OKademy is a start-up that wants to improve the healthcare in the Netherlands by improving the process by which graduated medical students are being matched to a hospital team. This is needed since there is a lack of surgery assistants, while graduate students need to wait on average six months to start their work in a hospital. To match quickly and sufficiently, the hard-skills as well as soft-skills of the student need to be known. To achieve this, universities need to provide OKademy with each student’s soft-skills. However, this takes an excessive amount of effort for the universities. To let the universities benefit from this idea, OKademy wants to have a system which keeps track of the soft-skills and matches students in groups for assignments, thus being beneficial for universities and hospitals. To help Okademy achieve its goal we developed a system that can be used by universities to help the students keep track of their soft-skills. The same system can be used to form theoretically well-functioning groups. These groups should be generated using an optimization algorithm that approximates the best groups regarding the soft-skills of students. In this system, students, referred to as ‘members’, and instructors, referred to as ‘hosts’, can register. Members can be assigned into groups and hosts can create or modify groups and courses accordingly. Members can, after registration, fill in their top 10 soft-skills with a corresponding automatized grade. Based on these skills, they will be assigned to groups for a course created by a host. A course will go through multiple phases. When all groups have completed the assignment, a member is able to give feedback to five random soft-skills of their group members and recommend a new one if desired. Based on this feedback, their soft-skill grades will be adjusted. The possible combinations of groups can be extremely large, therefore, it is not feasible to blindly search for the best group formation. Our algorithm will use the idea of genetic algorithms to explore the search space and approximate the best group formation. The the final product consists of a web application in which the multi-objective group formation optimization algorithm is implemented. To work in a structured manner, we used the Scrum method. Meetings with our client and coach were held every week. The system has been tested by functionality tests, user tests and unit tests, ensuring a functional system. OKademy will use this system in collaboration with universities and hospitals to solve the problem.

District heating systems (DHSs) have the potential to play a big part in the energy transition. The efficient operation of DHSs is therefore also an important subject of study. The operation of DHSs where combined heat and power (CHP) plants are used are particularly interesting, because CHPs can operate with high efficiency. In this work, the operational optimization of DHSs with CHP plants is considered. Determining the optimal heat and electricity production for CHPs for multiple time steps into the future is a complex problem. Because of the heat storage capabilities in the network many solutions are feasible, but determining which solutions are infeasible because of constraint violations in the DHS involves computing time delays that depend on complex network dynamics. In this work, the possibility of using an input convex neural network (ICNN) to learn the network dynamics of a DHS is explored. ICNNs have limitations on their learning capabilities, but theoretically allow for easier optimization. Experiments on the learning capabilities of ICNNs reveal that caution should be used when they are used to learn non-convex constraints, as the accuracy of the ICNN highly depends on how non-convex the function is. Experiments on the feasible space of supply temperatures to a small district heating network (DHN) reveal that although the network does not provide the same flexibility as heat storage tanks, still some flexibility in the operation can be found. This is due to the fact that water with a higher supply temperature is consumed by consumers at a slower pace and this increases the time delay between production and consumption. Supply temperatures that follow can then be lowered if the increased time delay causes this water to arrive when the heat demands are lower. In the experiments it was found that this flexibility in operation translates to non-convex areas in the feasible space. When this space would be learned by an ICNN, this space would be made convex. How much of the flexibility would be removed by doing this is yet unknown and could be researched in future work. Other future work can be done on safely learning non-convex constraints with an ICNN.