A moment generating function based approach for evaluating extended stochastic Petri Nets

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Abstract
A moment-generating-function (MGF)-based approach for performance analysis of extended stochastic Petri nets (ESPNs) is presented. The method integrates Petri nets, MGF and stochastic network concepts, and Mason's rule into a tool for evaluating various discrete-event dynamic systems. The ESPNs are modeled, given the specification of a system. Then, the state machine PN is derived, the transfer functions based on the MGFs of the related transitions are found, the network is reduced to a single transition with its transfer function for each performance measure, and system performance is calculated. Firing delays of transitions in ESPNs can be either deterministic or stochastic with an extended distribution. Three fundamental structures that can be reduced into a single transition are discussed. The machine-repairman model with a buffer is given as an example to illustrate the method for evaluating performance parameters.