Artificial neural networks for flexible manufacturing systems scheduling

Artificial neural networks (ANNs) are information processing systems motivated by the goals of reproducing the cognitive processes and organizational models of neurobiological systems. By virtue of their computational structure, ANN's feature attractive characteristics such as graceful degradation, robust recall with noisy and fragmented data, parallel distributed processing, generalization to patterns outside of the training set, nonlinear modeling capabilities, and learning. These computational features could provide enhanced inferencing functionality and real-time capabilities to develop approaches for traditional difficult problems such as flexible manufacturing system (FMS) scheduling. In this paper three different schemes of ANN's are applied to the FMS scheduling problem. These include a) relaxation-based networks, b) competitive-based schemes, and c) adaptive pattern recognition scheduling.