Tools to support Discrete-Event Simulation (DES) and Monte-Carlo Simulation education and practice

sim-tools is being developed to support Discrete-Event Simulation (DES) and Monte-Carlo Simulation education and applied simulation research. It is MIT licensed and freely available to practitioners, students and researchers via PyPi and conda-forge

1. Deliver high quality reliable code for DES and Monte-Carlo Simulation education and practice with full documentation.
2. Provide a simple to use Pythonic interface.
3. To improve the quality of simulation education using FOSS tools and encourage the use of best practice.

• Thomas Monks   

• Amy Heather   

• Alison Harper   

1. Implementation of classic Optimisation via Simulation procedures such as KN, KN++, OBCA and OBCA-m
2. Theoretical and empirical distributions module that includes classes that encapsulate a random number stream, seed, and distribution parameters.
3. An extendable Distribution registry that provides a quick reproducible way to parameterise simulation models.
4. Implementation of Thinning to sample from Non-stationary Poisson Processes (time-dependent) in a DES.
5. Automatic selection of the number of replications to run via the Replications Algorithm.
6. EXPERIMENTAL: model trace functionality to support debugging of simulation models.

pip install sim-tools

conda install -c conda-forge sim-tools

mamba is a FOSS alternative to conda that is also quicker at resolving and installing environments.

mamba install sim-tools

• Online documentation: https://sim-tools.github.io/sim-tools
• Introduction to DES in python: https://health-data-science-or.github.io/simpy-streamlit-tutorial/

If you use sim-tools for research, a practical report, education or any reason please include the following citation.

Monks, T., Heather, A., Harper, A. (2025). sim-tools: fundamental tools to support the simulation process in python. Zenodo. https://doi.org/10.5281/zenodo.4553641.

@software{sim_tools,
  author       = {Thomas Monks and Amy Heather and Alison Harper},
  title        = {sim-tools: fundamental tools to support the simulation process in python},
  year         = {2025},
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.4553641},
  url          = {https://doi.org/10.5281/zenodo.4553641}
}

• Optimisation Via Simulation

All contributions are welcome! Please see CONTRIBUTING.md for instructions on how to contribute.

Thanks goes to these wonderful people (emoji key):

Tom Monks 💻 🔣 🎨 📖 🤔 🚇 🚧 👀 ⚠️ ✅

Amy Heather 🐛 💻 📖 🤔 🚇 🚧 ⚠️

Alison Harper 📖

Sammi Rosser 🐛 💻

This project follows the all-contributors specification. Contributions of any kind welcome!