Description
This project is a school project developed in collaboration with CMA CGM, focusing on optimizing maritime routes using Python. The project involved analyzing a provided database of maritime routes and optimizing these routes based on three key criteria: time, distance, and fuel consumption. Utilizing graph theory, the project aimed to visualize and recommend the most efficient paths for shipping routes.
Key Features
- Route Optimization: Identifies the most efficient maritime routes based on time, distance, and fuel consumption.
- Data Visualization: Visualizes maritime routes and optimization results using Python’s data visualization libraries.
- Geographical Data Scraping: Collects and integrates geographical data to enhance route accuracy and recommendations.
- Graph Theory Application: Employs graph theory to model and solve the optimization problem.
Technologies Used
- Python
- Graph theory algorithms
- Data visualization libraries (e.g., Folium, NetworkX, Pyvis)
- Web scraping tools for geographical data (e.g., BeautifulSoup, Selenium)
Role and Contributions
- Analyzed the provided maritime route database to extract relevant information.
- Implemented graph theory algorithms to optimize routes based on the specified criteria.
- Scraped and integrated geographical data to improve the accuracy of route visualizations.
- Created visual representations of routes and optimizations to aid in decision-making.
- Developed recommendations for route optimization considering time, distance, and fuel consumption.
Outcome
Successfully optimized maritime routes for CMA CGM, demonstrating significant reductions in travel time, distance, and fuel consumption. The project highlighted advanced skills in Python programming, data analysis, and the application of graph theory, resulting in valuable recommendations for maritime logistics optimization. The jury of this Business Deep Dive personally selected this project as the winner.