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

Vision for sim-tools

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.

👥 Authors

• Thomas Monks   

• Amy Heather   

• Alison Harper   

Features:

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 reproduible 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.

Installation

Pip and PyPi

pip install sim-tools

Conda-forge

conda install -c conda-forge sim-tools

Mamba

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

mamba install sim-tools

Binder

Learn how to use sim-tools

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

Citation

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}
}

Online Tutorials

• Optimisation Via Simulation

Contributing to sim-tools

Please fork Dev, make your modifications, run the unit tests and submit a pull request for review.

Development environment:

• conda env create -f binder/environment.yml

• conda activate sim_tools

All contributions are welcome!

Tips

Once in the sim_tools environment, you can run tests using the following command:

pytest

To view the documentation, navigate to the top level directory of the code repository in your terminal and issue the following command to build the Jupyter Book:

jb build docs/

To lint the repository, run:

bash lint.sh

NumPy style docstrings are used.