  The emergence of Industry 4.0 is making production systems more flexible and
also more dynamic. In these settings, schedules often need to be adapted in
real-time by dispatching rules. Although substantial progress was made until
the '90s, the performance of these rules is still rather limited. The machine
learning literature is developing a variety of methods to improve them, but the
resulting rules are difficult to interpret and do not generalise well for a
wide range of settings. This paper is the first major attempt at combining
machine learning with domain problem reasoning for scheduling. The idea
consists of using the insights obtained with the latter to guide the empirical
search of the former. Our hypothesis is that this guided empirical learning
process should result in dispatching rules that are effective and interpretable
and which generalise well to different instance classes. We test our approach
in the classical dynamic job shop scheduling problem minimising tardiness,
which is one of the most well-studied scheduling problems. Nonetheless, results
suggest that our approach was able to find new state-of-the-art rules, which
significantly outperform the existing literature in the vast majority of
settings, from loose to tight due dates and from low utilisation conditions to
congested shops. Overall, the average improvement is 19%. Moreover, the rules
are compact, interpretable, and generalise well to extreme, unseen scenarios.
