Task 3

Emergency Department Discrete Event Simulator

This project models and simulates patient flow through an Emergency Department (ED) using discrete event simulation (DES).
It provides tools to run simulations under different scenarios (current, predicted, recommended) and visualize the results.

Requirements

• Python3.11

pip install -r requirements.txt

• Directory tree

project-root/
│
├── assets/                     # Diagrams, reference materials
│   └── Process Flow HTX.png
│
├── logs/                       # Simulation run logs
│   ├── simulation_current.log
│   ├── simulation_predicted.log
│   └── simulation_recommended.log
│
├── notebooks/                  # Jupyter notebooks for analysis/visualization
│   ├── visualisations_current.ipynb
│   ├── visualisations_predicted.ipynb
│   └── visualisations_recommended.ipynb
│
├── scripts/                    # Core simulation code
│   ├── main_current.py         # Entry point: current scenario
│   ├── main_predicted.py       # Entry point: predicted scenario
│   ├── main_reco.py            # Entry point: recommended scenario
│   ├── ED.py                   # Emergency Department logic
│   ├── FT.py                   # Fast track logic
│   ├── Triage.py               # Triage logic
│   ├── Lab_Test.py             # Lab test logic
│   ├── Transfer.py             # Transfers between units
│   ├── Exit.py                 # Patient discharge/exit
│   ├── Patient.py              # Patient agent definition
│   └── Sim.py                  # Simulation runner/engine
│
├── .gitignore
├── requirements.txt            # Python dependencies
└── README.md                   # Project documentation

Running the Simulation

We have simulated 3 scenerios and have added the visualisations to the powerpoint.

• Current System

python scripts/main_current.py

• Predicted System (10% Increase in Patient Volume) with Current Resources

python scripts/main_predicted.py

• Recommended System

python scripts/main_reco.py

Logs of the simulator will be saved in the logs/ folder

Visualisation and Interpretation of Simulation Outputs

Jupyter notebooks under notebooks/ provide visualizations of simulation outputs which can be found in Task 4 Presention.pptx:

• visualisations_current.ipynb – analysis of the current ED configuration
• visualisations_predicted.ipynb – projections under forecasted load
• visualisations_recommended.ipynb – evaluation of proposed improvements

The Main Key Performance Output Variable (KPOV) measured was the Percentage Utilisation of each Resource. This tells us how much of the available time a resource (e.g., doctors, triage nurses, treatment rooms) is actively being used.

How to interpret: A low utilisation (e.g., <50%) suggests underused capacity—resources may be idle for long stretches. A moderate utilisation (50–80%) typically reflects efficient use without excessive strain. A very high utilisation (e.g., >85–90%) may signal overloading, where staff or resources are constantly busy, increasing the likelihood of delays and bottlenecks.