall traffic dynamics.
To simulate the varying levels of automation, the study utilizes OpenTrafficSim (OTS), a microscopic traffic simulation software that incorporates a mental model to realistically represent human driving behaviour. This allows the simulation to account for human factors such as reaction time, perception, cognitive workload, and distractions, which are crucial in differentiating human drivers from automated vehicles. Four automation levels (0, 1, 2, and 3) defined by the Society of Automotive Engineers are modelled for specific driving characteristics within the freeway environment. Model parameters are adjusted for each level based on literature findings and practical considerations.
Simulation results indicate that the introduction of level 1 and level 2 vehicles, characterised by larger headway values, can negatively impact traffic performance but also result in less dangerous car-following behaviour. The increased headway leads to disruptions in traffic flow and an earlier onset of congestion. However, as the penetration rate of level 3 vehicles increases, traffic conditions significantly improve, with higher mean speeds, reduced travel times, and increased traffic flow observed. These findings highlight the potential benefits of higher levels of automation in enhancing traffic performance and safety.
The study also examines the impact of driver distraction on traffic performance and safety. By simulating both in-vehicle and roadside distractions, the research demonstrates that higher cognitive workloads can lead to more disruptive driving behaviour. As automation levels increase, the negative effects of distraction are mitigated.
Overall, this research provides valuable insights into the complexities of mixed traffic with varying automation levels. It demonstrates that while the transition phase may present challenges, higher levels of vehicle automation can significantly improve both traffic performance and safety on multi-lane freeways. Special emphasis is given to accurately simulating human driver behaviour and suggestions are made for future research, including the need for a dual-perception framework for more accurate modelling of level 1 vehicles and further investigation into the impact of different distraction types.
Guiding food safety authorities in inspection strategy design
A systems approach to food safety regulation
The Nederlandse- Voedsel en Warenautoriteit (NVWA) asked the at-first-sight simple question of how to combine risk-based and random methods for inspection. Risk-based and random inspection methods have contrasting incentives. Risk-based inspection increases efficiency and allocates resources efficiently but fails to regulate low and emergent risks. Random inspection tackles unforeseen and emerging risks, ensures representativeness and prevents bias but fails to detect food hazards and non-compliance with food safety laws efficiently. A void in the literature exists on how to address the question of the NVWA. The supposedly simple question thus appeared to be a rather complex one. The research develops guidance for food safety authorities in combining risk-based and random methods through a design science approach combined with a systems view to retrieve a complete understanding while allowing it to narrow down to practical guidance.
The first step to address the literature void is the development of the construct of inspection strategy based on design science cycles fueled by literature and interviews with regulatory authorities. The following definition of the construct is retrieved: an inspection strategy entails selecting and combining methods based on inherent considerations and operationalization on contextual considerations. The construct of inspection strategy is placed between inspection method selection and food safety regulation. The construct serves as a common language and shared understanding for food safety authorities.
The systems view is introduced to understand why inspection strategy design is complex for food safety authorities because of its context. Literature shows there is value in taking a systems view to regulation. In fact, the complex systems view applied to food safety regulation brought forward systems characteristics to consider in inspection strategy design, including adaptive, emergence, unpredictable and goal-seeking. The systems view showed that an inspection strategy is to be designed within the context of food safety regulation that is continuously changing in an unpredictable manner because of emerging food safety risks. Consequently, the complex systems view provided direction on how guidance for food safety authorities designing an inspection strategy within food safety regulation is to be developed.
Following the definition of inspection strategy and the exploration of the system complexities of the food safety regulation context, the requirements for an inspection strategy are identified. The requirements originated from the systems map, interviews and literature. Fourteen requirements are defined with corresponding categories and priorities. Based on the requirements, a visual framework is constructed, providing an overview of the considerations for food safety authorities designing an inspection strategy. The framework, when applied to strategies, exposes potential trade-offs. The framework respects the complexities of the previously uncovered complex system characteristics by providing insight without imposing rigid standards.
Guidance for food safety authorities is developed based on the previous findings. The guidance is twofold. First, a scale of encountered inspection strategies from random to risk-based is evaluated based on the requirements framework, guiding food safety authorities by providing insight into the associated trade-offs and helping them select a strategy. Second, questions are developed to guide food safety authorities before, during and after the design of an inspection strategy. Food safety authorities have to implement the guidance themselves.
Ultimately, the research holds potential for food safety authorities to improve their inspection strategies combining risk-based and random methods when implementing the guidance. Consequently, improving the state of food safety in an accountable, applicable, feasible and adaptive manner. Through validation is confirmed that the guidance is helpful for the NVWA. Furthermore, the research produces academic value by creating a common language in the academic field through the construct of an inspection strategy and by demonstrating the utility of systems thinking for regulation. The recommendation stands to implement the guidance, include participatory methods, validate with various food safety authorities and continue applying systems thinking in food safety regulation for future research.
