In: Journal of Parallel and Distributed Computing 15. 1992.
Abstract: The class of generalized colored stochastic Petri nets (GCSPNs) is very popular in the field of qualitative and quantitative analysis of dynamic systems. However, for more complex systems a flat net specification is often not adequate. Hierarchical nets are a natural way to cope with the complexity of today's systems. Apart from the specification of dynamic systems the hierarchical structure of a net can also be used for analysis. In this paper a class of hierarchical GCSPNs is defined and algorithms for qualitative and quantitative analysis are presented. The new algorithms are shown to be more efficient for the analysis of larger models and they significantly extend the class of models solvable on a given hardware.
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