Performance evaluation of automated manufacturing systems using generalized stochastic Petri nets

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Abstract
Generalized stochastic Petri net (GSPN) modules are used as basic building blocks to model and analyze complex manufacturing systems. This modular approach facilitates model construction and helps manage the complexity of modeling large manufacturing systems. The structural analysis ensures that the model is live and bounded, which guarantees that the equivalent Markov chain (MC) is ergodic. The temporal analysis is used to derive performance measures such as average production rates and average in process inventories. The main advantage of Petri nets (PNs) over MCs is that the number of places and transitions increases only slightly as the manufacturing system complexity increases, whereas the number of states in the MC increases exponentially. In addition, there is no need to enumerate all the possible states manually since they are automatically generated from the GSPN model. As a result, PN models can still be easily obtained for complicated interconnected systems. The straightforward application of this approach is demonstrated and reviewed for several manufacturing case studies. For serial transfer lines it is proven that this modular approach results in live and bounded GSPN models. Comparisons are made with deterministic and reduced state-space models. Examples containing as many as 9614 states are presented.

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