TY - JOUR
T1 - Drone Routing in a Time-Dependent Network: Towards Low Cost and Large Range Parcel Delivery
T2 - Toward Low-Cost and Large-Range Parcel Delivery
AU - Huang, Hailong
AU - Savkin, Andrey V.
AU - Huang, Chao
N1 - Funding Information:
Manuscript received May 22, 2020; revised July 7, 2020; accepted July 23, 2020. Date of publication July 28, 2020; date of current version November 18, 2020. This work was supported by the Australian Research Council. Paper no. TII-20-2583. (Corresponding author: Hailong Huang.) Hailong Huang and Andrey V. Savkin are with the School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia (e-mail: [email protected]; [email protected]).
Publisher Copyright:
© 2005-2012 IEEE.
PY - 2021/2
Y1 - 2021/2
N2 - Drones are a promising tool for parcel delivery, since they are cost-efficient and environmentally friendly. However, owing to the limited capacity of the on-board battery, their flight range is constrained. Thus, they cannot deliver some parcels if the customers are too far from the depot. To address this issue, this article proposes a novel method, in which a parcel delivery drone can 'take' a public transportation vehicle and travel on its roof. The problem under consideration is how to make use of the public transportation network to route the drone between the depot and the customer. Compared to the currently available methods that use drones, the most important merit of this approach is a significant expansion of the delivery area. We construct a multimodal network consisting of public transportation vehicles' trips and drone flights. Because of the complexity of this multimodal network, we convert it to a simple network with a set of simple procedures. In the extended network, we formulate the shortest drone path problem that minimizes the return instant to the depot, subject to that the drone energy consumption on this path is no greater than the initial energy. We present a Dijkstra-based method to find the shortest drone path. Moreover, we extend the proposed method to the case with uncertainty, because the public transportation vehicles cannot exactly follow their timetables in practice. Simulation results are presented to demonstrate how the method works.
AB - Drones are a promising tool for parcel delivery, since they are cost-efficient and environmentally friendly. However, owing to the limited capacity of the on-board battery, their flight range is constrained. Thus, they cannot deliver some parcels if the customers are too far from the depot. To address this issue, this article proposes a novel method, in which a parcel delivery drone can 'take' a public transportation vehicle and travel on its roof. The problem under consideration is how to make use of the public transportation network to route the drone between the depot and the customer. Compared to the currently available methods that use drones, the most important merit of this approach is a significant expansion of the delivery area. We construct a multimodal network consisting of public transportation vehicles' trips and drone flights. Because of the complexity of this multimodal network, we convert it to a simple network with a set of simple procedures. In the extended network, we formulate the shortest drone path problem that minimizes the return instant to the depot, subject to that the drone energy consumption on this path is no greater than the initial energy. We present a Dijkstra-based method to find the shortest drone path. Moreover, we extend the proposed method to the case with uncertainty, because the public transportation vehicles cannot exactly follow their timetables in practice. Simulation results are presented to demonstrate how the method works.
KW - Control of parcel delivery systems
KW - drones
KW - parcel delivery
KW - public transportation network
KW - unmanned aerial vehicles
UR - http://www.scopus.com/inward/record.url?scp=85096687101&partnerID=8YFLogxK
U2 - 10.1109/TII.2020.3012162
DO - 10.1109/TII.2020.3012162
M3 - Journal article
SN - 1551-3203
VL - 17
SP - 1526
EP - 1534
JO - IEEE Transactions on Industrial Informatics
JF - IEEE Transactions on Industrial Informatics
IS - 2
M1 - 9151388
ER -