Stochastic petri nets for modelling and simulation

Abstract

Stochastic Petri nets (SPNs) have proven to be a powerful and enduring graphically-oriented framework for modelling and performance analysis of complex systems. This tutorial focuses on the use of SPNs in discrete-event simulation. After describing the basic SPN building blocks and discussing the modelling power of the formalism, we present elements of a steady-state simulation theory for SPNs. Specifically, we provide conditions on the building blocks of an SPN that ensure long-run stability for the underlying marking process (or for a sequence of delays determined by the marking process) and the validity of estimation procedures such as the regenerative method, the method of batch means, and spectral methods. Department of Management Science and Engineering at Stanford University.