The Dutch healthcare system currently operates under intensifying fiscal and workforce pressures, transforming nurse scheduling from a simple operational task into a complex strategic conflict involving divergent stakeholder priorities. Addressing this "wicked problem," this study addresses nurse scheduling as a strategic decision challenge, introducing a novel methodological framework that bridges Operations Research and Game Theory. Unlike traditional optimization models, this research introduces the "Nurse Scheduling Game" (NSG), formulating the problem as an Integer Programming Game (IPG) to capture the behaviors and interactions of stakeholders explicitly.

The interaction is analyzed through two distinct equilibrium concepts representing different governance structures. First, the uncoordinated state is modeled as a simultaneous Nash Equilibrium, formulated as a Generalized Nash Equilibrium Problem (GNEP). Second, the potential for strategic improvement is explored through the hierarchical Stackelberg Equilibrium, formulated as a Bilevel Integer Problem (BIP). In the latter, the Hospital Manager (Leader) explicitly anticipates the Nurses' (Followers) reactions. To solve this computationally intractable bilevel problem, the study implements a novel Monte Carlo Multilevel Optimization (MCMO) framework.

Applied to a representative case study of a mid-sized Dutch hospital, the computational results quantify the significant costs associated with uncoordinated planning. Under Nash dynamics, the system converges to a state of "defensive buffering," resulting in outcomes approximately twice as expensive as the coordinated alternative. Conversely, the Stackelberg Equilibrium demonstrates the value of strategic anticipation. By transitioning from volume-based to precision-based allocation, the hierarchical model achieved a 51.0% reduction in total system costs and an 11.8% reduction in patient waiting times compared to the Nash baseline.

These findings translate into actionable policy implications, suggesting that the solution to budget overruns lies in shifting from reactive to anticipatory governance. The study supports the implementation of Algorithmic Workforce Management systems that couple budget setting with schedule design. Ultimately, this research offers a unified game-theoretic optimization framework that reconciles financial constraints with workforce autonomy, providing a viable pathway toward sustainability for the Dutch healthcare system.

Patient-ReportedOutcome Measures (PROMs) are standardised instruments that capture patients’ self-reportedhealth status, symptoms, and quality of life, making them central tovalue-based and patient-centred care (McClimans&Browne, 2012). In theNetherlands, national policy under the “UitkomstsgerichteZorg” program hasestablished PROMs sets for key conditions and promoted their routine use inshared decision-making (Ministerie van Volksgezondheid, Welzijn en Sport, 2023).However, real-world adoption remains patchy: clinicians face workflowintegration hurdles and time constraints, patients often lack feedback or clearincentives to participate, and economic justifications for PROMs investmentsare underdeveloped (van Engen, van Lint, et al., 2024; Huberts et al., 2024). Moreover,while patient organisations are formally involved in Dutch health governance,their potential to advocate for, educate about, and co-design PROMs initiativeshas not been systematically harnessed (van de Bovenkamp et al., 2010). Thisstudy addresses the central question: How can patient organisations effectivelyenhance the use of Patient -Reported Outcome Measures in Dutch clinicalpractice to maximise value for patients? Employing a mixedmethods design, itfirst conducts a targeted literature review to map barriers (e.g., digitalliteracy gaps, lack of workflow integration, General Data Protection Regulation(GDPR)related consent complexities) and enablers (user -friendly ICT platforms,clear clinical responsibilities, actionable feedback loops) across micro, meso,and macro levels (Sawatzky etal., 2021; Oude Voshaar et al., 2023). Next,semi-structured interviews with representatives from leading Dutch patientorganisations will explore their perspectives on these factors and investigateco-creative strategies for PROMs implementation. Finally, a Social Return onInvestment (SROI) analysis, using data from a Dutch academic cancer centre andnational health statistics quantifies the projected social and economic impact ofprioritised interventions (Social Value UK, 2012). Preliminary findings suggestthat patient organisations can play pivotal roles in (1) advocating for the toolsthat will remove barriers such as language barriers and low health literacy,and (2) advocating for the voice of patients when creating and implementingPROMs in clinical practice. Moreover, (3) facilitating workshops on the PROMsto increase engagement and expand knowledge, (4) leading public awarenesscampaigns and collaborating with other patient organisations to emphasise thepersonal and system-level benefits of PROMs. Furthermore, the SROI analysisindicates that hospital-level deployment yields a modest return of =C 0.20 forevery euro invested. However, scaling up to the national level increases thereturn to =C4.57 per euro. This further emphasises the need for widespread, coordinatedefforts to realise the full potential of PROMs. Keywords: PatientReportedOutcome Measures, value-based healthcare, patient organisations, barriers andenablers, Social Return on Investment.
Related dataset 4TU.ResearchData:
https://doi.org/10.4121/uuid:a6aba8e1-9019-47a9-ad07-49fed049944a

