[ { "id": "https://openalex.org/W4407275006", "doi": "10.1016/j.trc.2025.105026", "title": "The multi-visit vehicle routing problem with multiple heterogeneous drones", "year": 2025, "publication_date": "2025-02-08", "type": "article", "language": "en", "authors": [ "Yu Jiang", "Mengmeng Liu", "Xibei Jia", "Qingwen Xue" ], "first_author": "Yu Jiang", "venue": "Transportation Research Part C Emerging Technologies", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Vehicle routing problem", "Routing (electronic design automation)", "Computer science", "Transport engineering", "Engineering", "Aeronautics", "Operations research", "Computer network", "Biology" ], "cited_by_count": 28, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.trc.2025.105026" }, { "id": "https://openalex.org/W4211090622", "doi": "10.1080/19427867.2022.2035130", "title": "Applying the integrated model to understanding online buyers’ intention to adopt delivery drones in Iran", "year": 2022, "publication_date": "2022-02-11", "type": "article", "language": "en", "authors": [ "Houmaan Ganjipour", "Ali Edrisi" ], "first_author": "Houmaan Ganjipour", "venue": "Transportation Letters", "publisher": "Taylor & Francis", "abstract": "This study is aimed at determining the factors influential in the acceptance of delivery drones as a new way for last-mile delivery in the future. In this regard, the study model was proposed using the UTAUT2 as the base model and adding the factors of psychological characteristics, perceived risk, and need for human interaction to it. The information about 357 Iranian buyers was collected for the PLS-SEM by designing an online questionnaire. The results indicated that all variables of UTAUT2, except for performance expectancy, had a positive and significant effect on the intention of buyers to use delivery drones. Moreover, the personal norm positively affected the users’ intention, while the need for human interaction and perceived risk had adverse effects on it. The research findings indicated that innovativeness had a significant positive effect on effort expectancy and compatibility while having an insignificant effect on performance expectancy.", "keywords": [ "Expectancy theory", "Unified theory of acceptance and use of technology", "Psychology", "Social influence", "Social psychology", "Structural equation modeling", "Risk perception", "Business", "Applied psychology", "Marketing", "Computer science" ], "cited_by_count": 38, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1080/19427867.2022.2035130" }, { "id": "https://openalex.org/W4383163150", "doi": "10.1016/j.trc.2023.104203", "title": "An adaptive large neighborhood search metaheuristic for a passenger and parcel share-a-ride problem with drones", "year": 2023, "publication_date": "2023-07-04", "type": "article", "language": "en", "authors": [ "Rong Cheng", "Yu Jiang", "Otto Anker Nielsen", "David Pisinger" ], "first_author": "Rong Cheng", "venue": "Transportation Research Part C Emerging Technologies", "publisher": "Elsevier BV", "abstract": "With the increasing concerns about traffic congestion and climate change, much effort has been made to enhance sustainable urban mobility for passengers and goods. One emerging promising strategy is to transport passengers and goods in an integrated manner, as it could reduce the number of vehicles on the road compared with the separate transportation of passengers and goods. This study proposes the simultaneous transportation of passengers and goods using demand-responsive buses and drones. Compared with the prevalent strategies that rely only on ground vehicles to integrate passenger and parcel transportation, we propose the joint usage of ground vehicles and drones to transport passengers and deliver parcels. The ground vehicles for passenger and parcel delivery are on-demand buses, which combine the advantages of the flexibility of taxis and the large capacity of public transport modes. The drones automatically take off from and land on the on-demand buses’ rooftops and are only for parcel delivery. A new optimization problem that designs the routes for both demand-responsive buses and drones is proposed and denoted as the passenger and parcel share-a-ride problem with drones (SARP-D). A mixed-integer nonlinear programming model is devised; the nonlinearity exists because drone launch/recovery can occur simultaneously with request servicing by a bus at the same node. To solve the model for large-scale instances, we develop an adaptive large neighborhood search metaheuristic. Numerical experiments are conducted to validate the correctness of the model and evaluate the efficiency of the metaheuristic. Moreover, sensitivity analyses are performed to explore the influences of the maximum number of intermediate stops during one passenger request service, the drone flight endurance, and the unit delay penalty on the total cost, which comprises the transportation and delay costs.", "keywords": [ "Drone", "Metaheuristic", "Transport engineering", "Computer science", "Engineering", "Operations research", "Aeronautics", "Artificial intelligence" ], "cited_by_count": 42, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.trc.2023.104203" }, { "id": "https://openalex.org/W4412553343", "doi": "10.3390/drones9080513", "title": "Optimizing Energy Efficiency in Last-Mile Delivery: A Collaborative Approach with Public Transportation System and Drones", "year": 2025, "publication_date": "2025-07-22", "type": "article", "language": "en", "authors": [ "Pierre Romet", "Charbel Hage", "El‐Hassane Aglzim", "Tonino Sophy", "Franck Gechter" ], "first_author": "Pierre Romet", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Accurately estimating the energy consumption of unmanned aerial vehicles (UAVs) in real-world delivery scenarios remains a critical challenge, particularly when UAVs operate in complex urban environments and are coupled with public transportation systems. Most existing models rely on oversimplified assumptions or static mission profiles, limiting their applicability to realistic, scalable drone-based logistics. In this paper, we propose a physically-grounded and scenario-aware energy sizing methodology for UAVs operating as part of a last-mile delivery system integrated with a city’s bus network. The model incorporates detailed physical dynamics—including lift, drag, thrust, and payload variations—and considers real-time mission constraints such as delivery execution windows and infrastructure interactions. To enhance the realism of the energy estimation, we integrate computational fluid dynamics (CFD) simulations that quantify the impact of surrounding structures and moving buses on UAV thrust efficiency. Four mission scenarios of increasing complexity are defined to evaluate the effects of delivery delays, obstacle-induced aerodynamic perturbations, and early return strategies on energy consumption. The methodology is applied to a real-world transport network in Belfort, France, using a graph-based digital twin. Results show that environmental and operational constraints can lead to up to 16% additional energy consumption compared to idealized mission models. The proposed framework provides a robust foundation for UAV battery sizing, mission planning, and sustainable integration of aerial delivery into multimodal urban transport systems.", "keywords": [ "Mile", "Drone", "Last mile (transportation)", "Public transport", "Transport engineering", "Aeronautics", "Computer science", "Engineering", "Business", "Geography" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2504-446X/9/8/513/pdf?version=1753171530" }, { "id": "https://openalex.org/W2951315867", "doi": "10.3390/electronics8060659", "title": "A Novel Data Forwarding Strategy for a Drone Delay Tolerant Network with Range Extension", "year": 2019, "publication_date": "2019-06-11", "type": "article", "language": "en", "authors": [ "Saeid Iranmanesh", "Raad Raad" ], "first_author": "Saeid Iranmanesh", "venue": "Electronics", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Amazon, Uber Eats, and United Parcel Service (UPS) are planning to launch drone delivery services in the near future. Indeed, recently, Google has received Federal Aviation Administration (FAA) approval for its Wings delivery platform. Amazon claims that a drone logistics network is more cost-efficient and quicker than a motor vehicle delivery network. In this paper, we propose a data delivery service by the drone network in addition to parcel delivery. We propose Heuristic Flight Path Planning (HFPP) that plans a drone’s flight path based on parcel delivery destination as well as data delivery destinations (waypoints). We further extend the solution to include drone charging stations for range extension. Our simulation studies show that our proposed method has delivered the data and consignments such that HFPP delivers up to 33% more data packets compared with Encounter-Based Routing (EBR), Epidemic, and a similar path planning method. Also, HFPP reduces the data delivery delays by up to 72% while the overhead ratio is low.", "keywords": [ "Drone", "Computer science", "Network packet", "Overhead (engineering)", "Path (computing)", "Payload (computing)", "Aviation", "Service (business)", "Routing (electronic design automation)", "Computer network", "Engineering", "Business" ], "cited_by_count": 32, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2079-9292/8/6/659/pdf?version=1560243200" }, { "id": "https://openalex.org/W3081555530", "doi": null, "title": "Acceptance of Autonomous Delivery Vehicles for Last Mile Delivery in Germany : Extension of the Technology Acceptance Model to an Autonomous Delivery Vehicles Acceptance Model", "year": 2020, "publication_date": "2020-01-01", "type": "dissertation", "language": "en", "authors": [ "Jessica Hinzmann", "Katharina Bogatzki" ], "first_author": "Jessica Hinzmann", "venue": "Jonkoping