PySPN: An Extendable Python Library for Modeling & Simulation of Stochastic Petri Nets

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 introduce PySPN, a flexible and easily extendable Python library for Modeling & Simulation (M &S) of SPNs. PySPN aims 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.