The study evaluates the impact of the 2022 Dutch gender quota law for supervisory boards
in listed technology firms by examining performance outcomes, governance approaches, and
strategic elements. The research sought to answer an essential question in Management of
Technology about how externally imposed diversity requirements affect leadership structures
and adaptive capabilities, which innovation-driven companies need to maintain competitiveness.
The research employed a mixed-methods research approach using quantitative panel data
regressions, Difference-in-Differences models, event studies, and qualitative interviews with
board members and executives to generate comprehensive insights about early law impacts.
Research findings showed that gender diversity among supervisory board members in technology companies produced positive effects on market valuation and capital investment prior to
the quota introduction. The market results after implementing the quota showed that higher
diversity levels corresponded to slight reductions in profitability indicators, without claiming
causality. The study revealed no direct financial consequences between firms that were subject to quota requirements and those that already complied with gender diversity regulations.
The post-quota period revealed stronger market valuation results among technology firms than
their non-tech industry counterparts which indicates high absorptive capacity.
The qualitative results delivered fundamental insights by showing technology firms already
integrated gender diversity into their cultural frameworks. Yet they struggled to replace departing directors and obtain suitable candidates for succession roles. Board members often
described the quota as formalising existing priorities rather than fundamentally transforming
boardroom functioning, though it did prompt more frequent discussions about diversity within
nomination committees and governance processes. Directors expressed worries about tokenism along with fairness issues specifically when quota requirements clashed with their preferred internal candidates, while others saw value in the new conversations and gradual cultural shifts taking place.
The research findings demonstrate that the quota law successfully transformed supervisory
board demographics but it did not create short-term strategic or financial problems. True strategic integration of diversity into technology firms’ leadership decisions for innovation trajectory
management requires elements that surpass basic numeric compliance. The quota’s most notable impacts so far appear to lie not in direct financial outcomes, but in subtly reshaping how
companies discuss, prioritise, and institutionalise diversity as part of their broader governance
and organisational strategies.

Care for all is no longer guaranteed according to the Dutch Healthcare Authority. Dutch hospitals face increasing pressure in dealing with an increase in healthcare demand, staff shortages and financial uncertainty. To address these challenges the Dutch government introduced the Integral Care Agreement in which Shared Decision Making (SDM) is introduced as a way to tackle current healthcare problems. To support SDM, tools such as Decision Aids can be used. Decision Aids contain evidence based information on the treatment of a patient. This information helps the patient understand their medical condition and it facilitates them in choosing a treatment method that aligns with their values and preferences.

However, the adoption of decision aids seem to be lagging. This is due to the lack of clarity and financial uncertainty the implementation of Decision Aids bring. Hospitals are reimbursed for the care they deliver through a Diagnosis Treatment Combination (DBC). It is known that surgical DBCs generate more income for hospitals than non-surgical DBCs. A consequence of effective decision aid implementation is that patients opt for more conservative treatments, which could press the rise in demand for healthcare. However, as more patients choose for conservative treatments, the income of hospitals may decrease. If hospitals do not anticipate these changes in patient distribution across treatment options, they might become financially unstable.

This study aimed to help hospitals in their transition to Value Based Healthcare by evaluating the impact of Decision Aids. Current evaluation methods include analysing the changes in DBC and care activity volumes. A mixed methods approach was applied through 4 phases. Research methods included semi-structured interviews with healthcare professionals and System Dynamics Modeling.

Results showed a decrease in revenue for hospitals under the current financial system when Decision Aids are implemented, given that they cause for an increase in conservative treatments.

This study highlighted that these evaluation methods fall short in doing right compared to the quality improvement VBHC brings to care. Recommendations include finding a new evaluation method that is based on value-driven outcomes.

