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Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
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An Optimal Routing Algorithm for Unmanned Aerial Vehicles.

Sooyeon Kim1, Jae Hyun Kwak2, Byoungryul Oh1

  • 1Department of Electric and Electrical Engineering, Konkuk University, Seoul 05029, Korea.

Sensors (Basel, Switzerland)
|February 12, 2021
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Summary
This summary is machine-generated.

This study introduces a new algorithm for optimizing delivery routes for unmanned aerial vehicles (UAVs). The algorithm efficiently manages UAVs to minimize delivery distances and costs for a drone delivery network.

Keywords:
mixed integer linear programmingmultiple depots vehicle routing problemsubtour elimination, network optimizationunmanned aerial vehicle

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Area of Science:

  • Logistics and Supply Chain Management
  • Operations Research
  • Robotics and Automation

Background:

  • Unmanned aerial vehicles (UAVs) offer a promising solution for future delivery services due to their speed, safety, and environmental benefits.
  • Effective management of UAV delivery networks requires sophisticated route optimization systems.
  • Current delivery models face challenges in efficiency and scalability that UAVs can address.

Purpose of the Study:

  • To develop a novel routing algorithm for optimizing round-trip delivery routes for UAVs.
  • To minimize delivery distances and operational costs within a UAV delivery network.
  • To determine the optimal number of UAVs required per depot and their allocation to customers.

Main Methods:

  • A four-step algorithm was developed, starting with building a virtual network model of the operational environment.
  • The algorithm determines the optimal number of UAVs per depot and eliminates infeasible routes (subtours) using flow variables.
  • UAVs are allocated to customers to minimize delivery distances, allowing simultaneous deliveries to a single customer.

Main Results:

  • The algorithm successfully determines the optimal number of UAVs needed for service per depot.
  • It calculates efficient round-trip routes for UAVs, considering vehicle range and payload capacity.
  • The system allocates UAVs to customers, minimizing overall delivery costs and enabling parallel deliveries.

Conclusions:

  • The proposed algorithm provides an effective solution for managing UAV delivery networks.
  • It optimizes resource allocation and routing for cost-efficient and scalable drone-based logistics.
  • This research contributes to the advancement of autonomous delivery systems and logistics management.