Research Article
PySPN: An Extendable Python Library for Modeling & Simulation of Stochastic Petri Nets
@INPROCEEDINGS{10.1007/978-3-031-57523-5_6, author={Jonas Friederich and Sanja Lazarova-Molnar}, title={PySPN: An Extendable Python Library for Modeling \& Simulation of Stochastic Petri Nets}, proceedings={Simulation Tools and Techniques. 15th EAI International Conference, SIMUtools 2023, Seville, Spain, December 14-15, 2023, Proceedings}, proceedings_a={SIMUTOOLS}, year={2024}, month={4}, keywords={Stochastic Petri nets Modeling \& Simulation Python}, doi={10.1007/978-3-031-57523-5_6} }- Jonas Friederich
Sanja Lazarova-Molnar
Year: 2024
PySPN: An Extendable Python Library for Modeling & Simulation of Stochastic Petri Nets
SIMUTOOLS
Springer
DOI: 10.1007/978-3-031-57523-5_6
Abstract
Stochastic Petri Nets (SPNs) are a powerful formalism, widely used for modeling complex systems in various domains, ranging from manufacturing and logistics to healthcare and computer networks. In this paper, we introducePySPN, a flexible and easily extendablePythonlibrary for Modeling & Simulation (M &S) of SPNs.PySPNaims to provide researchers, engineers, and simulation practitioners with a user-friendly and efficient toolset to model, simulate, and analyze SPNs, facilitating the understanding and optimization of stochastic processes in dynamic systems.
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