Publication Date
Spring 4-25-2019
School
School of Engineering and Computational Sciences
Major
Engineering: Industrial and Systems
Keywords
Drone Traveling Salesman Problem, Traveling Salesman Problem, Ant Colony Optimization
Disciplines
Industrial Engineering | Operational Research | Systems Engineering
Recommended Citation
Houseknecht, Jessica, "An ACO-Inspired, Probabilistic, Greedy Approach to the Drone Traveling Salesman Problem" (2019). Senior Honors Theses. 849.
https://digitalcommons.liberty.edu/honors/849
Abstract
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.
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)
Included in
Industrial Engineering Commons, Operational Research Commons, Systems Engineering Commons
