  The ever higher complexity of manufacturing systems, continually shortening
life cycles of products and their increasing variety, as well as the unstable
market situation of the recent years require introducing grater flexibility and
responsiveness to manufacturing processes. From this perspective, one of the
critical manufacturing tasks, which traditionally attract significant attention
in both academia and the industry, but which have no satisfactory universal
solution, is production scheduling. This paper proposes an approach based on
genetics-based machine learning (GBML) to treat the problem of flow shop
scheduling. By the approach, a set of scheduling rules is represented as an
individual of genetic algorithms, and the fitness of the individual is
estimated based on the makespan of the schedule generated by using the
rule-set. A concept of the interactive software environment consisting of a
simulator and a GBML simulation engine is introduced to support human
decision-making during scheduling. A pilot study is underway to evaluate the
performance of the GBML technique in comparison with other methods (such as
Johnson's algorithm and simulated annealing) while completing test examples.