...
The first step to address the literature void is the development of the construct of inspection strategy based on design science cycles fueled by literature and interviews with regulatory authorities. The following definition of the construct is retrieved: an inspection strategy entails selecting and combining methods based on inherent considerations and operationalization on contextual considerations. The construct of inspection strategy is placed between inspection method selection and food safety regulation. The construct serves as a common language and shared understanding for food safety authorities.
The systems view is introduced to understand why inspection strategy design is complex for food safety authorities because of its context. Literature shows there is value in taking a systems view to regulation. In fact, the complex systems view applied to food safety regulation brought forward systems characteristics to consider in inspection strategy design, including adaptive, emergence, unpredictable and goal-seeking. The systems view showed that an inspection strategy is to be designed within the context of food safety regulation that is continuously changing in an unpredictable manner because of emerging food safety risks. Consequently, the complex systems view provided direction on how guidance for food safety authorities designing an inspection strategy within food safety regulation is to be developed.
Following the definition of inspection strategy and the exploration of the system complexities of the food safety regulation context, the requirements for an inspection strategy are identified. The requirements originated from the systems map, interviews and literature. Fourteen requirements are defined with corresponding categories and priorities. Based on the requirements, a visual framework is constructed, providing an overview of the considerations for food safety authorities designing an inspection strategy. The framework, when applied to strategies, exposes potential trade-offs. The framework respects the complexities of the previously uncovered complex system characteristics by providing insight without imposing rigid standards.
Guidance for food safety authorities is developed based on the previous findings. The guidance is twofold. First, a scale of encountered inspection strategies from random to risk-based is evaluated based on the requirements framework, guiding food safety authorities by providing insight into the associated trade-offs and helping them select a strategy. Second, questions are developed to guide food safety authorities before, during and after the design of an inspection strategy. Food safety authorities have to implement the guidance themselves.
Ultimately, the research holds potential for food safety authorities to improve their inspection strategies combining risk-based and random methods when implementing the guidance. Consequently, improving the state of food safety in an accountable, applicable, feasible and adaptive manner. Through validation is confirmed that the guidance is helpful for the NVWA. Furthermore, the research produces academic value by creating a common language in the academic field through the construct of an inspection strategy and by demonstrating the utility of systems thinking for regulation. The recommendation stands to implement the guidance, include participatory methods, validate with various food safety authorities and continue applying systems thinking in food safety regulation for future research.
...
The Nederlandse- Voedsel en Warenautoriteit (NVWA) asked the at-first-sight simple question of how to combine risk-based and random methods for inspection. Risk-based and random inspection methods have contrasting incentives. Risk-based inspection increases efficiency and allocates resources efficiently but fails to regulate low and emergent risks. Random inspection tackles unforeseen and emerging risks, ensures representativeness and prevents bias but fails to detect food hazards and non-compliance with food safety laws efficiently. A void in the literature exists on how to address the question of the NVWA. The supposedly simple question thus appeared to be a rather complex one. The research develops guidance for food safety authorities in combining risk-based and random methods through a design science approach combined with a systems view to retrieve a complete understanding while allowing it to narrow down to practical guidance.
The first step to address the literature void is the development of the construct of inspection strategy based on design science cycles fueled by literature and interviews with regulatory authorities. The following definition of the construct is retrieved: an inspection strategy entails selecting and combining methods based on inherent considerations and operationalization on contextual considerations. The construct of inspection strategy is placed between inspection method selection and food safety regulation. The construct serves as a common language and shared understanding for food safety authorities.
The systems view is introduced to understand why inspection strategy design is complex for food safety authorities because of its context. Literature shows there is value in taking a systems view to regulation. In fact, the complex systems view applied to food safety regulation brought forward systems characteristics to consider in inspection strategy design, including adaptive, emergence, unpredictable and goal-seeking. The systems view showed that an inspection strategy is to be designed within the context of food safety regulation that is continuously changing in an unpredictable manner because of emerging food safety risks. Consequently, the complex systems view provided direction on how guidance for food safety authorities designing an inspection strategy within food safety regulation is to be developed.
Following the definition of inspection strategy and the exploration of the system complexities of the food safety regulation context, the requirements for an inspection strategy are identified. The requirements originated from the systems map, interviews and literature. Fourteen requirements are defined with corresponding categories and priorities. Based on the requirements, a visual framework is constructed, providing an overview of the considerations for food safety authorities designing an inspection strategy. The framework, when applied to strategies, exposes potential trade-offs. The framework respects the complexities of the previously uncovered complex system characteristics by providing insight without imposing rigid standards.
