In: Proc. Large Scale Systems 95, London, UK, July 1995. 1995.
Abstract: Petri nets have proven to be a valuable model for discrete event dynamic systems, especially automated manufacturing systems. To model uncertainty within this representation approach, stochastic Petri nets have been developed. They suffer, however, from dimen-sionality explosion when it is attempted to model large real-world systems. There have been approaches based on the equivalent Markov chain, which is either solved through aggregation or through its singular perturbation treatment a reduced net is produced. We propose a method here to reduce directly the stochastic Petri net.