This research involves the development and evaluation of a work flow control model for a type of flexible manufacturing system (FMS) called a flexible line (FFL). The control model can be considered as a kind of hybrid intelligent model in that it uti...
This research involves the development and evaluation of a work flow control model for a type of flexible manufacturing system (FMS) called a flexible line (FFL). The control model can be considered as a kind of hybrid intelligent model in that it utilizes both computer simulation and neural network technique. Training data sets were obtained using computer simulation of typical FFL states. And these data sets were used to train the nueral network model. The model can easily incorporate particular aspects of a specific FFL such as limited butter 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.