Guidance for food safety authorities is developed based on the previous findings. The guidance is twofold. First, a scale of encountered inspection strategies from random to risk-based is evaluated based on the requirements framework, guiding food safety authorities by providing insight into the associated trade-offs and helping them select a strategy. Second, questions are developed to guide food safety authorities before, during and after the design of an inspection strategy. Food safety authorities have to implement the guidance themselves.
Ultimately, the research holds potential for food safety authorities to improve their inspection strategies combining risk-based and random methods when implementing the guidance. Consequently, improving the state of food safety in an accountable, applicable, feasible and adaptive manner. Through validation is confirmed that the guidance is helpful for the NVWA. Furthermore, the research produces academic value by creating a common language in the academic field through the construct of an inspection strategy and by demonstrating the utility of systems thinking for regulation. The recommendation stands to implement the guidance, include participatory methods, validate with various food safety authorities and continue applying systems thinking in food safety regulation for future research.
The first step to address the literature void is the development of the construct of inspection strategy based on design science cycles fueled by literature and interviews with regulatory authorities. The following definition of the construct is retrieved: an inspection strategy entails selecting and combining methods based on inherent considerations and operationalization on contextual considerations. The construct of inspection strategy is placed between inspection method selection and food safety regulation. The construct serves as a common language and shared understanding for food safety authorities.
The systems view is introduced to understand why inspection strategy design is complex for food safety authorities because of its context. Literature shows there is value in taking a systems view to regulation. In fact, the complex systems view applied to food safety regulation brought forward systems characteristics to consider in inspection strategy design, including adaptive, emergence, unpredictable and goal-seeking. The systems view showed that an inspection strategy is to be designed within the context of food safety regulation that is continuously changing in an unpredictable manner because of emerging food safety risks. Consequently, the complex systems view provided direction on how guidance for food safety authorities designing an inspection strategy within food safety regulation is to be developed.
Following the definition of inspection strategy and the exploration of the system complexities of the food safety regulation context, the requirements for an inspection strategy are identified. The requirements originated from the systems map, interviews and literature. Fourteen requirements are defined with corresponding categories and priorities. Based on the requirements, a visual framework is constructed, providing an overview of the considerations for food safety authorities designing an inspection strategy. The framework, when applied to strategies, exposes potential trade-offs. The framework respects the complexities of the previously uncovered complex system characteristics by providing insight without imposing rigid standards.
Guidance for food safety authorities is developed based on the previous findings. The guidance is twofold. First, a scale of encountered inspection strategies from random to risk-based is evaluated based on the requirements framework, guiding food safety authorities by providing insight into the associated trade-offs and helping them select a strategy. Second, questions are developed to guide food safety authorities before, during and after the design of an inspection strategy. Food safety authorities have to implement the guidance themselves.
Ultimately, the research holds potential for food safety authorities to improve their inspection strategies combining risk-based and random methods when implementing the guidance. Consequently, improving the state of food safety in an accountable, applicable, feasible and adaptive manner. Through validation is confirmed that the guidance is helpful for the NVWA. Furthermore, the research produces academic value by creating a common language in the academic field through the construct of an inspection strategy and by demonstrating the utility of systems thinking for regulation. The recommendation stands to implement the guidance, include participatory methods, validate with various food safety authorities and continue applying systems thinking in food safety regulation for future research.
Localization of Medical Device Manufacturing in Low- and Middle-Income countries
A study to develop key drivers and inhibitors to shifting manufacturing and assembly of medical devices in Low and Middle income countries
Master thesis
(2021)
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J. Jitin Gopakumar, C.P. van Beers, A. Verbraeck, S. Hinrichs-Krapels, Florent Geerts
The research was conducted looking into the possibilities of local manufacturing and assembly of medical devices in low and middle-income countries. Desktop research and expert interviews were conducted to learn the operations of medical devices (and other product) manufacturers. The interview process validated the literature study and also brought to light some new information on how medical device manufacturing (both local and international) operates. The research resulted in key drivers and inhibitors that companies need to consider when shifting their operations (or not) to low and middle-income countries.
...
The research was conducted looking into the possibilities of local manufacturing and assembly of medical devices in low and middle-income countries. Desktop research and expert interviews were conducted to learn the operations of medical devices (and other product) manufacturers. The interview process validated the literature study and also brought to light some new information on how medical device manufacturing (both local and international) operates. The research resulted in key drivers and inhibitors that companies need to consider when shifting their operations (or not) to low and middle-income countries.
