A genetics-based hybrid scheduler for generating static schedules in flexible manufacturing contexts

Existing computerized systems that support scheduling decisions for flexible manufacturing systems (FMS´s) rely largely on knowledge acquired through rote learning for schedule generation. In a few instances, the systems also possess some ability to learn using deduction or supervised induction. We introduce a novel AI-based system for generating static schedules that makes heavy use of an unsupervised learning module in acquiring significant portions of the requisite problem processing knowledge. This scheduler pursues a hybrid schedule generation strategy wherein it effectively combines knowledge acquired via genetics-based unsupervised induction with rote-learned knowledge in generating high-quality schedules in an efficient manner. Through a series of experiments conducted on a randomly generated problem of practical complexity, we show that the hybrid scheduler strategy is viable, promising, and, worthy of more in-depth investigations