Intelligent scheduling controller for shop floor control systems: A hybrid genetic algorithm/decision tree learning approach

This work develops an intelligent scheduling controller (ISC) to support a shop floor control system (SFCS) to make real-time decisions, robust to various production requirements. Selecting near-optimal subset system attributes (or features) based on various production requirements to construct ISC knowledge bases is a critical issue because of the existence of much shop floor information in an SFCS. Accordingly, this work developed a learning-based ISC methodology to acquire knowledge of a dynamic dispatching rule control mechanism. The proposed approach integrates genetic algorithms (GAs) and decision trees (DTs) learning to evolve a combinatorial optimal subset of features from possible shop floor information concerning a DT-based ISC knowledge classifier. A GA is employed to search the space of all possible subsets of a large set of candidate features. For a given feature subset, a DT algorithm is invoked to generate a DT. Applying the GA/DT-based knowledge learning mechanism to the experimental results demonstrates that the use of an optimal subset of system attributes to build scheduling knowledge bases enhanced generalization ability of the learning bias above that in the absence of an attribute selection procedure, in terms of prediction accuracy of unseen data under various performance criteria. Furthermore, simulation results indicate that the GA/DT-based ISC improves system performance in the long run over that obtained with classical DT-based ISC and the heuristic individual dispatching rule, according to various performance criteria.