Abstract
Markov Regenerative Processes (MRgP) with enabling restriction allow to model stochastic processes where the firing distribution of some events may be specified by a non-Markovian Probability Distribution Function, provided that at most one of these events is enabled in any state of the process. The GreatSPN framework is a collection of tools for the modeling and analysis of systems specified as Stochastic Petri Nets. The paper describes the new features of the MRgP solver of GreatSPN to deal with MRgP processes. The solver supports a rich language for the specification of non-Markovian events, and different solution techniques (explicit, matrix-free, component-based) for the MRgP analysis. The potentiality of the tools are shown on a few examples.
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