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

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.

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.