2016
DOI: 10.1007/978-3-319-30599-8_9
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30 Years of GreatSPN

Abstract: GreatSPN is a tool for the stochastic analysis of systems modelled as (stochastic) Petri nets. This chapter describes the evolution of the GreatSPN framework over its lifespan of 30 years, from the first stochastic Petri net analyzer implemented in Pascal, to the current, fancy, graphical interface that supports a number of different model analyzers. This chapter reviews, with the help of a manufacturing system example, how GreatSPN is currently used for an integrated qualitative and quantitative analysis of P… Show more

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Cited by 60 publications
(37 citation statements)
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“…The repository includes the nets in the file formats required for the three tools used in this paper, i.e., GreatSPN [14], PeabraiN [15], and TimeNET [16]. The file format of GreatSPN does not follow any particular standard, while the file formats supported by PeabraiN and TimeNET are compliant to the adopted ISO standard for description of PNML [13] or have a corresponding import/export converter.…”
Section: A Generalized Stochastic Petri Net Repositorymentioning
confidence: 99%
See 1 more Smart Citation
“…The repository includes the nets in the file formats required for the three tools used in this paper, i.e., GreatSPN [14], PeabraiN [15], and TimeNET [16]. The file format of GreatSPN does not follow any particular standard, while the file formats supported by PeabraiN and TimeNET are compliant to the adopted ISO standard for description of PNML [13] or have a corresponding import/export converter.…”
Section: A Generalized Stochastic Petri Net Repositorymentioning
confidence: 99%
“…Furthermore, we also provide a tool to interchange the models between different tools and the shell scripts used to launch experimentation as a way to make easier the reproducibility of the experiments carried out in this paper. As an example analysis, this paper furthermore applies the proposed evaluation framework to three selected event-driven simulators, namely GreatSPN [14], PeabraiN [15], and TimeNET [16]. A comparative analysis among these three tools is carried out and results are reported from the user perspective.…”
mentioning
confidence: 99%
“…GreatSPN [3] is an open source framework 3 for modeling and analyzing Petri nets, which includes several tools accessible either through a common graphical interface [2], with a mode of interaction that was recently re-designed to support teaching [5], or through in-line commands for expert users. With GreatSPN the user can draw Petri nets (place/transition nets, colored nets and their stochastic variations) interactively, can compute (and visualize) the RG explicitly or symbolically, can analyze net properties (both qualitative and stochastic), can solve stochastic and Ordinary differential equations/Stochastic differential equations (ODE/SDE) systems, and can model-check CTL logic properties as well as performing stochastic model checking for properties defined using automata (the CSL TA logic), and other advanced features.…”
Section: Greatspnmentioning
confidence: 99%
“…The Component Method, including the component optimization and the greedy heuristics, have been implemented as part of the GreatSPN solver for non-ergodic DSPNs. GreatSPN [14] is a tool for Petri net definition and analysis developed mainly at the University of Torino during the last 30 years. GreatSPN has been recently renovated to include a new Java-based interface with colored and plain token game simulation, model checking for branching and stochastic logics, a new solver for DSPN, and additional facilities for model composition and for performing multiple experiments.…”
Section: Component Based Solution Of Mrpmentioning
confidence: 99%
“…Indeed there are cases in which the solution of a component is equivalent to the transient solution of a CTMC. The method has been implemented inside the GreatSPN tool [14] as a solver for non-ergodic DSPNs and it is also part of the model-checker MC4CSLTA [15] of the stochastic logic CSL TA [16]. This paper shows that the Component Method applied to MPS nets identifies the same components and has the same time complexity as the ad-hoc techniques developed for MPS and implemented in DEEM.…”
Section: Introduction and State Of Artmentioning
confidence: 99%