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Advances of Drones in Logistics

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A special issue of Drones (ISSN 2504-446X). This special issue belongs to the section "Innovative Urban Mobility".

Deadline for manuscript submissions: closed (13 April 2024) | Viewed by 14228

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Special Issue Editors

Prof. Dr. Tom Cherrett

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Guest Editor

Faculty of Engineering and Physical Sciences, The University of Southampton, Southampton, UK
Interests: urban logistics systems; last mile logistics; waste logistics; drone logistics

Dr. Paul Royall

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Guest Editor

Institute of Pharmaceutical Science, Department of Pharmacy, Faculty of Life Sciences & Medicine, King’s College London, 150 Stamford Street, London SE1 9NH, UK
Interests: delivery of medicines by drone; the maintenance of quality of medicines and medical products by drone; exploration of the relationship between solid state properties and the performance of pharmaceutical materials and medicines
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Dr. Andy Oakey

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Guest Editor

Faculty of Engineering and Physical Sciences, The University of Southampton, Southampton, UK
Interests: logistics system modelling; integrated logistics systems; logisics system optimisation; drone logistics

Special Issue Information

Dear Colleagues,

Time efficiency is a key factor in logistics, and the use of uncrewed aerial vehicles (drones) are being increasingly seen as potential tools to improve operations by reducing the time required to complete certain tasks. Drones are being used to improve the management of inventory in warehouses, whilst others are being trialled for fast point-to-point goods deliveries in areas where the traditional land journey is more challenging. With increasing levels of automation going forward, delivery drones may be able to reduce labour and transportation costs for certain types of movement in specific situations.

There are also challenges in the future development and adoption of drones used in logistics, including:

(i) the cost of the technology and staffing,

(ii) safety and regulatory requirements,

(iii) performance and reliability standards,

(iv) the ways in which drones can be effectively integrated into existing land-based logistics systems

(v) how to effectively optimise drone logistics alongside traditional freight modes (drone routing and scheduling)

(vi) public perception and acceptance,

(vii) understanding the realistic demand for such services

This Special Issue seeks high-quality and innovative scientific papers that address these challenges through practical application and theoretical case studies.

Prof. Dr. Tom Cherrett
Dr. Paul Royall
Dr. Andy Oakey
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Drones is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

drone-based warehouse logistics
integrated logistics incorporating drones
optimisation of drone-based logistics solutions
drone technology in logistics
drone logistics costs and emissions
security and surveillance in drone-based logistics
public perception and acceptance of drone logistics

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Published Papers (7 papers)

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Research

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20 pages, 1709 KiB

Open AccessFeature PaperArticle

Fed4UL: A Cloud–Edge–End Collaborative Federated Learning Framework for Addressing the Non-IID Data Issue in UAV Logistics

by Chong Zhang, Xiao Liu, Aiting Yao, Jun Bai, Chengzu Dong, Shantanu Pal and Frank Jiang

Drones 2024, 8(7), 312; https://doi.org/10.3390/drones8070312 - 10 Jul 2024

Viewed by 1462

Abstract

Artificial intelligence and the Internet of Things (IoT) have brought great convenience to people’s everyday lives. With the emergence of edge computing, IoT devices such as unmanned aerial vehicles (UAVs) can process data instantly at the point of generation, which significantly decreases the requirement for on-board processing power and minimises the data transfer time to enable real-time applications. Meanwhile, with federated learning (FL), UAVs can enhance their intelligent decision-making capabilities by learning from other UAVs without directly accessing their data. This facilitates rapid model iteration and improvement while safeguarding data privacy. However, in many UAV applications such as UAV logistics, different UAVs may perform different tasks and cover different areas, which can result in heterogeneous data and add to the problem of non-independent and identically distributed (Non-IID) data for model training. To address such a problem, we introduce a novel cloud–edge–end collaborative FL framework, which organises and combines local clients through clustering and aggregation. By employing the cosine similarity, we identified and integrated the most appropriate local model into the global model, which can effectively address the issue of Non-IID data in UAV logistics. The experimental results showed that our approach outperformed traditional FL algorithms on two real-world datasets, CIFAR-10 and MNIST. Full article

(This article belongs to the Special Issue Advances of Drones in Logistics)

