https://github.com/pythonhealthdatascience/intro-open-sim
An introduction to building open Descrete-Event Simulation (DES) in Python
https://github.com/pythonhealthdatascience/intro-open-sim
Last synced: 7 months ago
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An introduction to building open Descrete-Event Simulation (DES) in Python
- Host: GitHub
- URL: https://github.com/pythonhealthdatascience/intro-open-sim
- Owner: pythonhealthdatascience
- License: mit
- Created: 2024-10-02T15:30:18.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-05-04T14:49:32.000Z (7 months ago)
- Last Synced: 2025-05-04T15:34:47.168Z (7 months ago)
- Language: Jupyter Notebook
- Homepage: https://pythonhealthdatascience.github.io/intro-open-sim/
- Size: 2.92 MB
- Stars: 6
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
- Citation: CITATION.cff
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[](https://doi.org/10.5281/zenodo.13971858)
# An introduction to Discrete-Event Simulation (DES) using Free and Open Source Software
> Work in progress. This is tutorial was initially prepared for the [Operational Research Society 12th Simulation Workshop in 2025 (SW25)](https://www.theorsociety.com/ORS/ORS/Events/2025/Simulation-Workshop/SW25.aspx). It has been adapted from our [template repository](https://github.com/pythonhealthdatascience/stars-simpy-jupterlite) for sharing `simpy` models with JupyterLite. The SW25 tutorial paper is now open access CC-BY 4.0: [https://doi.org/10.36819/SW25.004](https://doi.org/10.36819/SW25.004)
## 🧑💻 1. Tutorial
We begin with two notebooks that introduce some basic concepts for creating DES in python:
* `01_sampling.ipynb`: Sampling from statistical distributions using `numpy`
* `02_basic_simpy.ipynb`: Creating simple `simpy` DES models that make use of `numpy` sampling
Your understanding of these is then tested in:
* `03a_exercise1.ipynb`: Exercise to testing understanding of the basics of `simpy` and `numpy`
* `03b_exercise1_solutions.ipynb`: Example solutions for the exercise
We then move on to some more advanced concepts, and create a full process model:
* `04_111_model.ipynb`: Full `simpy` process model, creating a model for a 111 call centre
* `05_basic_results.ipynb`: Collecting results from a single run by storing process metrics during a run and performing calculations at the end
* `06a_basic_results_exercise.ipynb`: An exercise to practice collecting results from a `simpy` model.
* `07_experiments.ipynb`: our approach to setting up a model for multiple replications, experimentation, and common random numbers
* `08_full_model.ipynb`: an extended version of the 111 call centre process. We also introduce a warm-up period
* `09_time_weighted_calcs.ipynb`: An alternative approach to collects results for queue length and resource utilisation.
## 🔧 2. Set-up
To run the notebooks in this tutorial, you can either run via your browser or locally...
### 2.1 Running notebooks within your browser
This tutorial has been set up to run on your browser without the need to install any components. This is achieved using Web Assembly technology i.e. [JupterLite](https://github.com/jupyterlite/jupyterlite) and [xeus-python](https://github.com/jupyter-xeus/xeus-python). A model notebook is downloaded to your local machine and all dependencies are pre-installed via conda-forge. The model then lives in the browsers cache. You can make changes to the model or create new files and these are persisted (until the browser cache is cleared).
> ✨ **To access this tutorial in your browser:** .
### 2.2 Running notebooks locally on your machine
#### 2.2.1 Downloading the code
Either clone the repository using git or click on the green "code" button and select "Download Zip".
```bash
git clone https://github.com/pythonhealthdatascience/intro-open-sim.git
```
#### 2.2.2 Installing dependencies and running JupyterLab
All dependencies can be found in [`binder/environment.yml`]() and are pulled from conda-forge. To run the code locally, we recommend installing [miniforge](https://github.com/conda-forge/miniforge);
> miniforge is FOSS alternative to Anaconda and miniconda that uses conda-forge as the default channel for packages. It installs both conda and mamba (a drop in replacement for conda) package managers. We recommend mamba for faster resolving of dependencies and installation of packages.
navigating your terminal (or cmd prompt) to the directory containing the repo and issuing the following command:
```
mamba env create -f binder/environment.yml
```
Activate the mamba environment using the following command:
```
mamba activate simpy_tutorial
```
You can then run the notebooks in `content/` locally on your machine using JupyterLab. Issue the following command and JupyterLab will open in your browser. Notebooks are in the `content/` directory.
```
jupyter-lab
```
## 📝 3. Citation
If the work is of use to you we would value a citation! Please cite the [SW25 tutorial paper](https://doi.org/10.36819/SW25.004) and the code repository.
```bibtex
@inproceedings{monks2025building,
title={Building Healthcare Discrete-Event Simulation Models
in Free and Open Source Software: An Introductory Tutorial},
author={Monks, Thomas and
Harper, Alison and
Heather, Amy and
Mayne, Andrew and
Mustafee, Navonil},
booktitle={Proceedings of the Operational Research Society Simulation Workshop 2025 (SW25)},
editor={Luis, M. and Harper, A. and Monks, T. and Mustafee, N.},
year={2025},
doi={10.36819/SW25.004},
url={https://doi.org/10.36819/SW25.004},
organization={Operational Research Society}
}
```
```bibtex
@software{open_sim_tutorial,
author = {Monks, Thomas and
Harper, Alison and
Heather, Amy},
title = {An introduction to Discrete-Event Simulation using
Free and Open Source Software.
},
month = oct,
year = 2024,
publisher = {Zenodo},
version = {v0.2.0},
doi = {10.5281/zenodo.13971859},
url = {https://doi.org/10.5281/zenodo.13971859},
}
```
## 🤝 4. Acknowledgements
We would like to thank the [JupterLite](https://github.com/jupyterlite/jupyterlite) and [xeus-python](https://github.com/jupyter-xeus/xeus-python) developers for making this work possible. This discrete-event simulation focussed tutorial was based on the learning materials and template provided by [Jupyterlite xeus-python demo](https://github.com/jupyterlite/xeus-python-demo) and [tutorial given at PyData 2023](https://www.youtube.com/watch?v=WXRslU9D3bo) by Jeremy Tuloup.
## 💰 5. Funding
STARS is supported by the Medical Research Council [grant number [MR/Z503915/1](https://gtr.ukri.org/projects?ref=MR%2FZ503915%2F1)].