Application of intelligent technique for the efficient operation of the flexible manufacturing system

This research involves the development and evaluation of a work flow control model for a type of flexible manufacturing system (FMS) called a flexible flow line (FFL). The control model can be considered as a kind of hybrid intelligent model in that it utilizes both computer simulation and nueral network technique. Training data sets were obtained using computer simulation of typical FFL states. And these data sets were used to train the neural network model. The model can easily incorporate particular aspects of a specific FFL such as limited buffer capacity and dispatching rules used. It also dynamically adapts to system uncertainty caused by such factors as machine breakdowns. Performance of the control model is shown to be superior to the random releasing method and the Minimal Part Set (MPS) heuristic in terms of machine utilization and work-in-process inventory level.