OPTIMIZATION OF DESTINATIONS AND PATHS FOR QUADCOPTER DELIVERY OF AMAZON PARCELS_Code.zip

The aim of this project is to optimize a
package delivery quadcopter route in a heavily-populated city. Current
limitations on using quadcopters for delivering packages prevent one quadcopter
from carrying more than one package in one trip. The research is motivated by
an interest in investigating the economic benefits of a quadcopter carrying
more than one package at a time, enabling visits to multiple destinations
before returning to a base station.<br>
This problem comprises two components: a path optimization to find the distance
between destinations, and a destination optimization to find the optimal tour
that passes through all destinations. The standard path optimization was solved
using a gradient-based optimizer (MATLAB function fmincon), and a genetic
algorithm was used to solve the destination optimization problem. Constraints added
to the destination optimization problem, including the total weight of packages
carried by a single quadcopter and the total distance of a single subtour, help
make the model developed in this problem more realistic.