Stochastic Petri nets: an elementary introduction
Advances in Petri nets 1989
A decomposition approach for stochastic reward net models
Performance Evaluation
Structural techniques and performance bounds of stochastic Petri net models
Advances in Petri Nets 1992, The DEMON Project
On the Product Form Solution for Stochastic Petri Nets
Proceedings of the 13th International Conference on Application and Theory of Petri Nets
A framework for rare event simulation of stochastic Petri nets using “RESTART”
WSC '96 Proceedings of the 28th conference on Winter simulation
The Möbius State-Level Abstract Functional Interface
TOOLS '02 Proceedings of the 12th International Conference on Computer Performance Evaluation, Modelling Techniques and Tools
State Space Construction and Steady--State Solution of GSPNs on a Shared--Memory Multiprocessor
PNPM '97 Proceedings of the 6th International Workshop on Petri Nets and Performance Models
The Möbius state-level abstract functional interface
Performance Evaluation - Modelling techniques and tools for computer performance evaluation
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We present a decomposition approach for the solution of large generalised stochastic Petri nets using p-invariants to identify submodels. Due to the structure of interfaces between submodels, types of interactions are defined. An interaction graph is derived, in which the information flow among submodels is represented by the direction of arcs. According to solution quality and efficiency, the information graph is refined to get a suitable partition of the model. The submodels of this partition are aggregated in a special way to preserve the interface structure and its throughput. Combination and solution of the aggregates results in a second step to include the interaction influence into interface substitutions. The isolated solution of the expanded submodels with interface substitution results in approximations of the marking probabilities. The solution process may be iterative, depending on the interaction types among submodels in the solution partition. As all steps of the evaluation are based on model structure, the derivation of the reachability set and the corresponding Markov process is avoided.