In order to measure the spectrum of radio emissions from galaxies and other deep space objects, a new superconducting spectrometer, working at very cold temperatures close to the absolute zero, is developed. An advanced cooling system called a cryostat is used to cool down the spectrometer. The cool down of the cryostat involves the control of multiple sensors and actuators connected to the cryostat to achieve a final temperature below 250 millikelvin. A software program is used for this purpose. As extra hardware components have been added to the cryostat, the existing program does no longer fulfill the requirements. For this reason a new software program, which can monitor temperatures of all components and start control processes, is developed. The developed program consists of a client server structure. The server handles the logic of the cryostat using several controllers. It can send data to a native client, which is the graphical user interface, or a REST API. The native client displays sensor readouts received from the server and allows full control of server, which means it can start the cool down process as well as manual control processes. The REST API allows the user to have full control over the server using a Python script to achieve measurements which cannot be done from the native client. The increased automation, improved control and ability to integrate with external Python scripts allow the user to focus on the essential parts of an experiment making the developed program an improvement over the previous program.

This thesis has researched the automation of performance evaluation of vehicle routing heuristics. The trade-off between solution quality, which is composed of multiple variables, and runtime make performance evaluation challenging. Therefore, it is often done by human experts. The research question of this thesis is: “How can we determine a performance measure that correctly represents the trade off between quality and runtime in vehicle routing heuristics?”. A literature review revealed that much research was done on performance evaluation, but not on heuristics specifically. The performance profile, a cumulative distribution function, is said to reflect all major performance characteristics of a solver. This, combined with a clustering algorithm, is used in this thesis in a classifier to detect performance anomalies. The performance profile needs a performance measure, for which three options were introduced: the area under the chart, the quality at the same time and the maximum difference. Through experimentation, 18 measure configurations were tested and rated on their accuracy and apparent issues. Three of the measure configurations have promising results, with an accuracy of roughly 80%.