Title

Greedy Algorithms for Scheduling Package Delivery with Multiple Drones

Abstract

Unmanned Aerial Vehicles (or drones) can be used for a myriad of civil applications, such as search and rescue, precision agriculture, or last-mile package delivery. Interestingly, the cooperation between drones and ground vehicles (trucks) can even enhance the quality of service. In this paper, we investigate the symbiosis among a truck and multiple drones in a last-mile package delivery scenario, introducing the Multiple Drone-Delivery Scheduling Problem (MDSP). From the main depot, a truck takes care of transporting a team of drones that will be used to deliver packages to customers. Each delivery is associated with a drone's energy cost, a reward that characterizes the priority of the delivery, and a time interval representing the launch and rendezvous times from and to the truck. The objective of MDSP is to find an optimal scheduling for the drones that maximizes the overall reward subject to the drone's battery capacity while ensuring that the same drone performs deliveries whose time intervals do not intersect. After showing that MDSP is an NP-hard problem, we devise an optimal Integer Linear Programming (ILP) formulation for it. Consequently, we design a heuristic algorithm for the single drone case and two more heuristic algorithms for the multiple drone case. Finally, we thoroughly compare the performance of our presented algorithms on different synthetic datasets.

Meeting Name

23rd International Conference on Distributed Computing and Networking, ICDCN 2022 (2022: Jan. 4-7, Delhi, India)

Department(s)

Computer Science

Comments

This work was partially supported by NSF grants CNS-1818942, OAC-1725755, OAC-2104078, and SCC-1952045; and also partially supported by "HALY-ID" project funded by the European Union’s Horizon 2020 under grant agreement ICT-AGRI-FOOD no. 862665, no. 862671, and from MIPAAF .

Keywords and Phrases

Drone; Last-Mile Delivery System; Truck

International Standard Book Number (ISBN)

978-145039560-1

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2022 Association for Computing Machinery (ACM), All rights reserved.

Publication Date

07 Jan 2022

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