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28 pages, 2789 KiB

Open AccessArticle

Drone-Based Instant Delivery Hub-and-Spoke Network Optimization

by Zhi-Hua Hu, Yan-Ling Huang, Yao-Na Li and Xiao-Qiong Bao

Drones 2024, 8(6), 247; https://doi.org/10.3390/drones8060247 - 5 Jun 2024

Cited by 1 | Viewed by 1916

Abstract

Drone-based transportation is emerging as a novel mode in city logistics, featuring first-mile pickup and last-mile instant delivery using drones and truck transshipment. A fundamental challenge involves coordinating merchants, drones, transshipment hubs, trucks, and consumer communities through the hub-and-spoke network (HSN). This study formulated the optimization problem for HSN to minimize logistics costs and loss of orders constrained by service time limits. The ε-constraint model, two evolutionary algorithms based on Non-dominated Sorting Genetic Algorithm II (NSGA-II) using permutation (EAp) and rand key-based (EAr) encoding/decoding schemes were devised to solve the bi-objective mathematical program. Three groups of twelve experiments were conducted using ideal datasets and datasets generated from Shenzhen city to validate the models and algorithms. Relaxing the logistics objective by 10% and subsequently minimizing the loss of orders can significantly reduce average unmet orders by 24.61%; when spokes were beyond 20, the ε-constraint model failed to achieve solutions within an acceptable time. While EAp and EAr demonstrated competence, EAr proved to be more competitive in computation time, hypervolume, spacing metric, and the number of non-dominated solutions on the Pareto fronts. Key parameters influencing the HSN solutions include drone and truck speeds, acceptable delivery times, and the processing and waiting time at hubs. Full article

(This article belongs to the Special Issue Advances of Drones in Logistics)

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14 pages, 754 KiB

Open AccessArticle

The Development of an Optimal Operation Algorithm for Food Delivery Using Drones Considering Time Interval between Deliveries

by Young Kwan Ko, Hyeseon Han, Yonghui Oh and Young Dae Ko

Drones 2024, 8(6), 230; https://doi.org/10.3390/drones8060230 - 30 May 2024

Cited by 1 | Viewed by 967

Abstract

These days, many attempts are being made worldwide to use drones for food delivery. Especially in the case of food, fast delivery is required, while maintaining its temperature and taste to the maximum. Therefore, using drones is suitable for food delivery because they can move through the air without being affected by traffic congestion. In this study, the purpose is to develop an optimal algorithm that can complete the delivery of customer food orders in the shortest time using drones. We have applied mathematical-model-based optimization techniques to develop an algorithm that reflects the given problem situation. Since the delivery capacity of drones is limited, and especially small, multiple drones may be used to deliver the food ordered by a particular customer. What is important here is that the drones assigned to one customer must arrive consecutively within a short period of time. This fact is reflected in this mathematical model. In the numerical example, it can be confirmed that the proposed algorithm operates optimally by comparing a case where the arrival time of multiple drones assigned to one customer is limited to a certain time and a case when it is not. Full article

(This article belongs to the Special Issue Advances of Drones in Logistics)

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23 pages, 2415 KiB

Open AccessArticle

Hybrid Encryption for Securing and Tracking Goods Delivery by Multipurpose Unmanned Aerial Vehicles in Rural Areas Using Cipher Block Chaining and Physical Layer Security

by Elias Yaacoub, Khalid Abualsaud and Mohamed Mahmoud

Drones 2024, 8(3), 111; https://doi.org/10.3390/drones8030111 - 21 Mar 2024

Cited by 1 | Viewed by 1780

Abstract

This paper investigated the use of unmanned aerial vehicles (UAVs) for the delivery of critical goods to remote areas in the absence of network connectivity. Under such conditions, it is important to track the delivery process and record the transactions in a delay-tolerant fashion so that this information can be recovered after the UAV’s return to base. We propose a novel framework that combines the strengths of cipher block chaining, physical layer security, and symmetric and asymmetric encryption techniques in order to safely encrypt the transaction logs of remote delivery operations. The proposed approach is shown to provide high security levels, making the keys undetectable, in addition to being robust to attacks. Thus, it is very useful in drone systems used for logistics and autonomous goods delivery to multiple destinations. This is particularly important in health applications, e.g., for vaccine transmissions, or in relief and rescue operations. Full article

