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Deployment of Charging Stations for Drone Delivery Assisted by Public Transportation Vehicles

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Bibliographic Details
Title: Deployment of Charging Stations for Drone Delivery Assisted by Public Transportation Vehicles
Authors: Hailong Huang, Andrey V. Savkin
Contributors: Department of Aeronautical and Aviation Engineering
Source: IEEE Transactions on Intelligent Transportation Systems. 23:15043-15054
Publisher Information: Institute of Electrical and Electronics Engineers (IEEE), 2022.
Publication Year: 2022
Subject Terms: anzsrc-for: 1507 Transportation and Freight Services, anzsrc-for: 35 Commerce, 05 social sciences, 3509 Transportation, anzsrc-for: 0905 Civil Engineering, anzsrc-for: 4603 Computer vision and multimedia computation, 35 Commerce, 02 engineering and technology, Parcel delivery, anzsrc-for: 3509 Transportation, Management, Charging stations, 0502 economics and business, 11. Sustainability, Unmanned aerial vehicle (UAV), Public transportation vehicles, anzsrc-for: 4602 Artificial intelligence, 0202 electrical engineering, electronic engineering, information engineering, Logistics and Supply Chains, 7 Affordable and Clean Energy, anzsrc-for: 0801 Artificial Intelligence and Image Processing, Tourism and Services, Drones
Description: To enable the drone delivery service in a remote area, this paper considers the approach of deploying charging stations and collaborating with public transportation vehicles. From the warehouse which is far from a customer, a drone takes some public transportation vehicles to reach some position close to the remote area. When the customer is unreachable from the position where the drone leaves the public transportation vehicle, the drone swaps the battery at a charging station. The focus of this paper is the deployment of charging stations. We propose a new model to characterize the delivery time for customers. We formulate the optimal deployment problem to minimize the average delivery time for the customers, which is a reflection of customer satisfaction. We then propose a sub-optimal algorithm that relocates the charging stations in sequence, which ensures that any movement of a charging station leads to a decrease in the average flight distance. The comparison with a baseline method confirms that the proposed model can more accurately estimate the flight distance of a customer than the commonly used model, and the proposed algorithm can relocate the charging stations achieving lower flight distance.
Document Type: Article
File Description: application/pdf
ISSN: 1558-0016
1524-9050
DOI: 10.1109/tits.2021.3136218
Rights: IEEE Copyright
CC BY NC ND
Accession Number: edsair.doi.dedup.....352f45e30cb76c9496741b15052bec48
Database: OpenAIRE
Description
Abstract:To enable the drone delivery service in a remote area, this paper considers the approach of deploying charging stations and collaborating with public transportation vehicles. From the warehouse which is far from a customer, a drone takes some public transportation vehicles to reach some position close to the remote area. When the customer is unreachable from the position where the drone leaves the public transportation vehicle, the drone swaps the battery at a charging station. The focus of this paper is the deployment of charging stations. We propose a new model to characterize the delivery time for customers. We formulate the optimal deployment problem to minimize the average delivery time for the customers, which is a reflection of customer satisfaction. We then propose a sub-optimal algorithm that relocates the charging stations in sequence, which ensures that any movement of a charging station leads to a decrease in the average flight distance. The comparison with a baseline method confirms that the proposed model can more accurately estimate the flight distance of a customer than the commonly used model, and the proposed algorithm can relocate the charging stations achieving lower flight distance.
ISSN:15580016
15249050
DOI:10.1109/tits.2021.3136218