Neural network-based adaptive production control system for a flexible manufacturing cell under a random environment

Abstract

A Neural Network (NN)-based Production Control System (PCS) for a Flexible Manufacturing Cell (FMC), operating in a highly random produce-to-order environment is presented. The proposed PCS chooses periodically, on the basis of the current state of the system, the most appropriate scheduling rule, out of several predetermined ones. The proposed PCS is based on multi-layer NNs, one for each competing scheduling rule, that predict the FMC's performance. The NNs are retrained periodically. The performance of the proposed NN-based PCS was tested by simulation of two different FMC configurations. The NN-based PCS has performed significantly better than a decision-tree-based PCS and a single-rule-based PCS.