Towards Value-Based Healthcare
Research to the design for an IT architecture to enable value-based healthcare in the Netherlands
The sustainability of the Dutch healthcare system is under severe pressure with increasing expenditure, more demand for care and a shortage of healthcare professionals. In the current healthcare system, providers are rewarded for increasing volume, but not for adding value, which only exacerbates the situation. Value-based healthcare (VBHC) is a candidate framework to reform the current healthcare system into an outcome-based system. Information systems are essential for the implementation of VBHC to execute outcome and cost measurements and collaborate across the care cycle. A literature review shows that there is a knowledge gap in the scientific literature to the architectures and components for such value-enabling information systems. This thesis aims to fill that gap by identifying and designing the essential components for a value-enabling IT architecture for the Dutch healthcare system.
The Information Systems (IS) research framework of Hevner et al. (2004) is adopted to guide the research. The environmental analysis shows several deficiencies in the as-is architecture for VBHC including a lack of patient-centredness, inaccessible or unavailable data, and complex to extract and integrate data. Preliminary semi-structured interviews reveal that, besides IT barriers, there is a deeply rooted trust issue among stakeholders which hampers successful implementation of VBHC. It is found that trust is often related to transparency and can be managed. Increasing transparency in the healthcare architecture would allow for (i) continuous quality improvements, (ii) improved decision-making, (iii) positive financial stimulus and (iv) patient empowerment. The design activities aim to facilitate VBHC through integrating components that reduce or eliminate the IT barriers and increase transparency. Another round of semi-structured interviews is carried out to find the principles, requirements and components for the design. The overall design developed uses three types of environment: a Healthcare Information System (HIS) used by caregivers, a Personal Healthcare Environment (PHE) used by patients and a Quality Registration System, available to all stakeholders. The design and components are evaluated with an expert panel. The most essential components are: PHE, HIS, Quality Registry system, Clinical building blocks (Zibs), terminology standards, a data integration centre, logging and monitoring services, measurements and evaluations, auditing IT and care processes and an intermediary that stimulates improvement and collaboration. The expert panel reached a consensus that transparency in healthcare would contribute to solving some of the problems, but it will not solve the core problems that lie within the healthcare structure. There are several areas recommended for future research. First, the components should be evaluated with a broader expert panel to increase the validity. Second, the PHE is an essential component, but still in its infancy. It is recommended to further evaluate the utility for VBHC. Third, blockchain technology might enhance the design due to its inherent characteristics that offer transparency. Fourth, further research on the privacy-utility trade-off in the context of VBHC is recommended. Finally, interoperable systems rely on a shared and uniform language. It is recommended to research which standards have the highest potential to facilitate the architecture components.
...
The Information Systems (IS) research framework of Hevner et al. (2004) is adopted to guide the research. The environmental analysis shows several deficiencies in the as-is architecture for VBHC including a lack of patient-centredness, inaccessible or unavailable data, and complex to extract and integrate data. Preliminary semi-structured interviews reveal that, besides IT barriers, there is a deeply rooted trust issue among stakeholders which hampers successful implementation of VBHC. It is found that trust is often related to transparency and can be managed. Increasing transparency in the healthcare architecture would allow for (i) continuous quality improvements, (ii) improved decision-making, (iii) positive financial stimulus and (iv) patient empowerment. The design activities aim to facilitate VBHC through integrating components that reduce or eliminate the IT barriers and increase transparency. Another round of semi-structured interviews is carried out to find the principles, requirements and components for the design. The overall design developed uses three types of environment: a Healthcare Information System (HIS) used by caregivers, a Personal Healthcare Environment (PHE) used by patients and a Quality Registration System, available to all stakeholders. The design and components are evaluated with an expert panel. The most essential components are: PHE, HIS, Quality Registry system, Clinical building blocks (Zibs), terminology standards, a data integration centre, logging and monitoring services, measurements and evaluations, auditing IT and care processes and an intermediary that stimulates improvement and collaboration. The expert panel reached a consensus that transparency in healthcare would contribute to solving some of the problems, but it will not solve the core problems that lie within the healthcare structure. There are several areas recommended for future research. First, the components should be evaluated with a broader expert panel to increase the validity. Second, the PHE is an essential component, but still in its infancy. It is recommended to further evaluate the utility for VBHC. Third, blockchain technology might enhance the design due to its inherent characteristics that offer transparency. Fourth, further research on the privacy-utility trade-off in the context of VBHC is recommended. Finally, interoperable systems rely on a shared and uniform language. It is recommended to research which standards have the highest potential to facilitate the architecture components.
...