Planning and decision-making have become increasingly complex, especially in the energy sector. Modeling to Generate Alternatives (MGA) enables the creation of multiple alternative solutions, enhancing the decision-making process. However, when applied to large system optimization problems, the MGA method can become computationally cumbersome. Bio-inspired metaheuristics, a branch of artificial intelligence, have the potential to overcome these computational limitations by applying metaheuristic methods that can solve multiple model solutions simultaneously. Currently, a sophisticated integration of such heuristics with MGA, specifically focused on energy system optimization, has not yet been realized.

This thesis aims to answer the question: How does a combination of MGA and bio-inspired heuristics, aimed at optimizing energy system design, compare with existing deterministic MGA methods? To address this question, a metaheuristic-MGA algorithm was developed and compared with existing spatial energy system MGA results. First, a literature review was conducted to determine the most suitable metaheuristic for this study. The review confirmed the scarcity of literature on the combination of metaheuristics and MGA in energy systems. However, relevant studies applying a metaheuristic-MGA approach to general optimization problems were identified. Based on these findings, a genetic algorithm (GA) was selected as the most appropriate method for this thesis. The mathematical formulation presented in the literature was adapted to fit the spatial optimization problem of energy systems.

The complete mathematical process of the GA-MGA algorithm was developed in Python. Next, a small energy system test model was built in Calliope to evaluate the performance of the developed GA-MGA algorithm. The algorithm’s parameters were further fine-tuned using existing parameter-tuning methods, performance measurements, and assessments of the computational time required to complete the algorithmic process.

Before applying the developed GA-MGA algorithm to a large-scale model, it needed to be scaled to prevent errors or computational inefficiencies when generating results. It was determined that the desired resolution was not feasible due to the excessive computational time required for its completion. Instead, a time-masking method was applied to the resolution, preserving high-resolution characteristics while improving computational efficiency.

The GA-MGA algorithm was then applied to a large European energy system model, and the results were compared to existing MGA results. However, due to differences in resolution, the GA-MGA-generated results did not meet the standards of the existing MGA results, making direct comparisons less robust and reliable than desired. The comparison revealed a significant difference in battery capacity deployment, with the GA-MGA solutions deploying higher quantities of battery capacity. The lack of spatial distribution data for the large model was solved by comparing the GA-MGA results to a plot of the existing MGA results. While this was not an ideal comparison, it provided an opportunity to analyze spatial deployment differences between the GA-MGA and the existing MGA results. The comparison showed that the GA-MGA algorithm favored high-capacity deployment at specific locations, whereas the existing MGA results exhibited a more diverse capacity distribution.

The limitations of the results primarily stemmed from shortcomings in spatial comparison and differences in resolution between the two modeling techniques. Another key limitation was the algorithm’s structure, which presents several opportunities for improvement in optimizing the GA-MGA approach. Despite these challenges, the theoretical combination of GA-MGA demonstrated promising potential. Future research should focus on enhancing the algorithm’s performance and conducting more in-depth comparisons with MGA results to fully evaluate its effectiveness. Further research in this area could expand access to the MGA method for tackling large, complex problems, ultimately contributing to more effective planning and decision-making processes. This, in turn, would support efforts to address major societal challenges.

The Port of Rotterdam is the biggest port in Europe and with its great location an important place for trade between Europe and other continents. Record numbers of intercepted illegal goods show that criminals use this port to traffic illegal goods. A lot is still unknown about the criminal supply chain in the Port of Rotterdam and about the chance to catch illegal goods that are smuggled. Therefore this research provides insights into this criminal supply chain by looking into the effects of the distribution of resources by law enforcement agencies between methods that can be used to catch illegal goods on the chance to catch illegal goods.

This research looks at the criminal supply chain from South America to the Port of Rotterdam where it focuses on the smuggling methods used inside the Europe Container Terminals in the Port of Rotterdam. Criminals make use of four smuggling methods which are the pincode fraud method, the switch and pincode fraud method, the extraction method and the empty depot method. Law enforcement agencies have a scan and a surveillance method to catch illegal goods. The methods of law enforcement agencies can catch different smuggling methods and both have a chance to catch illegal goods smuggled with the empty depot method. Whereas criminals choose per illegal container which smuggling method will be used, this is not possible for law enforcement agencies. Therefore, a linear relationship between the part of resources appointed to a method and the accuracy of that method is assumed for the methods of law enforcement agencies.

An agent-based model is built to capture the complexity of this criminal supply chain and show the behaviour of the cat-and-mouse-like situation between criminals and law enforcement agencies. This agent-based model is combined with the Nash equilibrium known from game theory, as game theory can give insights into this situation using a mathematical framework. As these research methods have not been combined for the distribution of resources among methods in the Port of Rotterdam in earlier research, this research provides insights into how these research methods can be combined and whether they will provide similar results.

