Integration of inductive learning and neural networks for multi-objective FMS scheduling

Abstract

In this paper, we propose an integrated approach of inductive learning and competitive neural networks for developing multi-objective flexible manufacturing system (FMS) schedulers. Simulation and competitive neural networks are applied sequentially to extract a set of classified training data which is used to create a compact set of scheduling rules through inductive learning. The FMS scheduler can assist the operator to make decisions in real time, while satisfying multiple objectives desired by the operator. A simulation-based experiment is performed to evaluate the performance of the resulting scheduler.