(This article belongs to the Special Issue Advances of Drones in Logistics)

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22 pages, 6075 KiB

Open AccessArticle

Impact of Drone Battery Recharging Policy on Overall Carbon Emissions: The Traveling Salesman Problem with Drone

by Emine Es Yurek

Drones 2024, 8(3), 108; https://doi.org/10.3390/drones8030108 - 20 Mar 2024

Cited by 3 | Viewed by 2052

Abstract

This study investigates the traveling salesman problem with drone (TSP-D) from a sustainability perspective. In this problem, a truck and a drone simultaneously serve customers. Due to the limited battery and load capacity, the drone temporarily launches from and returns to the truck after each customer visit. Previous studies indicate the potential of deploying drones to reduce delivery time and carbon emissions. However, they assume that the drone battery is swapped after each flight. In this study, we analyze the carbon emissions of the TSP-D under the recharging policy and provide a comparative analysis with the swapping policy. In the recharging policy, the drone is recharged simultaneously on top of the truck while the truck travels. A simulated annealing algorithm is proposed to solve this problem. The computational results demonstrate that the recharging policy can provide faster delivery and lower emissions than the swapping policy if the recharging is fast enough. Full article

(This article belongs to the Special Issue Advances of Drones in Logistics)

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29 pages, 10427 KiB

Open AccessFeature PaperArticle

Paving the Way for Last-Mile Delivery in Greece: Data-Driven Performance Analysis with a Customized Quadrotor

by Charalabos Ioannidis, Argyro-Maria Boutsi, Georgios Tsingenopoulos, Sofia Soile, Regina Chliverou and Chryssy Potsiou

Drones 2024, 8(1), 6; https://doi.org/10.3390/drones8010006 - 29 Dec 2023

Cited by 2 | Viewed by 3136

Abstract

Cargo drones are a cutting-edge solution that is becoming increasingly popular as flight times extend and regulatory frameworks evolve to accommodate new delivery methods. The aim of this paper was to comprehensively understand cargo drone dynamics and guide their effective deployment in Greece. A 5 kg payload quadrotor with versatile loading mechanisms, including a cable-suspended system and an ultra-light box, was manufactured and tested in five Greek cities. A comprehensive performance evaluation and analysis of flight range, energy consumption, altitude-related data accuracy, cost-effectiveness, and environmental were conducted. Based on hands-on experimentation and real-world data collection, the study proposes a novel data-driven methodology for strategically locating charging stations and addressing uncertainties like weather conditions and battery discharge during flights. Results indicate significant operational cost savings (89.44%) and a maximum emissions reduction (77.42%) compared to conventional transportation. The proposed strategic placement of charging stations led to substantial reductions in travel distance (41.03%) and energy consumption (56.73%) across five case studies in Greek cities. Full article

(This article belongs to the Special Issue Advances of Drones in Logistics)

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Review

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34 pages, 3899 KiB

Open AccessReview

Drone-Assisted Multimodal Logistics: Trends and Research Issues

by Kyunga Kim, Songi Kim, Junsu Kim and Hosang Jung

Drones 2024, 8(9), 468; https://doi.org/10.3390/drones8090468 - 8 Sep 2024

Viewed by 1930

Abstract

This study explores the evolving trends and research issues in the field of drone-assisted multimodal logistics over the past two decades. By employing various text-mining techniques on related research publications, we identify the most frequently investigated topics and research issues within this domain. Specifically, we utilize titles, abstracts, and keywords from the collected studies to perform both Latent Dirichlet Allocation techniques and Term Frequency-Inverse Document Frequency analysis, which help in identifying latent topics and the core research themes within the field. Our analysis focuses on three primary categories of drone-assisted logistics: drone–truck, drone–ship, and drone–robot systems. The study aims to uncover which latent topics have been predominantly emphasized in each category and to highlight the distinct differences in research focuses among them. Our findings reveal specific trends and gaps in the existing literature, providing a clear roadmap for future research directions in drone-assisted multimodal logistics. This targeted analysis not only enhances our understanding of the current state of the field but also identifies critical areas that require further investigation to advance the application of drones in logistics. Full article

(This article belongs to the Special Issue Advances of Drones in Logistics)

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