🚀 Single-Channel Queue Simulation System (C# WinForms)

A Windows Forms application built in C# to simulate a single-channel, single-server queueing system using the Inverse Transform Technique for random variable generation.

📚 Overview (Academic Context)

This project is a practical implementation of "Manual Simulation" concepts. It applies mathematical models and builds stochastic simulation frameworks without relying on built-in search or replace functions. This demonstrates a deep understanding of how random engines operate within simulation software.

The project strictly follows the step-by-step simulation methodology outlined in standard academic curricula (such as the single-server queue examples found in simulation textbooks).

✨ Key Features

• Advanced Setup Interface (Form2):

  • Create probability distribution tables for Interarrival Time and Service Time.
  • Input field for the maximum number of time intervals with strict input validation (int.TryParse).
  • Automated Random Probability Generation: Generates random numbers and applies a custom Normalization algorithm to ensure the total sum of all probabilities equals exactly 1.0.
  • Automatic calculation of Cumulative Probability and the Random Number Assignment (RNA) range using a custom StringBuilder logic.
  • Dynamic, editable DataGridView with smart warnings (if the sum exceeds 1.0, or locks further input if the sum reaches 1.0 early).

• Simulation and Statistics Interface (Form1):

  • Generates separate random numbers for each customer's arrival and service.
  • A custom logic function (LookupValueByRandomNumber) that maps the generated random number to the correct time interval based on the cumulative probability, without using any built-in search functions (like IndexOf, Find, or Replace).
  • A detailed results table displaying (Arrival Time, Start of Service, End of Service, Time in Queue, Time in System, and Server Idle Time).
  • Performance Metrics: Statistical results (e.g., Average Wait Time, Server Utilization) are displayed dynamically in a dedicated Label.
  • Smart Summary Rows: Automatically appends Total and Average rows at the end of the results table with customized background coloring and bold fonts for improved readability.

🛠️ Technologies & Libraries Used

• Language: C# (.NET 6+)
• UI Framework: Windows Forms (WinForms)
• Data Management: System.Data.DataTable and System.Data.DataRow (Enforcing a clear separation between data logic and UI).
• Data Visualization: DataGridView with dynamic data binding.
• Data Structures: System.Collections.Generic.List<T> for storing random numbers before normalization.
• Mathematical Logic: LINQ (Sum, AsEnumerable) and Math.Abs for robust floating-point precision checks.

📖 User Guide (How to Use)

1. Run the Application: The main window (Form1) will appear.
2. Open Settings: Click the Set Up Form button to open the configuration window (Form2).
3. Configure Probabilities (Form2):
   • Under the Time Between Arrival tab, enter the maximum number of time intervals (e.g., 8) and click Input.
   • Fill in the Probability column manually, or click Generate Probability to get a valid normalized set of random numbers.
   • Repeat the exact same steps for the Service Time tab.
   • Note: You cannot input further values in remaining probability rows if the cumulative sum has already reached 1.0.
4. Run Simulation (Form1):
   • Enter the desired number of customers in the Number Of Customers textbox.
   • Click the Simulate button.
5. Read the Results:
   • A detailed simulation table will appear in the DataGridView.
   • Scroll down to see the colored Total and Average summary rows.
   • The performance metrics (e.g., Server Utilization and Average Time in Queue) will be displayed in the Label located above the table.

📊 Implemented Mathematical Algorithms

The simulation relies on a custom probability distribution. The following algorithms are implemented entirely from scratch:

1. Normalization of Random Numbers:
   • Sum = Σ RandomNumbers[i]
   • NormalizationFactor = 1.0 / Sum
   • FinalProbability = RandomNumbers[i] * NormalizationFactor (Ensures the sum of all probabilities exactly equals 1.0).
2. Cumulative Probability Calculation:
   • Cumulative[i] = Cumulative[i-1] + Probability[i]
3. Inverse Transform Technique (Value Mapping):
   • Check if the generated random number RN falls within the range: Cumulative[i-1] < RN <= Cumulative[i].
   • Return the corresponding Time[i].

⚙️ Installation & Local Setup

1. Clone the repository to your local machine.

   git clone https://github.com/ProMoath/Single-Channel-Queue-Simulation.git

2. Open the solution file Simulation.sln using Microsoft Visual Studio 2022 (or newer).
3. Ensure Simulation is set as the Startup Project.
4. Press F5 or click the Run button to launch the application.

📂 Project Structure

• Form1.cs / Form1.Designer.cs: Main window, simulation logic, and results table.
• Form2.cs / Form2.Designer.cs: Setup window for configuring probabilities, cumulative sums, and random generation.
• Program.cs: The application's main entry point.

🤝 License

This project is licensed under the MIT License. You are free to use, modify, and distribute it for academic or personal purposes.

🤝 Contributing

Contributions are welcome! To get started:

# 1. Fork the repository on GitHub

# 2. Create a feature branch
git checkout -b feature/your-feature-name

# 3. Make your changes and commit
git commit -m "feat: describe your change"

# 4. Push your branch
git push origin feature/your-feature-name

# 5. Open a Pull Request on GitHub

📞 Contact

Channel	Details
👨‍💻 Developer	Moath Alshahari
📧 Email	moathalshah2023@gmail.com
🐙 GitHub	@ProMoath

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⭐ Support the Project

If you find this project useful, please give it a star on GitHub and share it with others!