Muhuthu/TSP_notebook

This repository contains a Python solution for optimizing product distribution routes across multiple locations in Kenya using the Traveling Salesman Problem (TSP) approach. The implementation provides an end-to-end solution from geocoding locations to visualizing the optimal route. ## 🌍 Optimal Route Planning for Market Distribution ## 📋 Project Description This repository contains a **Python solution** for optimizing product distribution routes across Kenya using advanced **Traveling Salesman Problem (TSP)** algorithms. The system provides a complete pipeline from location geocoding to interactive route visualization. ## ✨ Key Features ### 🗺 Geospatial Intelligence - Automatic coordinate fetching using Nominatim API - Location validation and error handling ### 📏 Distance Matrix - Accurate geodesic distance calculations. - Customizable distance metrics. ### ⚡ TSP Solvers - Brute-force implementation (for small datasets) - Heuristic approaches (for larger datasets) - Performance benchmarking. ### 🎨 Visualization - Interactive Folium maps - Route animation capabilities - Custom marker styling ## 🛠 Technology Stack | Category | Technologies | |-----------------|---------------------------------------| | **Core** | Python 3.10+,jupyter | | **Data** | NumPy, Pandas | | **Visualization**| Matplotlib, Seaborn, Folium | | **Geospatial** | Geopy, Nominatim | | **Algorithms** | Custom TSP implementations | ## 💼 Use Cases pie title Application Areas "Product Distribution" : 35 "Delivery Services" : 30 "Field Operations" : 20 "Travel Planning" : 15 ## 🚀 Getting Started ### Installation. # Clone repository git clone github.com cd route-optimizer # Install dependencies pip install -r requirements.txt. # Launch Jupyter jupyter notebook OptimalRoutePlanner.ipynb ## 📂 Repository Structure TSP_notebook/ ├── 📒 OptimalRoutePlanner.ipynb # Main implementation ├── 📝 requirements.txt # Dependencies ├── 📄 README.md # Documentation ├── 📁 data/ # Sample datasets └── 📁 images/ # Visualization …