University Library (Jönköping University)", "publisher": "Jönköping University", "abstract": "The steady growth of the e-commerce sector and the associated logisticalchallenges in the last mile, as well as the equally increasing expectations ofconsumers for parcel delivery call for innovation in the last mile. Drones androbots seem to be a reasonable alternative delivery option to meet thesechallenges. Before these technologies are used as means of transport in the lastmile, it is necessary to investigate whether it will be accepted by potentialconsumers. This thesis aims to identify the factors influencing conumser’ acceptance ofautonomous delivery vehicles for delivery in Germany. To determine thebehaviour of potential consumers, the Technology Acceptance Model wasextended by several factors from different acceptance models that seemedrelevant from a consumer perspective. In order to investigate consumer acceptance, a quantitative approach wasconducted using questionnaires. The propsed hypotheses were tested usingstructural equation modelling. Further, a multi-group analysis was conducted toindentify sociodemographic differences. The results show that price sensitivity, perceived usefulness, hedonic motivation,and perceived ease of use influence the behavioural intention of consumers inGermany to use autonomous delivery vehicles, whereas privacy security andfacilitating conditions do not have a significant effect. Further no significantdifferences were found in the multigroup analysis.", "keywords": [ "Technology acceptance model", "Last mile (transportation)", "Mile", "Delivery system", "Extension (predicate logic)", "Engineering", "Transport engineering", "Operations management", "Business", "Computer science", "Usability", "Geography" ], "cited_by_count": 14, "is_retracted": false, "is_paratext": false, "open_access_url": "http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-48879" }, { "id": "https://openalex.org/W4380303644", "doi": "10.1109/tits.2023.3281522", "title": "An Enhanced Backtracking Search Algorithm for the Flight Planning of a Multi-Drones-Assisted Commercial Parcel Delivery System", "year": 2023, "publication_date": "2023-06-12", "type": "article", "language": "en", "authors": [ "Yiying Zhang", "Guanzhong Zhou", "Peng Hang", "Chao Huang", "Hailong Huang" ], "first_author": "Yiying Zhang", "venue": "IEEE Transactions on Intelligent Transportation Systems", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "Using drones to carry out commercial parcel delivery can significantly promote the transformation and upgrading of the logistics industry thanks to the saving of human labor source, which is becoming a new component of intelligent transportation systems. However, the flight distance of drones is often constrained due to the limited battery capacity. To address this challenge, this paper designs a multi-drones-assisted commercial parcel delivery system, which supports long-distance delivery by a generalized service network (GSN). Each node of the GSN is equipped with charging piles to provide a charging service for drones. Given the limited number of charging piles at each node and the limited battery capacity of a drone, to ensure the efficient operation of the system, the flight planning problem of drones is converted into a large-scale optimization problem by a priority-based encoding mechanism. To solve this problem, an enhanced backtracking search algorithm (EBSA) is reported, which is inspired by the characteristics of the considered flight planning problem and the weak ability of the backtracking search algorithm to escape from a local optimum. The core components of EBSA are the designed comprehensive learning mechanism and local escape operator. Experimental results prove the validity of the improved strategies and the excellent performance of EBSA on the considered flight planning problem.", "keywords": [ "Backtracking", "Drone", "Node (physics)", "Computer science", "Service (business)", "Local search (optimization)", "Engineering", "Mathematical optimization", "Operations research", "Artificial intelligence", "Algorithm", "Mathematics" ], "cited_by_count": 12, "is_retracted": false, "is_paratext": false, "open_access_url": "http://ira.lib.polyu.edu.hk/bitstream/10397/99333/1/Zhang_Enhanced_Backtracking_Search.pdf" }, { "id": "https://openalex.org/W2971057457", "doi": "10.1007/978-3-030-28377-3_42", "title": "An Integer Programming Model for the Capacitated Vehicle Routing Problem with Drones", "year": 2019, "publication_date": "2019-01-01", "type": "conference-paper", "language": "en", "authors": [ "Jarosław Wikarek", "Paweł Sitek", "Lukasz Zawarczynski" ], "first_author": "Jarosław Wikarek", "venue": "Lecture notes in computer science", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Drone", "Computer science", "Truck", "Payload (computing)", "Integer programming", "Routing (electronic design automation)", "Operations research", "Mathematical