The sustainability of the Dutch healthcare system is under severe pressure with increasing expenditure, more demand for care and a shortage of healthcare professionals. In the current healthcare system, providers are rewarded for increasing volume, but not for adding value, which only exacerbates the situation. Value-based healthcare (VBHC) is a candidate framework to reform the current healthcare system into an outcome-based system. Information systems are essential for the implementation of VBHC to execute outcome and cost measurements and collaborate across the care cycle. A literature review shows that there is a knowledge gap in the scientific literature to the architectures and components for such value-enabling information systems. This thesis aims to fill that gap by identifying and designing the essential components for a value-enabling IT architecture for the Dutch healthcare system.
The Information Systems (IS) research framework of Hevner et al. (2004) is adopted to guide the research. The environmental analysis shows several deficiencies in the as-is architecture for VBHC including a lack of patient-centredness, inaccessible or unavailable data, and complex to extract and integrate data. Preliminary semi-structured interviews reveal that, besides IT barriers, there is a deeply rooted trust issue among stakeholders which hampers successful implementation of VBHC. It is found that trust is often related to transparency and can be managed. Increasing transparency in the healthcare architecture would allow for (i) continuous quality improvements, (ii) improved decision-making, (iii) positive financial stimulus and (iv) patient empowerment. The design activities aim to facilitate VBHC through integrating components that reduce or eliminate the IT barriers and increase transparency. Another round of semi-structured interviews is carried out to find the principles, requirements and components for the design. The overall design developed uses three types of environment: a Healthcare Information System (HIS) used by caregivers, a Personal Healthcare Environment (PHE) used by patients and a Quality Registration System, available to all stakeholders. The design and components are evaluated with an expert panel. The most essential components are: PHE, HIS, Quality Registry system, Clinical building blocks (Zibs), terminology standards, a data integration centre, logging and monitoring services, measurements and evaluations, auditing IT and care processes and an intermediary that stimulates improvement and collaboration. The expert panel reached a consensus that transparency in healthcare would contribute to solving some of the problems, but it will not solve the core problems that lie within the healthcare structure. There are several areas recommended for future research. First, the components should be evaluated with a broader expert panel to increase the validity. Second, the PHE is an essential component, but still in its infancy. It is recommended to further evaluate the utility for VBHC. Third, blockchain technology might enhance the design due to its inherent characteristics that offer transparency. Fourth, further research on the privacy-utility trade-off in the context of VBHC is recommended. Finally, interoperable systems rely on a shared and uniform language. It is recommended to research which standards have the highest potential to facilitate the architecture components.
The Information Systems (IS) research framework of Hevner et al. (2004) is adopted to guide the research. The environmental analysis shows several deficiencies in the as-is architecture for VBHC including a lack of patient-centredness, inaccessible or unavailable data, and complex to extract and integrate data. Preliminary semi-structured interviews reveal that, besides IT barriers, there is a deeply rooted trust issue among stakeholders which hampers successful implementation of VBHC. It is found that trust is often related to transparency and can be managed. Increasing transparency in the healthcare architecture would allow for (i) continuous quality improvements, (ii) improved decision-making, (iii) positive financial stimulus and (iv) patient empowerment. The design activities aim to facilitate VBHC through integrating components that reduce or eliminate the IT barriers and increase transparency. Another round of semi-structured interviews is carried out to find the principles, requirements and components for the design. The overall design developed uses three types of environment: a Healthcare Information System (HIS) used by caregivers, a Personal Healthcare Environment (PHE) used by patients and a Quality Registration System, available to all stakeholders. The design and components are evaluated with an expert panel. The most essential components are: PHE, HIS, Quality Registry system, Clinical building blocks (Zibs), terminology standards, a data integration centre, logging and monitoring services, measurements and evaluations, auditing IT and care processes and an intermediary that stimulates improvement and collaboration. The expert panel reached a consensus that transparency in healthcare would contribute to solving some of the problems, but it will not solve the core problems that lie within the healthcare structure. There are several areas recommended for future research. First, the components should be evaluated with a broader expert panel to increase the validity. Second, the PHE is an essential component, but still in its infancy. It is recommended to further evaluate the utility for VBHC. Third, blockchain technology might enhance the design due to its inherent characteristics that offer transparency. Fourth, further research on the privacy-utility trade-off in the context of VBHC is recommended. Finally, interoperable systems rely on a shared and uniform language. It is recommended to research which standards have the highest potential to facilitate the architecture components.
