Evaluation of part-type mix for a machining workstation using generalized stochastic Petri nets

A generalized stochastic Petri net (GSPN) model for a machining workstation controller is developed by using equivalent GSPN representations of controller place types. Performance measures such as the average production rates, the average time to produce a part and the machine utilization are computed as a function of part-type mix. It is shown that for certain part-type mixes, these performance measures are highly sensitive to variations in the system´s parameters, while for other part-type mixes they are more robust. The net result is an analysis tool for allocating resources when designing and implementing such workstations, and for evaluating potential performance improvements due to further automation in the workstation