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.