Towards the Enhancement of Supply Chain Visibility in the FMCG Industry
Design of a comprehensive support tool to assess the maturity of supply chain visibility
Master thesis
(2021)
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L.A. Guleij, A. Verbraeck, M.W. Ludema, Nick Vermeulen, W.W.A. Beelaerts van Blokland
The Fast Moving Consumer Goods (FMCG) industry is driven by dynamic competition, globalization, and increasing pressure to improve efficiency and customer satisfaction. The increased uncertainty and complexity of coordination, result in the companies’ necessity to innovate and obtain capabilities for responsiveness and decision¬ making. Hereby, creating supply chain visibility is crucial to oppose the problems supply chain managers are dealing with. However, in many cases limited insight exists into how the right information can be collected and how to receive the right information across the supply chain that is accurate, complete, in ¬time, and of high quality. A first step towards strengthening the supply chain visibility of FMCG enterprises, is by assessing the performance of the companies’ supply chain visibility. Therefore, this study aims to design of a comprehensive support tool to assess the maturity of supply chain visibility. An extensive literature study and empirical research is executed to gain insights into the trends, complexity drivers, and critical information in the FMCG industry. Additionally, the main barriers impeding supply chain visibility were investigated. Design science as methodology is used to transform the theoretical knowledge into a digital tool as operational assessment method. This tool was developed during an iterative process existing of two separate analyses: the Visibility Maturity Scan and a Detailed Supply/Demand Visibility Analysis. Validation sessions with FMCG managers were carried out to test the practicality, overall impression, and added value of the digital tools. The managers confirmed that the tool creates awareness for critical areas, identifies critical suppliers/customers, and provides support for setting action priorities. Also, it can be used as a regular health check and benchmark of the organization over multiple years. The Visibility Maturity Scan provides grip for FMCG companies and is valuable during the project definition phase, where critical areas within the supply chain can be identified more easily. It can be placed at the website, sent by email, or used during sessions as starting point for further project proposals. The Detailed Demand/Supply Analysis provides detailed insights into the maturity of customers and suppliers in the business-to-business perspective and can be used as part of a project or detailed analysis. The tool can be used for benchmarking to a limited extent, but is more appropriate to monitor improvement processes within the same organization over time. All in all, the tool offers priorities, constraints, and implications that enterprises face in the transition towards a visible supply chain.
...
The Fast Moving Consumer Goods (FMCG) industry is driven by dynamic competition, globalization, and increasing pressure to improve efficiency and customer satisfaction. The increased uncertainty and complexity of coordination, result in the companies’ necessity to innovate and obtain capabilities for responsiveness and decision¬ making. Hereby, creating supply chain visibility is crucial to oppose the problems supply chain managers are dealing with. However, in many cases limited insight exists into how the right information can be collected and how to receive the right information across the supply chain that is accurate, complete, in ¬time, and of high quality. A first step towards strengthening the supply chain visibility of FMCG enterprises, is by assessing the performance of the companies’ supply chain visibility. Therefore, this study aims to design of a comprehensive support tool to assess the maturity of supply chain visibility. An extensive literature study and empirical research is executed to gain insights into the trends, complexity drivers, and critical information in the FMCG industry. Additionally, the main barriers impeding supply chain visibility were investigated. Design science as methodology is used to transform the theoretical knowledge into a digital tool as operational assessment method. This tool was developed during an iterative process existing of two separate analyses: the Visibility Maturity Scan and a Detailed Supply/Demand Visibility Analysis. Validation sessions with FMCG managers were carried out to test the practicality, overall impression, and added value of the digital tools. The managers confirmed that the tool creates awareness for critical areas, identifies critical suppliers/customers, and provides support for setting action priorities. Also, it can be used as a regular health check and benchmark of the organization over multiple years. The Visibility Maturity Scan provides grip for FMCG companies and is valuable during the project definition phase, where critical areas within the supply chain can be identified more easily. It can be placed at the website, sent by email, or used during sessions as starting point for further project proposals. The Detailed Demand/Supply Analysis provides detailed insights into the maturity of customers and suppliers in the business-to-business perspective and can be used as part of a project or detailed analysis. The tool can be used for benchmarking to a limited extent, but is more appropriate to monitor improvement processes within the same organization over time. All in all, the tool offers priorities, constraints, and implications that enterprises face in the transition towards a visible supply chain.
The Route of Crime
Analysing the impact of risk vs gain trade-offs on international criminal supply chains
Master thesis
(2021)
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R. Klaassen, I.M. van Schilt, A. Verbraeck, J.H. Kwakkel, H.G. van der Voort, Ron Van Den Bosch, Hans Baas
The growing import of illegal products in the Netherlands via container ships causes increased violence, addiction, and tax evasion in the Dutch society. With the current criminal detection practices the Dutch National Police and Dutch Customs do not manage to intercept the majority of the illegal shipments. The resources used by the Dutch Police and Customs for criminal detection do not yet entail information from the criminal risk vs gain trade-off point of view. Therefore, this study set out to research the impact of the criminal risk vs gain trade-off on the international criminal supply chains.
