Algorithms for the generation of state-level representations of stochastic activity networks with general reward structures

M. A. Qureshi, William H Sanders, A. P.A. van Moorsel, R. German

Research output: Contribution to conferencePaper

Abstract

Stochastic Petri nets (SPNs) and extensions are a popular method for evaluating a wide variety of systems. In most cases, their numerical solution requires generating a state-level stochastic process, which captures the behavior of the SPN with respect to a set of specified performance measures. These measures are commonly defined at the net level by means of a reward variable. In this paper, discussed are issues regarding the generation of state-level reward models for systems specified as stochastic activity networks (SANs) with 'step-based reward structures.' While discussing issues related to the generation of the underlying state-level reward model, an algorithm to determine whether a given SAN is 'well-specified' is provided.

Original languageEnglish (US)
Pages180-190
Number of pages11
StatePublished - Dec 1 1995
EventProceedings of the 6th International Workshop on Petri Nets and Performance Models - Durham, NC, USA
Duration: Oct 3 1995Oct 6 1995

Other

OtherProceedings of the 6th International Workshop on Petri Nets and Performance Models
CityDurham, NC, USA
Period10/3/9510/6/95

ASJC Scopus subject areas

  • Applied Mathematics
  • Modeling and Simulation

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    Qureshi, M. A., Sanders, W. H., van Moorsel, A. P. A., & German, R. (1995). Algorithms for the generation of state-level representations of stochastic activity networks with general reward structures. 180-190. Paper presented at Proceedings of the 6th International Workshop on Petri Nets and Performance Models, Durham, NC, USA, .