When combining the Nash equilibrium from game theory with the agent-based model it is expected that the distribution of resources for the two players, criminals and law enforcement agencies, in the agent-based model will eventually end up in or around the Nash equilibrium. This is because, in the Nash equilibrium, no player can get a higher expected payoff by deviating from the equilibrium. The players in the agent-based model update their distribution of resources every period of four weeks to adapt to the behaviour of the other player. This adaption is modelled with an updating rule. As literature indicated that the modelling of this updating rule can cause different behaviour in agent-based models, six different updating rules are tested in this research.

Given a chance to catch illegal goods of the scan of 0.18 for smuggling methods that can be caught by the scan and a chance of 0.8 for surveillance for methods that can be caught by surveillance, the Nash equilibrium is reached when law enforcement agencies appoint 40/49 of their resources to the scan and 9/49 to surveillance and criminals appoint 40/49 of their resources to the pincode fraud method and 9/49 to the switch and pincode fraud method and the extraction method combined. In the Nash equilibrium criminals will not appoint any resources to the empty depot method.

The six updating rules used in the agent-based model show different behaviours. Some updating rules cause the players in the agent-based model to appoint all their resources to only one method and others cause the players to end up in a cyclic pattern around the Nash equilibrium. The agent-based model also shows that by changing parameters of certain updating rules, the behaviour can change completely. For some updating rules the distribution of resources can get appointed according to an equilibrium which is not a Nash equilibrium as the payoffs of the methods are not equal to each other. Various difficulties arise when designing an updating rule. None of the six updating rules can guarantee the agent-based models to end up in or around the Nash equilibrium.

These difficulties include that resources should be distributed according to the payoff of methods according to game theory and should not be done according to the success rate of methods. Other difficulties arise when methods get appointed little or no resources. As it is the best response for players to distribute all their resources to one method when the other player is not playing according to the Nash equilibrium, the updating rule needs to be able to appoint zero resources to a method. Updating rules should also ensure that this method can be reappointed more resources when the other player changes its distribution of resources. Other difficulties include equilibria that are not a Nash equilibrium as mentioned above and the fact that players do not adapt in the same way when they do not have the same number of methods.

From this research, it can be concluded that in order to use an agent-based model for the situation between law enforcement agencies and criminals the knowledge about Nash equilibria should be considered while creating the model. This will not happen automatically as seen by the multiple difficulties this research showed. This research also shows that updating the distribution of resources by law enforcement agencies is important as they risk having an unnecessarily low chance to catch illegal goods when criminals adapt, while law enforcement agencies would not.

Therefore, it is recommended for law enforcement agencies to update the distribution of resources when it is expected that criminals will adapt as well, to not risk having an unnecessarily low chance of catching illegal goods. Furthermore, it is recommended to consider the Nash equilibrium while updating the distribution of resources and to carefully observe the distribution of resources of criminals when appointing all resources to only one method.

Limitations of this research include that there are only a limited number of methods included and players can not learn new methods. Due to the limited data about the criminal supply chain and about the methods that can be used by law enforcement agencies, there is deep uncertainty in model parameters, especially in the chances of catching illegal goods of the methods of law enforcement agencies. Therefore the results are not entirely valid and should only be used to analyse expected behaviour. Further research is needed on these chances of catching illegal goods and to test more updating rules as this research does not provide an updating rule that will ensure that an agent-based model will always end up in or around the Nash equilibrium.

Although competition is a dynamic phenomenon, currently used competition models do not take price dynamics into account, and some models are not even quantitative. This poses a problem for regulators or hedge funds and M&A departments who rely on these models to either quantify price and demand movements, or to determine the cost of competition.

This thesis solves that problem by using Economic Engineering to build on the existing game-theoretic models of competition to include price dynamics. A bond-graph model of a competitive market is developed, from which the price dynamics are derived. Model-predictive controllers are used to model profit-maximizing companies within this model, and to simulate competitive behavior and its effects on prices and demand flows.

Finally, this thesis shows how control engineering tools in both the time and the frequency domain can then be exploited by regulators and hedge funds. Time-domain simulations enable regulators to quantify the effects of competition on prices and demands, and analyses in the frequency domain enable hedge funds to determine the change in company valuations due to changes in competition, i.e., the cost of competition.