Standard performance evaluation methods for discrete-state stochastic models such as Petri nets either generate the reachability graph followed by a numerical solution of equations, or use some variant of simulation. Both methods have characteristic advantages and disadvantages depending on the size of the reachability graph and type of performance measure. The paper proposes a hybrid performance evaluation algorithm for Stochastic Petri Nets that integrates elements of both methods. It automatically adapts its behavior depending on the available size of main memory and number of model states. As such, the algorithm unifies simulation and numerical analysis in a joint framework. It is proved to result in an unbiased estimator whose variance tends to zero with increasing simulation time; furthermore, its applicability is demonstrated through case studies.
CITATION STYLE
Zimmermann, A., Hotz, T., & Lavista, A. C. (2017). A hybrid multi-trajectory simulation algorithm for the performance evaluation of stochastic petri nets. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10503 LNCS, pp. 107–122). Springer Verlag. https://doi.org/10.1007/978-3-319-66335-7_7
Mendeley helps you to discover research relevant for your work.