optimization", "Linear programming", "Computer network", "Network packet", "Engineering" ], "cited_by_count": 27, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-030-28377-3_42" }, { "id": "https://openalex.org/W4386265770", "doi": "10.1007/978-3-031-36698-7_21", "title": "Machine Learning for Drone-Based Last Mile Delivery of Perishables", "year": 2023, "publication_date": "2023-01-01", "type": "conference-paper", "language": "en", "authors": [ "Selwyn Piramuthu" ], "first_author": "Selwyn Piramuthu", "venue": "Progress in IS", "publisher": "Springer International Publishing", "abstract": null, "keywords": [ "Drone", "Mile", "Last mile (transportation)", "Computer science", "Engineering", "Geography" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-031-36698-7_21" }, { "id": "https://openalex.org/W3173630589", "doi": "10.3390/electronics10131549", "title": "Machine-Learning-Enabled Intrusion Detection System for Cellular Connected UAV Networks", "year": 2021, "publication_date": "2021-06-26", "type": "article", "language": "en", "authors": [ "Rakesh Shrestha", "Atefeh Omidkar", "Sajjad Ahmadi Roudi", "Hassan Abbas", "Shiho Kim" ], "first_author": "Rakesh Shrestha", "venue": "Electronics", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "The recent development and adoption of unmanned aerial vehicles (UAVs) is due to its wide variety of applications in public and private sector from parcel delivery to wildlife conservation. The integration of UAVs, 5G, and satellite technologies has prompted telecommunication networks to evolve to provide higher-quality and more stable service to remote areas. However, security concerns with UAVs are growing as UAV nodes are becoming attractive targets for cyberattacks due to enormously growing volumes and poor and weak inbuilt security. In this paper, we propose a UAV- and satellite-based 5G-network security model that can harness machine learning to effectively detect of vulnerabilities and cyberattacks. The solution is divided into two main parts: the model creation for intrusion detection using various machine learning (ML) algorithms and the implementation of ML-based model into terrestrial or satellite gateways. The system identifies various attack types using realistic CSE-CIC IDS-2018 network datasets published by Canadian Establishment for Cybersecurity (CIC). It consists of seven different types of new and contemporary attack types. This paper demonstrates that ML algorithms can be used to classify benign or malicious packets in UAV networks to enhance security. Finally, the tested ML algorithms are compared for effectiveness in terms of accuracy rate, precision, recall, F1-score, and false-negative rate. The decision tree algorithm performed well by obtaining a maximum accuracy rate of 99.99% and a minimum false negative rate of 0% in detecting various attacks as compared to all other types of ML classifiers.", "keywords": [ "Computer science", "Intrusion detection system", "Artificial intelligence", "Network packet", "Machine learning", "Decision tree", "Computer security", "Real-time computing", "Computer network" ], "cited_by_count": 138, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2079-9292/10/13/1549/pdf?version=1624872769" }, { "id": "https://openalex.org/W4389611470", "doi": "10.1016/j.tranpol.2023.12.004", "title": "Examining urban delivery service user profiles and determinants of drone delivery adoption in Ghana considering usage before and after the COVID-19 pandemic", "year": 2023, "publication_date": "2023-12-12", "type": "article", "language": "en", "authors": [ "Reuben Tamakloe", "Kaihan Zhang", "Jonathan Atandzi", "Dongjoo Park" ], "first_author": "Reuben Tamakloe", "venue": "Transport Policy", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Pandemic", "Business", "Drone", "Service (business)", "Probit", "Service delivery framework", "Probit model", "Marketing", "Internet privacy", "Environmental health", "Coronavirus disease 2019 (COVID-19)", "Medicine" ], "cited_by_count": 19, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.tranpol.2023.12.004" }, { "id": "https://openalex.org/W7154564162", "doi": "10.4018/979-8-3373-7128-3.ch009", "title": "Technology Innovations and Digital Solutions for Sustainable Last-Mile Logistics", "year": 2026, "publication_date": "2026-04-16", "type": "book-chapter", "language": "ng", "authors": [ "Deepak Gupta", "Erkin Kholiyarov", "Anvar Uralbayev", "Bekzod Madaminov", "Abdullayeva Marjona", "Nazirjon Rajabov", "Ergashev Nuriddin" ], "first_author": "Deepak Gupta", "venue": null, "publisher": null, "abstract": "The rapid growth of e-commerce and urbanization has intensified the complexity of last-mile logistics, creating significant environmental and operational challenges. This chapter examines the transformative role of technology innovations and digital solutions in achieving sustainable last-mile delivery systems. It explores cutting-edge technologies including