Integrating inductive learning and simulation in rule-based scheduling

Abstract

This paper proposes a framework for incorporating machine learning into the real time scheduling of a flexible manufacturing system, and extends it to scheduling in a flexible flow system. While the bulk of previous research on dynamic machine scheduling deals with the relative effectiveness of a single scheduling rule, the approach presented in this study provides a mechanism for the state-dependent selection of one from among several rules. We develop a Pattern Directed Scheduler (PDS) with a built-in inductive learning module for heuristic acquisition and refinement. Both simulation and inductive learning modules complement each other, resulting in improvement in the overall performance of the system. Computational results show that such a pattern directed scheduling results in favorable scheduling performance, intelligent scheduling mechanism.