Aerial drones offer a distinct potential to reduce the delivery time and energy consumption for the delivery of time-sensitive and small products. However, there is still a need in the relevant industry to understand the performance of drone-based delivery under different business needs and drone operating conditions. We studied a drone deployment optimization problem for direct delivery of time-sensitive products with release dates to customers maintaining a specified time window. This paper presents a new mixed-integer programming model, new valid inequalities, a new greedy heuristic algorithm, and a Genetic algorithm to help business owners optimally schedule and route their drone fleet minimizing the required fleet size, the required number of additional batteries, and total energy consumption. A realistic feature of the optimization method is that instead of replacing the drone battery after each return to the depot, it keeps track of the remaining energy in the drone battery and decides on battery replacements accounting for the drone routing and the user-specified minimum required battery energy. Numerical results based on real data from drone flight tests and prepared food delivery industry provide insights into the effect of different practical drone operating parameters on the required fleet size, the required number of battery replacements, and energy consumption. Here, results demonstrate that the proposed heuristic algorithm substantially outperforms the accelerated CPLEX in runtime while sacrificing the solution quality by a small amount. Additionally, results show that using a mixed fleet of hexacopter and quadcopter drones reduces the total energy consumption by 48.52% compared to using a homogeneous fleet of only hexacopters.
Bhuiyan, Tanveer Hossain, et al. "Aerial drone fleet deployment optimization with endogenous battery replacements for direct delivery of time-sensitive products." Expert Systems with Applications, vol. 252, no. Part B, May. 2024. https://doi.org/10.1016/j.eswa.2024.124172
Bhuiyan, Tanveer Hossain, Walker, Victor G., Roni, Mohammad Sadekuzzaman, & Ahmed, Imtiaz (2024). Aerial drone fleet deployment optimization with endogenous battery replacements for direct delivery of time-sensitive products. Expert Systems with Applications, 252(Part B). https://doi.org/10.1016/j.eswa.2024.124172
Bhuiyan, Tanveer Hossain, Walker, Victor G., Roni, Mohammad Sadekuzzaman, et al., "Aerial drone fleet deployment optimization with endogenous battery replacements for direct delivery of time-sensitive products," Expert Systems with Applications 252, no. Part B (2024), https://doi.org/10.1016/j.eswa.2024.124172
@article{osti_2583936,
author = {Bhuiyan, Tanveer Hossain and Walker, Victor G. and Roni, Mohammad Sadekuzzaman and Ahmed, Imtiaz},
title = {Aerial drone fleet deployment optimization with endogenous battery replacements for direct delivery of time-sensitive products},
annote = {Aerial drones offer a distinct potential to reduce the delivery time and energy consumption for the delivery of time-sensitive and small products. However, there is still a need in the relevant industry to understand the performance of drone-based delivery under different business needs and drone operating conditions. We studied a drone deployment optimization problem for direct delivery of time-sensitive products with release dates to customers maintaining a specified time window. This paper presents a new mixed-integer programming model, new valid inequalities, a new greedy heuristic algorithm, and a Genetic algorithm to help business owners optimally schedule and route their drone fleet minimizing the required fleet size, the required number of additional batteries, and total energy consumption. A realistic feature of the optimization method is that instead of replacing the drone battery after each return to the depot, it keeps track of the remaining energy in the drone battery and decides on battery replacements accounting for the drone routing and the user-specified minimum required battery energy. Numerical results based on real data from drone flight tests and prepared food delivery industry provide insights into the effect of different practical drone operating parameters on the required fleet size, the required number of battery replacements, and energy consumption. Here, results demonstrate that the proposed heuristic algorithm substantially outperforms the accelerated CPLEX in runtime while sacrificing the solution quality by a small amount. Additionally, results show that using a mixed fleet of hexacopter and quadcopter drones reduces the total energy consumption by 48.52% compared to using a homogeneous fleet of only hexacopters.},
doi = {10.1016/j.eswa.2024.124172},
url = {https://www.osti.gov/biblio/2583936},
journal = {Expert Systems with Applications},
issn = {ISSN 0957-4174},
number = {Part B},
volume = {252},
place = {United States},
publisher = {Elsevier},
year = {2024},
month = {05}}
Idaho National Laboratory (INL), Idaho Falls, ID (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Office of Sustainable Transportation. Vehicle Technologies Office (VTO); USDOE Office of Nuclear Energy (NE)
Grant/Contract Number:
AC07-05ID14517
OSTI ID:
2583936
Alternate ID(s):
OSTI ID: 2367207
Report Number(s):
INL/JOU--22-67807-Rev000
Journal Information:
Expert Systems with Applications, Journal Name: Expert Systems with Applications Journal Issue: Part B Vol. 252; ISSN 0957-4174