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The growing import of illegal products in the Netherlands via container ships causes increased violence, addiction, and tax evasion in the Dutch society. With the current criminal detection practices the Dutch National Police and Dutch Customs do not manage to intercept the majority of the illegal shipments. The resources used by the Dutch Police and Customs for criminal detection do not yet entail information from the criminal risk vs gain trade-off point of view. Therefore, this study set out to research the impact of the criminal risk vs gain trade-off on the international criminal supply chains.
Embeddedreal-time systems that have cost or energy constraints are usually limited inprocessing power and memory. This limitation typically leads to applyingsimpler execution models such as non-preemptive scheduling. A problem with anon-preemptive real-time system is that finding a schedule without causingdeadline misses is NP-hard. Finding a schedule is therefore done with online schedulingpolicies, i.e. policies that make decisions upon arrival or after execution ofa task to schedule the next one. Onlinenon-preemptive scheduling policies are typically priority based, namely, theypick the highest priority job to schedule based on criteria as period or absolutedeadline. These are work-conserving policies which cannot keep the processoridle while there are still pending jobs in the ready queue. Non-work-conservingpolicies allow an idle cpu while there are still jobs in the ready queue. Whilethis increases schedulability, it also has an increased overhead. Current stateof the art policies like Precautious Rate Monotonic (PRM) and Critical WindowsEarliest Deadline First (CW-EDF) have an idle-time insertion policy which caninsert an idle time in the schedule (between the execution of the jobs) whilestill having pending jobs. PRM verifies whether the highest priority job in theready queue is able to finish without causing a deadline miss for the highestpriority task in the system, which is the task with the lowest period. PRMimproves schedulability with increased overhead of O(1).CW-EDF comes with additional overhead O(n log n) in which it verifies ifscheduling the highest priority pending job will result in a deadline miss for thenext job of all other tasks in the system. PRM has low overhead, while CW-EDFhas better schedulability despite having higher overhead. Alimitation of these non-work-conserving policies is the missing support for event-triggeredtask, where jobs are released at unknown time instants. Namely, the existingnon-work-conserving policies are designed for strict periodic tasks. In thisthesis we will introduce a policy which has schedulability as high as CW-EDF,but prior to running the system it detects the critical tasks which have aninfluence on the idle-time insertion policy. We ignore the non-critical tasks duringthe idle-time insertion policy, reducing the runtime overhead. We also introducea policy which will support time-triggered tasks, by using an arrival curvewhich stores the possible behavior of the event-triggered task. With this arrivalcurve we will reduce the number of deadline misses compared to the existingnon-work-conserving policies. ...
Embeddedreal-time systems that have cost or energy constraints are usually limited inprocessing power and memory. This limitation typically leads to applyingsimpler execution models such as non-preemptive scheduling. A problem with anon-preemptive real-time system is that finding a schedule without causingdeadline misses is NP-hard. Finding a schedule is therefore done with online schedulingpolicies, i.e. policies that make decisions upon arrival or after execution ofa task to schedule the next one. Onlinenon-preemptive scheduling policies are typically priority based, namely, theypick the highest priority job to schedule based on criteria as period or absolutedeadline. These are work-conserving policies which cannot keep the processoridle while there are still pending jobs in the ready queue. Non-work-conservingpolicies allow an idle cpu while there are still jobs in the ready queue. Whilethis increases schedulability, it also has an increased overhead. Current stateof the art policies like Precautious Rate Monotonic (PRM) and Critical WindowsEarliest Deadline First (CW-EDF) have an idle-time insertion policy which caninsert an idle time in the schedule (between the execution of the jobs) whilestill having pending jobs. PRM verifies whether the highest priority job in theready queue is able to finish without causing a deadline miss for the highestpriority task in the system, which is the task with the lowest period. PRMimproves schedulability with increased overhead of O(1).CW-EDF comes with additional overhead O(n log n) in which it verifies ifscheduling the highest priority pending job will result in a deadline miss for thenext job of all other tasks in the system. PRM has low overhead, while CW-EDFhas better schedulability despite having higher overhead. Alimitation of these non-work-conserving policies is the missing support for event-triggeredtask, where jobs are released at unknown time instants. Namely, the existingnon-work-conserving policies are designed for strict periodic tasks. In thisthesis we will introduce a policy which has schedulability as high as CW-EDF,but prior to running the system it detects the critical tasks which have aninfluence on the idle-time insertion policy. We ignore the non-critical tasks duringthe idle-time insertion policy, reducing the runtime overhead. We also introducea policy which will support time-triggered tasks, by using an arrival curvewhich stores the possible behavior of the event-triggered task. With this arrivalcurve we will reduce the number of deadline misses compared to the existingnon-work-conserving policies.
