SmartRoute - Optimized Delivery Routing & Time Prediction

SmartRoute is a comprehensive logistics optimization system designed to streamline delivery operations. It provides tools for optimizing vehicle routes, predicting delivery times, and offering real-time operational insights. The system leverages OpenStreetMap data, machine learning models, and a dynamic dashboard for efficient fleet management.

Key Features

1. Optimized Route Planning

• Utilizes shortest path algorithms and Traveling Salesman Problem (TSP) solutions to determine the most efficient delivery routes.
• Integrates with OpenStreetMap for accurate road network data.

2. Predictive Delivery Time Estimation

• Employs machine learning models to predict delivery times based on factors such as distance, traffic, and weather conditions.
• Continuously improves predictions with real-time data.

3. Dynamic Vehicle Assignment

• Assigns orders to vehicles dynamically based on capacity, priority, and weight.
• Ensures optimal utilization of fleet resources.

4. Interactive Dashboard

• Displays real-time route visualizations and delivery metrics.
• Provides operational insights to help managers make informed decisions.

5. Scalable and Containerized

• Fully Dockerized deployment with a FastAPI backend and React frontend.
• Scalable architecture to handle growing business needs.

Installation & Setup

Prerequisites

• Docker and Docker Compose installed on your system.
• Node.js and npm installed for frontend development.

Steps to Run

1. Clone the repository:

   git clone https://github.com/LohithVattikuti/SmartRoute-Optimized-Delivery-Routing-Time-Prediction.git
   cd SmartRoute-Optimized-Delivery-Routing-and-Time-Prediction

2. Start the backend and frontend services using Docker Compose:

   docker-compose up --build

3. Access the application:

   • Backend API: http://localhost:8000
   • Frontend Dashboard: http://localhost:3000

Tech Stack

Backend

• Framework: FastAPI (Python)
• Libraries: OpenStreetMap (OSMnx), NetworkX
• Database: PostgreSQL

Frontend

• Framework: React (JavaScript)
• Libraries: Google Maps API, Tailwind CSS

Machine Learning

• Libraries: Scikit-learn, RandomForest, Pandas, NumPy
• Models: Delivery time prediction using regression algorithms

Deployment

• Tools: Docker, Docker Compose

How It Works

1. Route Optimization:

   • The system calculates the shortest and most efficient delivery routes using advanced algorithms.
   • Routes are visualized on an interactive map.

2. Delivery Time Prediction:

   • Machine learning models predict delivery times based on real-world factors like traffic and weather.
   • Predictions are displayed on the dashboard for better planning.

3. Dynamic Dashboard:

   • A user-friendly dashboard provides real-time insights into delivery operations.
   • Managers can monitor vehicle locations, delivery statuses, and overall performance.

Future Enhancements

• Integration with third-party logistics APIs for real-time traffic and weather updates.
• Support for multi-city delivery operations.
• Enhanced machine learning models for better predictions.
• Mobile app for drivers to receive real-time updates and instructions.

License

This project is licensed under the MIT License. Feel free to use and modify it as needed.