Publication Date

Spring 4-25-2019


School of Engineering and Computational Sciences


Engineering: Industrial and Systems


Drone Traveling Salesman Problem, Traveling Salesman Problem, Ant Colony Optimization


Industrial Engineering | Operational Research | Systems Engineering


In recent years, major companies have done research on using drones for parcel delivery. Research has shown that this can result in significant savings, which has led to the formulation of various truck and drone routing and scheduling optimization problems. This paper explains and analyzes a new approach to the Drone Traveling Salesman Problem (DTSP) based on ant colony optimization (ACO).

The ACO-based approach has an acceptance policy that maximizes the usage of the drone. The results reveal that the pheromone causes the algorithm to converge quickly to the best solution. The algorithm performs comparably to the MIP model, CP model, and EA of Rich & Ham (2018), especially in instances with a larger number of stops.

DTSP_ACOTop10.m (26 kB)
MATLAB Source Code for the Probabilistic, Greedy Algorithm to the Drone Traveling Salesman Problem

NearestNeighbor.m (1 kB)
MATLAB Source Code for the Nearest Neighbor Heuristic (Subfunction Used by DTSP_ACOTop10.m)