Alternative Methods for Incorporating Non-exponential Distributions into Stochastic Petri Nets

A natural and compact way to incorporate non-exponential distributions into stochastic Petri nets is described. It allows users to directly specify the nonexponential transitions at the next level without providing the detailed construction; for example, to specify an Erlang distribution, the user only needs to provide the number of stages and the mean of the distribution. The refinement of the transition with a general distribution is performed automatically with a net-independent mechanism. The resulting net is a GSPN that can be solved with standard techniques. The authors also show how to expand conflicting transitions under the race-enabling policy (without the interconnection of places and transitions internal to the expansion of the different transitions), and have identified the different semantics introduced by nonexponential distributions, when a model does or does not use a control place