A decomposition approach for stochastic Petri net models

We present a decomposition approach for the solution of large stochastic Petri nets (SPNs). The overall model consists of a set of submodels whose interactions are described by an import graph. Each node of the graph corresponds to a parametrized SPN submodel and an arc from submodel A to submodel B corresponds to a parameter value that B must receive from A. The quantities exchanged between submodels are based on only three primitives. The import graph is normally cyclic, so the solution method is based on fixed point iteration. The authors apply their technique to the analysis of a flexible manufacturing system.