Applying machine learning to the design of decision support systems for intelligent manufacturing

This paper presents a Decision Support System (DSS) with inductive learning capability for model management. Simulation is used as the primary environment for modeling manufacturing systems and their processes. We propose an adaptive DSS framework for incorporating machine learning into the real time scheduling of a Flexible Manufacturing System (FMS).The resulting DSS, referred to as Pattern Directed Scheduling (PDS) system, has the unique characteristics of being an adaptive scheduler. While the bulk of previous research on dynamic production scheduling deals with the relative effectiveness of a single dispatching rule scheduling, the approach presented in this study provides a mechanism for the state-dependent selection of one among a set of dispatching rules.We address the PDS approach in the context of a Model Management System (MMS), with built-in simulation and inductive learning modules for heuristic acquisition and refinement. These modules complement each other in performing the decision support functions. Computational results show that such a pattern directed scheduling approach leads to superior system performance. It also provides a new framework for developing adaptive DSS.U of I OnlyETDs are only available to UIUC Users without author permissio