Smart Scheduler

A powerful scheduling application built with modern web technologies.

Technologies

Backend

Frontend

Algorithms

Data Visualization

Dependencies

Backend Dependencies

• Flask (2.3.3): A lightweight WSGI web application framework.
• python-dotenv (1.0.0): Loads environment variables from .env files.
• Flask-SQLAlchemy (3.1.1): Adds SQLAlchemy support to Flask application.
• Flask-Migrate (4.0.5): Handles SQLAlchemy database migrations for Flask.
• Flask-Cors (4.0.0): Handles Cross-Origin Resource Sharing (CORS).
• Jupyter Notebook: Interactive computing environment for data analysis and visualization.
• Matplotlib: Comprehensive library for creating static, animated, and interactive visualizations.
• Pandas: Data analysis and manipulation library.
• NumPy: Library for numerical computing with Python.

Frontend Dependencies

• React (19.0.0): JavaScript library for building user interfaces.
• React DOM (19.0.0): React package for DOM rendering.
• React Router DOM (7.5.0): Declarative routing for React.
• TypeScript (5.7.2): Strongly typed programming language.
• Vite (6.2.0): Next generation frontend tooling.
• TailwindCSS (4.1.4): Utility-first CSS framework.
• Zustand (5.0.3): State management solution using simplified flux principles.
• Axios (1.8.4): Promise-based HTTP client for the browser and Node.js.
• Recharts (2.15.2): Redefined chart library built with React and D3.
• Lucide React (0.488.0): Icon library for React applications.
• Radix UI (1.2.0): Unstyled, accessible UI component library.
• Class Variance Authority (0.7.1): Utility for creating consistent component variants.
• Tailwind Merge (3.2.0): Utility for merging Tailwind CSS classes.
• TW Animate CSS (1.2.5): Animation library for Tailwind CSS.

Optimization Algorithms

The application uses several advanced optimization algorithms to generate efficient schedules:

• Ant Colony Optimization: Uses pheromone trails and heuristic information to find optimal solutions through collaborative search.
• Multi-Objective Genetic Algorithm (MOGA): Evolves solutions using crossover and mutation while optimizing for multiple competing objectives.
• Simple Genetic Algorithm: A streamlined genetic approach focused on quick convergence to good solutions.
• Hill Climbing: A local search algorithm that continuously moves to neighboring states to find better solutions.

Data Analysis and Visualization

The backend utilizes Jupyter notebooks for algorithm development, testing, and visualization:

• Algorithm Prototyping: Jupyter notebooks provide an interactive environment for developing and testing scheduling algorithms.
• Performance Analysis: Visualize algorithm performance metrics including convergence rates and solution quality.
• Schedule Visualization: Generate visual representations of created schedules for easier interpretation.
• Comparative Analysis: Compare different algorithm approaches and parameter configurations to identify optimal solutions.

Jupyter notebooks are located in the backend/jupyter/ directory and include:

• ant_colony.ipynb: Implementation and visualization of the Ant Colony Optimization algorithm
• Additional algorithm notebooks for other optimization approaches

Using Jupyter Notebooks

To run the Jupyter notebooks:

cd backend
pip install jupyter matplotlib pandas numpy
jupyter notebook

Navigate to the jupyter/ directory to access the algorithm notebooks.

Getting Started

Backend Setup

cd backend
pip install -r requirements.txt
python -m venv venv
source venv/Scripts/activate
python app.py

Frontend Setup

cd frontend
npm install
npm run dev

License

MIT