A Decision Trees-based knowledge mining approach for controlling a complex production system

In this study, we use decision trees constructed by learning-with-supervision techniques for representing the best policies found by a Reinforcement Learning algorithm applied to an optimization problem of a complex production system. Until now, the relative scientific literature includes studies that mainly propose dynamic programming-based approaches for treating such kind of combinatorial problems. Decision trees are used to approximate functions of multiple variables with discrete values. In this case the “leaves” of the tree correspond to the set of function values while the non-terminal nodes to its independent variables. In the present research, the parameters of the optimization problem and the corresponding optimal policies found by the Reinforcement Learning algorithm applied, will be used as the training data set. Representing the best found policies using decision trees will support more effective qualitative analysis and further understanding of their properties.