Multi-agent multi-target catching is the act of multiple agents trying to catch multiple moving targets. The exact algorithms created for such chases are generally targets that are actively trying to get as far as possible, while the agents try to prevent that. However, these algorithms can get much more efficient whenever targets also want to get caught.
In this thesis, we introduce cooperative multi-agent multi-target catching, where the targets give as much information to the agents as they can to ensure that they get caught as quickly as possible. When targets share their path, agents can calculate where each of the targets can be intercepted and with that, increase the efficiency of the assignments. This leads to about half the number of iterations needed or about two-thirds of the distance that the agents need to travel.
We also introduce two variations: allowing agents to catch multiple targets and using only specific stopping locations. Allowing agents to catch multiple targets slightly lowers both the number of iterations and the agent distance needed. Using only specific stopping locations increases the number of iterations needed, but has next to no effect on the agent distance when using the targets' paths.

Through the years, companies have been exploring the field of data science. The Nederlandse Spoorwegen (NS) is not an exception to this. Modern trains are equipped with sensors that measure a variety of conditions within the train. This data is being stored in their data warehouse. This data has been proven useful for detection and response times to problems, which warrants two high-level goals of the NS: punctuality and reliability. However, even with the available data, visualization and detection of location-specific problems are not yet implemented. Location-specific problems are problems that are not caused by the train, but by the infrastructure or human fault at that specific location. At the moment, most patterns in error codes are only backed up by suspicions, since these error codes are not stored in a way they are easily readable. Therefore, it is hard to find connections between multiple error codes. This document describes the created system that supports the analysis of location-specific error code patterns. With the system, the NS will be able to improve their two high-level goals and ultimately improve customer satisfaction.

For the system, a framework was made, which allows the NS to further develop and extend on data analyses. Furthermore, an extensive UI was created, allowing users to investigate found error code patterns and trace back problems to their origin. With the system, the NS is able to verify and create new hypotheses on possible problematic locations. In this document, the problem in elaborated on, multiple solutions are given of which one is chosen and thoroughly motivated, the solutions are elaborated on and, finally, some recommendations for future expansion are given.