An iterative algorithm for performance evaluation of Stochastic Event Graph

Stochastic Event Graph (SEG) is a useful tool to model some discrete event dynamic systems and evaluate their performance. In this paper, the evolution of the system is analysed by unfolded SEG and its state equations are presented. By using the tree structure of the correlative property of the state variables, an algorithm for the probability distributions of state variables is derived. Finally, for an example of decision making organization, the steady solutions are reached after two iterations. In comparison with Markov Chain analysis, this algorithm has better convergence, and can solve problems with general distributions of service time. It can obtain not only steady distributions, but also transient properties