Impact of UAV Delivery on Sustainability and Costs under Traffic Restrictions

Compared with traditional vehicles delivery, unmanned aerial vehicle (UAV) delivery can reduce energy consumption and greenhouse gas emissions, which benefits environmental sustainability. Besides, UAVs can overcome traffic restrictions, which are the big obstacle in parcel delivery. In reality, there are two kinds of most popular traffic restrictions, vehicle-type restriction, and half-side traffic. We propose a mixed-integer (0-1 linear) green routing model with these two kinds of traffic restrictions for UAVs to exploit the environmental aspects of the use of UAVs in logistics. A genetic algorithm is proposed to efficiently solve the complex routing problem, and an experimental analysis is made to illustrate and validate our model and the algorithm. We found that, under both these two traffic restrictions, UAV delivery can accomplish deliveries that cannot be carried out or are carried out at much higher costs by vehicles only and can always effectively save costs and cut CO2 emissions, which is environmentally friendly. Furthermore, UAV delivery saves more cost and cuts more CO2 emission under the first kind of traffic restriction than that under the second.

Download Full-text

An Evolutionary Trajectory Planning Algorithm for Multi-UAV-Assisted MEC System

10.21203/rs.3.rs-794751/v1 ◽

2021 ◽

Author(s):

Muhammad Asim ◽

Wali Khan ◽

Samir Brahim Belhaouari

Keyword(s):

Energy Consumption ◽

Unmanned Aerial Vehicle ◽

Trajectory Planning ◽

Mixed Integer ◽

Phase Variable ◽

Mobile Edge Computing ◽

Evolutionary Trajectory ◽

Multiple Uavs ◽

Planning Algorithm ◽

Aerial Vehicle

Abstract This paper presents a multi-unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system, where multiple UAVs are used to serve mobile users (MUs). We aim to minimize the overall energy consumption of the system by planning the trajectories of UAVs. To plan the trajectories of UAVs, we need to consider the deployment of hovering points (HPs) of UAVs, their association with UAVs, and their order for each UAV. Therefore, the problem is very complicated, as it is non-convex, nonlinear, NP-hard, and mixed-integer. To solve the problem, this paper proposed an evolutionary trajectory planning algorithm (ETPA), which comprises three phases. In the first phase, variable-length GA is adopted to update the deployments of HPs for UAVs. Accordingly, redundant HPs are removed by the remove operator. Subsequently, differential evolution clustering is adopted to cluster HPs into different clusters without knowing the number of HPs in advance. Finally, a GA is proposed to construct the order of HPs for UAVs. The experimental results on a set of eight instances show that the proposed ETPA outperforms other compared algorithms in terms of the energy consumption of the system.This paper presents a multi-unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system, where multiple UAVs are used to serve mobile users (MUs). We aim to minimize the overall energy consumption of the system by planning the trajectories of UAVs. To plan the trajectories of UAVs, we need to consider the deployment of hovering points (HPs) of UAVs, their association with UAVs, and their order for each UAV. Therefore, the problem is very complicated, as it is non-convex, nonlinear, NP-hard, and mixed-integer. To solve the problem, this paper proposed an evolutionary trajectory planning algorithm (ETPA), which comprises three phases. In the first phase, variable-length GA is adopted to update the deployments of HPs for UAVs. Accordingly, redundant HPs are removed by the remove operator. Subsequently, differential evolution clustering is adopted to cluster HPs into different clusters without knowing the number of HPs in advance. Finally, a GA is proposed to construct the order of HPs for UAVs. The experimental results on a set of eight instances show that the proposed ETPA outperforms other compared algorithms in terms of the energy consumption of the system.

Download Full-text

Cooperated Routing Problem for Ground Vehicle and Unmanned Aerial Vehicle: The Application on Intelligence, Surveillance and Reconnaissance Missions

10.20944/preprints201902.0110.v1 ◽

2019 ◽

Author(s):

Yao Liu ◽

Zhihao Luo ◽

Zhong Liu ◽

Jianmai Shi ◽

Guangquan Cheng

Keyword(s):

Unmanned Aerial Vehicle ◽

Base Station ◽

Mixed Integer ◽

Routing Problem ◽

Ground Vehicle ◽

Mip Model ◽

Aerial Vehicle ◽

Set Up ◽

Operational Constraints ◽

Mobile Base

In this paper, we present a novel Two-Echelon Ground Vehicle and Its Mounted Unmanned Aerial Vehicle Cooperated Routing Problem (2E-GUCRP). The 2E-GUCRP arises in the field of Unmanned Aerial Vehicle (UAV) Routing Problem such as those encountered in the context of city logistics. In a typical cooperated system, the UAV is launched from the Ground Vehicle (GV) and automatically flies to the designated target. Meanwhile, acting as a mobile base station, the GV can charge or change the UAV’s battery on the designated landing points to enable the UAV to continue its mission. The objective is to design efficient GV and UAV routes to minimize the total mission time while meeting the operational constraints. A Mixed Integer Programming (MIP) model, which could be solved by commercial software, is constructed to describe this problem. In order to quickly solve the medium-scale problems, two existing heuristics to solve 2E-VRP are improved. The computational experiments are set up to compare our model with the 2E-VRP. The results indicate that the 2E-GUCRP obtains a better efficiency. Further discussion of the practical instance points out that the increase in efficiency is related to the speed relationship between the GV and the UAV.

Download Full-text