autonomous vehicles, drones, electric fleets, blockchain for supply chain transparency, artificial intelligence for route optimization, Internet of Things (IoT) sensors, and crowdsourcing platforms. The chapter analyzes how these innovations address traditional challenges such as traffic congestion, carbon emissions, delivery failures, and cost inefficiencies.", "keywords": [ "Transformative learning", "Supply chain", "Internet of Things", "Crowdsourcing", "Cloud computing", "Digital transformation", "Sustainable growth rate", "The Internet" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.4018/979-8-3373-7128-3.ch009" }, { "id": "https://openalex.org/W4389302055", "doi": "10.1109/ice/itmc58018.2023.10332420", "title": "Drone Delivery Systems for Logistics Operations: Identification of Regulatory Challenges", "year": 2023, "publication_date": "2023-06-19", "type": "conference-paper", "language": "en", "authors": [ "Batool Madani", "Malick Ndiaye" ], "first_author": "Batool Madani", "venue": null, "publisher": null, "abstract": "With the continued expansion of e-commerce, the logistical obligation to address last-mile delivery challenges has grown substantially. Innovative solutions such as drones are being integrated into delivery networks to improve delivery operations. The deployment of drones has given new opportunities to design improved logistics systems in terms of low delivery cost and customer satisfaction. While logistics companies are primarily motivated to minimize last-mile costs, regulatory authorities should pursue efficient last-mile processes in order to accomplish broader goals and ensure sustainability. In other words, regulatory authorities must develop, implement, and enforce legislation for drone operations more promptly as drone technologies drive new business and service models. However, current regulatory frameworks do not yet consider the use of drones in delivery systems. Therefore, the purpose of this study is to identify the critical challenges of adopting drones in delivery operations, followed by proposing the main pillars of a drone regulatory framework. These pillars serve as the foundation for the establishment of comprehensive regulatory frameworks, which will be based on the integration of industry and regulatory authorities’ standpoints.", "keywords": [ "Drone", "Last mile (transportation)", "Business", "Software deployment", "Legislation", "Identification (biology)", "Risk analysis (engineering)", "Sustainability", "Process management", "Best practice", "Obligation", "Computer science" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/ice/itmc58018.2023.10332420" }, { "id": "https://openalex.org/W4388553081", "doi": "10.1016/j.engappai.2023.107363", "title": "A novel mathematical model and a hybrid grouping evolution strategy algorithm for an automated last mile delivery system considering wind effect", "year": 2023, "publication_date": "2023-11-09", "type": "article", "language": "en", "authors": [ "M.T. Ahmadi", "Seyed Hessameddin Zegordi" ], "first_author": "M.T. Ahmadi", "venue": "Engineering Applications of Artificial Intelligence", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Computer science", "Last mile (transportation)", "Heuristic", "Time horizon", "Operations research", "Mathematical optimization", "Routing (electronic design automation)", "Mile", "Artificial intelligence", "Computer network", "Mathematics" ], "cited_by_count": 11, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.engappai.2023.107363" }, { "id": "https://openalex.org/W4411628874", "doi": "10.1016/j.tre.2025.104273", "title": "Assessing the sustainability of last-mile distribution strategies to manage expedited shipping with dynamic and stochastic demand", "year": 2025, "publication_date": "2025-06-25", "type": "article", "language": "en", "authors": [ "Anmol Pahwa", "Miguel Jaller" ], "first_author": "Anmol Pahwa", "venue": "Transportation Research Part E Logistics and Transportation Review", "publisher": "Elsevier BV", "abstract": "As many e-retailers compete through increasingly consumer-focused services, urban freight faces a critical need for sustainable alternate last-mile distribution practices. While previous research has investigated the performance of various distribution strategies under different planning and network design scenarios, their performance under the dynamic and stochastic conditions inherent to consumer-focused services (e.g., expedited deliveries) is not well understood. This study introduces a dynamic-stochastic last-mile network design (DS-LMND) problem, formulated as a multi-echelon capacitated location routing problem with time-windows (ME-C-LRP-TW), to address this gap. Consequently, this work develops a Monte Carlo simulation–optimization framework integrated with an adaptive large neighborhood search (ALNS) metaheuristic to solve the problem. Using this framework, the authors configure the distribution structure and simulate