Port call efficiency (PCE) is an important factor in the port choice of shipping lines. This research strives to contribute to the current work on PCE optimization. This is done by applying relative new techniques on port call related data. A combination of process mining (PM) and discrete event simulation (DEVS) is explored, with the Port of Rotterdam as a case study, to determine how they can contribute to identifying and assessing policies that improve port call efficiency. It is concluded that PM can be used as a tool for monitoring a port’s behaviour and spotting bottlenecks, from which port call efficiency policies are derived. Furthermore, it proved to be a useful method for conformance checking the event engine. In order to assess the identified policies, a discrete event simulation model is created, using a model structure, identified through PM. A policy is tested, where all the tugboats in the port work together as one fleet, instead of multiple fleets. From this, it is concluded that this method is successful in assessing scenarios, whose results can be translated back to real-live decision making.
...
Port call efficiency (PCE) is an important factor in the port choice of shipping lines. This research strives to contribute to the current work on PCE optimization. This is done by applying relative new techniques on port call related data. A combination of process mining (PM) and discrete event simulation (DEVS) is explored, with the Port of Rotterdam as a case study, to determine how they can contribute to identifying and assessing policies that improve port call efficiency. It is concluded that PM can be used as a tool for monitoring a port’s behaviour and spotting bottlenecks, from which port call efficiency policies are derived. Furthermore, it proved to be a useful method for conformance checking the event engine. In order to assess the identified policies, a discrete event simulation model is created, using a model structure, identified through PM. A policy is tested, where all the tugboats in the port work together as one fleet, instead of multiple fleets. From this, it is concluded that this method is successful in assessing scenarios, whose results can be translated back to real-live decision making.
Innovation beyond the stage-gate
Factors influencing commercialisation phase within financial services and solutions to mitigate barriers
Financial services industry which used to be relatively traditional is in the midst of change caused by the emergence of FinTech, forcing traditional firms to improve its innovation effectiveness. There exist various studies on corporate entrepreneurship, especially on the fuzzy front-end. However, studies on the back-end phase of innovations for financial services is still severely limited. This study attempted to increase our understanding of factors influencing the innovation outcome, as well as to explore possible solutions to increase effectiveness. A total of twelve factors were identified from literature.
Through an explorative case study and practitioners interviews, the influence of these twelve factors in real-life working situation are determined. The case study was undertaken at a major European bank. Meanwhile, expert interviews were conducted with practitioners from various traditional financial services firms and the academic field. Based on the empirical result, five drivers and three barriers are identified. Several factors are quite applicable for innovation in general, although others are more specific for the financial services industry. Five improvement points are proposed based on the interviews and further literature review. This research contributes to improving empirical knowledge of factors during the commercialisation of service innovation and providing a basis for further research. ...
Through an explorative case study and practitioners interviews, the influence of these twelve factors in real-life working situation are determined. The case study was undertaken at a major European bank. Meanwhile, expert interviews were conducted with practitioners from various traditional financial services firms and the academic field. Based on the empirical result, five drivers and three barriers are identified. Several factors are quite applicable for innovation in general, although others are more specific for the financial services industry. Five improvement points are proposed based on the interviews and further literature review. This research contributes to improving empirical knowledge of factors during the commercialisation of service innovation and providing a basis for further research. ...
Financial services industry which used to be relatively traditional is in the midst of change caused by the emergence of FinTech, forcing traditional firms to improve its innovation effectiveness. There exist various studies on corporate entrepreneurship, especially on the fuzzy front-end. However, studies on the back-end phase of innovations for financial services is still severely limited. This study attempted to increase our understanding of factors influencing the innovation outcome, as well as to explore possible solutions to increase effectiveness. A total of twelve factors were identified from literature.
Through an explorative case study and practitioners interviews, the influence of these twelve factors in real-life working situation are determined. The case study was undertaken at a major European bank. Meanwhile, expert interviews were conducted with practitioners from various traditional financial services firms and the academic field. Based on the empirical result, five drivers and three barriers are identified. Several factors are quite applicable for innovation in general, although others are more specific for the financial services industry. Five improvement points are proposed based on the interviews and further literature review. This research contributes to improving empirical knowledge of factors during the commercialisation of service innovation and providing a basis for further research.
Through an explorative case study and practitioners interviews, the influence of these twelve factors in real-life working situation are determined. The case study was undertaken at a major European bank. Meanwhile, expert interviews were conducted with practitioners from various traditional financial services firms and the academic field. Based on the empirical result, five drivers and three barriers are identified. Several factors are quite applicable for innovation in general, although others are more specific for the financial services industry. Five improvement points are proposed based on the interviews and further literature review. This research contributes to improving empirical knowledge of factors during the commercialisation of service innovation and providing a basis for further research.