Hospital Manager – Emergency Room Simulation 🏥

A real-time Emergency Room simulation built using Python and Pygame. This project models a multi-server priority queueing system using discrete-event simulation principles.

The system simulates stochastic patient arrivals, severity-based prioritization, doctor assignment, and treatment completion in a dynamic graphical environment.

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Game Screenshots

Emergency Room View

Patient Under Treatment

Waiting Area Queue

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Features

• Real-time discrete-event simulation
• Multi-doctor (multi-server) system
• Severity-based priority queue (Critical > Moderate > Minor)
• Heap-based scheduling using heapq
• Adjustable number of doctors
• Adjustable patient arrival probability
• Custom simulation duration (in minutes)
• Start / Pause / Resume / Restart controls
• Scrollable hospital layout
• Live statistics panel (time, queue size, treated count)

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Simulation Model

Patient Severity Structure

Severity	Priority	Service Time (seconds)
Critical	1	20 – 30
Moderate	2	10 – 20
Minor	3	5 – 10

Lower numerical priority means higher urgency.

The system follows a non-preemptive priority queue discipline.

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How It Works

Each simulation step:

1. Increments simulation time
2. Generates patient arrivals probabilistically
3. Updates service time for busy doctors
4. Releases doctors when treatment completes
5. Assigns highest-priority waiting patient
6. Updates waiting time of queued patients

The simulation ends when the defined duration is reached.

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Performance Metrics

The simulation tracks:

• Total patients treated
• Queue length
• Simulation time
• Throughput
• Doctor utilization
• Average waiting time

These metrics allow analysis of congestion and system stability.

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Project Structure

Hospital-Simulation/
│
├── config.py
├── entities.py
├── simulation.py
├── ui.py
├── main.py
├── Assets/

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Installation & Run

Requirements

• Python 3.x
• Pygame

Install Pygame

pip install pygame

Run the Simulation

python main.py

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Mathematical Model

Arrival Rate:

λ = p

Service Rate:

μ = 1 / Avg(Service Time)

Utilization:

ρ = λ / (cμ)

Where:

• c = number of doctors
• Stability condition: ρ < 1

If ρ ≥ 1, the queue grows without bound.

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Concepts Demonstrated

• Discrete Event Simulation
• Priority Queues
• Multi-Server Queueing Systems
• Stochastic Modeling
• Event-Driven Programming
• Real-Time GUI Rendering

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This project serves as a dynamic experimental tool for analyzing emergency department congestion, resource allocation, and system stability under uncertainty.