Markov regenerative models

The Markov Regenerative Stochastic Process (MRGP) has been shown to capture the behavior of real systems with both deterministic and exponentially distributed event times. In this paper we survey the MRGP literature and focus on the different solution techniques that can be adopted for their transient analysis. We also discuss the automated generation of MRGPs from Deterministic and Stochastic Petri Nets (DSPNs). Some examples are developed and solved to illustrate the modeling power of MRGPs and DSPNs.