Simulation Codes

This repository contains a collection of Python scripts that implement various discrete-event simulation models. These scripts are designed to be educational examples of classic simulation problems.

Available Simulations

This repository includes the following simulation models:

1. Able-Baker Customer Care (Multi-Server Queue): A classic multi-server queuing system.
2. Single-Server Queue: A fundamental queuing system with one server.
3. Multi-Server Queue with Finite Capacity: A multi-server system with a limited waiting line.

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1. Able-Baker Customer Care Simulation

This script (able-baker.py) simulates a multi-channel queueing system. It models a customer care center with two servers, Able and Baker, who have different service time distributions.

How it Works

• Arrivals: Callers arrive at random intervals.
• Server Choice: Arriving callers are served by Able if free, otherwise by Baker if free.
• Queue: If both servers are busy, callers wait in an infinite-capacity queue.
• Metrics: The simulation tracks caller delays and server idle time.

The original script has been refactored to use a more modular, class-based design that follows modern Python best practices.

How to Run

python able-baker.py

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2. Single-Server Queue Simulation

This script (single_server_queue.py) implements a basic single-server queuing system. It is a fundamental model for understanding queuing theory.

How it Works

• Arrivals: Customers arrive at random intervals.
• Server: There is only one server to handle the customers.
• Queue: If the server is busy, customers wait in an infinite-capacity queue.
• Metrics: The simulation tracks customer delays and server idle time.

How to Run

python single_server_queue.py

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3. Multi-Server Queue with Finite Capacity

This script (finite_queue_simulation.py) is a variation of the Able-Baker model. It features two servers but introduces a queue with a limited capacity.

How it Works

• Arrivals: Callers arrive at random intervals.
• Server Choice: Servers are chosen based on availability, similar to the Able-Baker model.
• Finite Queue: If both servers are busy, callers can wait in a queue of a fixed size.
• Balking: If a caller arrives when the queue is full, they "balk"—leave the system without being served.
• Metrics: The simulation tracks delays, server utilization, and the number of balked callers.

How to Run

python finite_queue_simulation.py