https://github.com/3bbaas/newspaper-seller-simulation-case-study
Newspaper-Seller-Simulation-Case-Study
https://github.com/3bbaas/newspaper-seller-simulation-case-study
case-study modeling newspaper-seller simulation
Last synced: 4 months ago
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Newspaper-Seller-Simulation-Case-Study
- Host: GitHub
- URL: https://github.com/3bbaas/newspaper-seller-simulation-case-study
- Owner: 3bbaas
- Created: 2025-05-04T00:12:58.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-05-05T13:46:14.000Z (about 1 year ago)
- Last Synced: 2025-06-18T04:38:50.567Z (about 1 year ago)
- Topics: case-study, modeling, newspaper-seller, simulation
- Language: Python
- Homepage: https://3bbaas.github.io/Newspaper-Seller-Simulation-Case-Study/
- Size: 6.37 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Newspaper Seller Simulation Case Study
## Description
This repository contains a case study project for the "Modeling & Simulation" subject. The primary objective is to determine the optimal number of newspapers a seller should purchase each day to maximize profits, considering daily demands classified as Good, Fair, or Poor days. The project includes a C++ console application for core simulations, a Python desktop GUI for user interaction, and comprehensive documentation in multiple formats.
### Problem Overview
- **Objective**: Maximize daily profit by determining the quantity of newspapers to purchase.
- **Parameters**:
- Selling price per newspaper $(P)$: \$ $0.50$
- Cost per newspaper $(C)$: \$ $0.33$
- Scrap value per unsold newspaper $(S)$: \$ $0.05$
- Daily demand $(d)$: Varies based on the type of day (Good, Fair, Poor)
- Quantity of newspapers purchased $(X)$
- **Demand Distribution**: Possible demand values are $40, 50, 60, 70, 80, 90, 100$ newspapers, with probabilities depending on the day type.
### Model Formulation
The model calculates daily profit based on the following equations:
- Revenue from sales: $(\min(d, X) \times P)$
- Excess demand: $\max(X, d) - d$ (Note: This represents unsold newspapers, not excess demand in the standard sense)
- Profit per newspaper: $P - C$
- Lost profit: $\text{excess demand} \times (P - C)$
- Number of scraps: $\max(X, d) - d$ (Same as excess demand)
- Salvage from scraps: $\text{Number of scraps} \times S$
- Cost of daily newspapers: $X \times C$
- Daily Profit: $\text{Revenue Sales} - \text{Cost of daily newspapers} - \text{lost Profit} + \text{Salvage from scrap}$
**Note**: The definition of "excess demand" and "lost profit" differs from the standard newsvendor model, where lost profit typically relates to unmet demand $\max(d - X, 0) $. In this model, "excess demand" refers to unsold newspapers $\max(X - d, 0)$, leading to a unique profit calculation: $(P - C) \times \min(d, X) + (S - P) \times \max(X - d, 0)$.
## Project Structure
The repository is organized as follows:
| Folder/File | Description |
|-------------|-------------|
| `Console App/` | Core C++ implementation |
| `main.cpp` | Entry point for the console application simulating the newspaper seller’s decision-making |
| `Desktop-App/` | Python desktop GUI application |
| `main.py` | Entry point for the GUI application |
| `assets/` | Folder with GUI assets (e.g., images, icons) |
| `requirements.txt` | List of Python dependencies for the GUI |
| `Docs/` | Documentation and modeling files |
| `Newspaper Seller Case Study.docx` | Word document with detailed project information |
| `Newspaper Seller Case Study.pdf` | PDF version of the project documentation |
| `Newspaper Seller Problem - Modeling Simulation.xlsx` | Excel spreadsheet for modeling and simulation |
| HTML + CSS Documentation | A simple HTML + CSS documentation (e.g., `index.html`) available in the repository. Open with a web browser for interactive documentation |
## How to Run
### C++ Console Application
1. Ensure a C++ compiler is installed (e.g., [g++](https://gcc.gnu.org/) for Linux/Mac, [Visual Studio](https://visualstudio.microsoft.com/) for Windows).
2. Navigate to the `Console App/` directory.
3. Compile `main.cpp`:
- For g++: `g++ main.cpp -o newspaper_simulation`
- For Visual Studio: Open `main.cpp` in Visual Studio and build the project.
4. Run the executable:
- Linux/Mac: `./newspaper_simulation`
- Windows: `newspaper_simulation.exe`
### Python Desktop GUI
1. Ensure [Python](https://www.python.org/) is installed (check `requirements.txt` for the required version).
2. Navigate to the `Desktop-App/` directory.
3. Install dependencies: `pip install -r requirements.txt`
4. Run the application: `python main.py`
## Documentation
Detailed information about the project, including the problem definition, assumptions, and model formulation, is available in the `Docs/` folder:
- `Newspaper Seller Case Study.docx`: Comprehensive project details in Word format.
- `Newspaper Seller Case Study.pdf`: PDF version for easy sharing and viewing.
- `Newspaper Seller Problem - Modeling Simulation.xlsx`: Excel spreadsheet containing simulation data and modeling.
Additionally, an [online documentation](https://3bbaas.github.io/Newspaper-Selle-rSimulation-Case-Study/) is included in the repository. Locate the `index.html` file (or similar) and open it in a web browser for an interactive overview.
## Team Members:
- [Ahmed Abbas (Me)](https://github.com/3bbaas)
- [Rokiya Abdelsattar](https://github.com/RokiyaAbdElsatar)
- [Ahmed Ibrahim](https://github.com/jamika78)
That's all.