Traditional scheduling techniques suffer from a lack of flexibility. The problem's instances need to be deterministic, and results on datasets with small benchmark instances do usually not transfer to large-scale instances. We propose to develop adaptive algorithms that can leverage the similarities between instances of industrial scheduling problems. In particular, we focus on applications of modern machine learning techniques to combinatorial optimization problems, an emerging and promising research area. Traditional scheduling techniques such as constraint, mixed-integer, or answer set programming are highly generic, domain-independent, and, therefore, do not explicitly exploit the specificities of a problem domain. However, in a production facility, the settings between two consecutive schedules are often very similar. The machines, workers, production capacity, etc., usually stay the same or do not change significantly. Traditional scheduling techniques do not take advantage of such similarities, while machine learning, especially deep learning, can discover and exploit relationships in the data. Therefore, our research aims to incorporate machine learning into combinatorial optimization.