Drone Delivery Management
In the entire supply chain, the final leg of the delivery- last-mile delivery is the biggest cost driver that accounts for 30% of the supply chain costs. It is also one of the most labour-intensive and time-consuming part of a delivery network. Therefore, the next big step in delivery services is drone-based delivery. A major pain point for advancement in this area is the efficient routing and distribution of delivery jobs to a fleet of drones
- Automate the delivery Managment System. Reducing overall delivery cost and optimization of the battery usage. We calculate the most efficient path using the capacitated vehicle routing problem (CVRP) along with the Genetic algorithm. The capacitated vehicle routing problem (CVRP) is a VRP in which vehicles with limited carrying capacity need to pick up or deliver items at various locations.
- We have taken special care to give priority to the Emergency delivery and Prime Customer
- The code shows the real time tracking of the drone in the google maps
- Using Artificial Intelligence to provide the optimal route for the drone delivery.
- Using Non-dominated Sorting Genetic Algorithm - II for multi objective optimization like minimizing number of drones required and overall cost
- Managing multi-packet deliveries constraint to payload capacity and battery capacity of drones.
- Optimized D’Andrea equation for battery consumption of drones which considers various factors such as payload, air resistance, battery cost, life cycle, and cost of electricity usage.
- Create a virtual environment somewhere in your project directory and activate it.
python3 -m venv venv
source venv/bin/activate
- Install all dependencies from requirements.txt
pip install -r requirements.txt
- Now makemigrations and run them.
cd dronehackon
python manage.py makemigrations
python manage.py migrate
- Create super user
python manage.py createsuperuser
- Run local test server.
python manage.py runserver