On generating a hierarchy for GSPN analysis

This paper examines the (semi) automatic generation of a hierarchical structure for generalized stochastic Petri nets (GSPNs). The idea is to partition a GSPN automatically into a set of components with asynchronous communication. Net level results obtained by invariant computation for these subnets are used to define a macro description of the internal state. This yields a hierarchical structure which is exploited in several efficient analysis algorithms. These algorithms include reachability set/graph generation, structured numerical analysis techniques and approximation techniques based on decomposition and aggregation. A GSPN model of an existing production cell and its digital control is analyzed to demonstrate usefulness of the approach.

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