Efficient simulation of hierarchical stochastic activity network models

William H Sanders, Roberto S. Freire

Research output: Contribution to journalArticlepeer-review


Stochastic extensions to Petri nets have gained widespread acceptance as a method for describing the dynamic behavior of discrete-event systems. Both simulation and analytic methods have been proposed to solve such models. This paper describes a set of efficient procedures for simulating models that are represented as stochastic activity networks (SANs, a variant of stochastic Petri nets) and composed SAN-based reward models (SBRMs). Composed SBRMs are a hierarchical representation for SANs, in which individual SAN models can be replicated and joined together with other models, in an iterative fashion. The procedures exploit the hierarchical structure and symmetries introduced by the replicate operation in a composed SBRM to reduce the cost of future event list management. The procedures have been implemented as part of a larger performance-dependability modeling package known as UltraSAN, and have been applied to real, large-scale applications.

Original languageEnglish (US)
Pages (from-to)271-299
Number of pages29
JournalDiscrete Event Dynamic Systems: Theory and Applications
Issue number2-3
StatePublished - Jul 1993
Externally publishedYes


  • discrete event dynamic systems
  • discrete event simulation
  • hierarchical modeling
  • stochastic Petri nets
  • stochastic activity networks

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Electrical and Electronic Engineering


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