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Title:

Optimization of Multi-package Drone Deliveries Considering Battery Capacity
Cover of Optimization of Multi-package Drone Deliveries Considering Battery Capacity

Accession Number:

01630325

Record Type:

Component

Abstract:

Drone delivery has been tested by private companies around world. With advanced safety and reliability features, such as automated flight and sense-and-avoid technology to prevent collisions, it appears that deliveries by drone will soon be practical. This paper deals with an automated drone delivery system, rather than cooperative delivery by drones and trucks. The study assumes that a drone can lift multiple packages within its maximum payload and serve recipients in a service area of given radius. Battery capacities, the primary energy sources for drone operation, are analyzed to relate parcel payloads and flight ranges. Numerical analysis is used to optimize the drone fleet size for a service area, by minimizing the total costs of the delivery system. Four variables are explored to show the sensitivity of system outputs to input parameters: working period, drone operating speed, demand density of service area, and battery capacity. Extended daily working periods are shown to benefit both service providers and users. Increased drone operating speed reduces overall costs, but increases operating and investment costs for suppliers. The study indicates that drone deliveries are more economical in areas with high demand densities. Lastly, the large amount of energy storage resulted from battery improvement can reduce the number of drones satisfying all demands in a service area.

Supplemental Notes:

This paper was sponsored by TRB committee AV060 Standing Committee on Airfield and Airspace Capacity and Delay. Alternate title: Optimization of Multipackage Drone Deliveries Considering Battery Capacity

Monograph Accession #:

01618707

Report/Paper Numbers:

17-05769

Language:

English

Corporate Authors:

Transportation Research Board

500 Fifth Street, NW
Washington, DC 20001 United States

Authors:

Choi, Youngmin
Schonfeld, Paul M

Pagination:

16p

Publication Date:

2017

Conference:

Transportation Research Board 96th Annual Meeting

Location: Washington DC, United States
Date: 2017-1-8 to 2017-1-12
Sponsors: Transportation Research Board

Media Type:

Digital/other

Features:

Figures; References; Tables

Uncontrolled Terms:

Battery energy capacities; Payload

Subject Areas:

Aviation; Energy; Freight Transportation; Operations and Traffic Management; Planning and Forecasting

Source Data:

Transportation Research Board Annual Meeting 2017 Paper #17-05769

Files:

PRP, TRIS, TRB, ATRI

Created Date:

Dec 8 2016 12:19PM