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

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Simulation Tools and Techniques (SIMUtools 2023)

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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 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.

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Authors and Affiliations

  1. Mærsk Mc-Kinney Møller Institute, University of Southern Denmark, Odense, Denmark

    Jonas Friederich & Sanja Lazarova-Molnar

  2. Institute AIFB, Karlsruhe Institute of Technology, Karlsruhe, Germany

    Sanja Lazarova-Molnar

Authors
  1. Jonas Friederich
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  2. Sanja Lazarova-Molnar
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Corresponding author

Correspondence to Jonas Friederich .

Editor information

Editors and Affiliations

  1. Universidad de Sevilla, Sevilla, Spain

    José-Luis Guisado-Lizar

  2. Universidad de Sevilla, Sevilla, Sevilla, Spain

    Agustín Riscos-Núñez

  3. Universidad de Sevilla, Sevilla, Spain

    María-José Morón-Fernández

  4. Carleton University, Ottawa, IL, USA

    Gabriel Wainer

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© 2024 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Friederich, J., Lazarova-Molnar, S. (2024). PySPN: An Extendable Python Library for Modeling & Simulation of Stochastic Petri Nets. In: Guisado-Lizar, JL., Riscos-Núñez, A., Morón-Fernández, MJ., Wainer, G. (eds) Simulation Tools and Techniques. SIMUtools 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 519. Springer, Cham. https://doi.org/10.1007/978-3-031-57523-5_6

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  • DOI: https://doi.org/10.1007/978-3-031-57523-5_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-57522-8

  • Online ISBN: 978-3-031-57523-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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