last-mile operations for each distribution strategy. For these configured distribution structures, this work determines economic viability, environmental efficiency, and social equity – the three pillars of sustainability. Further, the authors determine the impact of demand uncertainties on the sustainability of last-mile distribution through the value of information (VI) and coefficient of variance (CV) metrics. In doing so, this work a) estimates the efficacy of conventional distribution strategy; b) confirms the competitiveness of electric delivery vehicles; c) evaluates the effectiveness of crowdsourced delivery services; d) advances the case for consolidation-based multi-echelon distribution strategies; e) establishes the rationale for customer pickups; and f) develops the use case for drone and robots, to cater to dynamic and stochastic demand with expedited shipping.", "keywords": [ "Mile", "Sustainability", "Last mile (transportation)", "Transport engineering", "Business", "Demand management", "Environmental economics", "Operations research", "Engineering", "Economics", "Geography" ], "cited_by_count": 10, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.tre.2025.104273" }, { "id": "https://openalex.org/W4313017794", "doi": "10.1016/j.ifacol.2022.10.212", "title": "Optimal models for autonomous trucks and drones resupply for last-mile delivery in urban areas", "year": 2022, "publication_date": "2022-01-01", "type": "article", "language": "en", "authors": [ "Zhe Yuan", "Simon HERVE" ], "first_author": "Zhe Yuan", "venue": "IFAC-PapersOnLine", "publisher": "Elsevier BV", "abstract": "The last-mile delivery in urban areas is the biggest challenge for carriers. In recent years, e-commerce has experienced strong growth, which has caused an explosion in the number of packages to be delivered. Businesses no longer hesitate to invest in new technologies such as artificial intelligence and drones. In this paper, we present a model of autonomous truck delivery resupply by drones, introducing the concept of transshipment point. Through comparisons with other delivery models, we show that our solution can be economically viable and improve the lives of inhabitants and customers.", "keywords": [ "Drone", "Last mile (transportation)", "Truck", "Transshipment (information security)", "Mile", "Transport engineering", "Computer science", "Point (geometry)", "Business", "Engineering", "Computer security", "Geography" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.ifacol.2022.10.212" }, { "id": "https://openalex.org/W4411104339", "doi": "10.1155/atr/3995437", "title": "The Delivery Time Performance Study of a Novel Collaborative Delivery System Integrating Drones and Ground Public Transit for Urban Last‐Mile Delivery", "year": 2025, "publication_date": "2025-01-01", "type": "article", "language": "en", "authors": [ "Shaozhi Hong", "Jiayu Zang", "Zeling Wang" ], "first_author": "Shaozhi Hong", "venue": "Journal of Advanced Transportation", "publisher": "Hindawi Publishing Corporation", "abstract": "With the rapid expansion of e‐commerce, effectively managing last‐mile delivery while satisfying consumer demands for timeliness and convenience has become increasingly imperative. Traditional delivery systems, which predominantly depend on human labor and freight vehicles, encounter significant challenges, including traffic congestion, escalating labor expenses, and environmental degradation. The advancements in drone technology present a promising solution for the logistics domain. This study proposes a novel collaborative delivery system that integrates drones with ground public transit, and aims to assess the delivery time performance of this system. Within this system, drones are responsible for direct parcel delivery to customers, while ground public transportation vehicles handle the transportation of drones and parcels between designated stops. The study develops a delivery strategy and establishes an operational framework to attain the delivery time for each customer. Subsequently, this study assesses the delivery time performance of this delivery system by introducing relevant metrics. Numerical experiments conducted across three distinct scenarios validate the proposed methodology, demonstrating the system’s potential in urban last‐mile delivery. The findings indicate that the collaborative delivery system exhibits good performance, especially in multi‐parcel delivery mode compared to single‐parcel delivery mode. Moreover, sensitivity analyses are performed to explore the effects of departure intervals and dwell time and obtain valuable insights on system operations.", "keywords": [ "Mile", "Drone", "Last mile (transportation)", "Public transport", "Transit (satellite)", "Delivery system", "Transport engineering", "Ground transportation", "Transit system", "Food delivery", "Engineering", "Computer science" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://onlinelibrary.wiley.com/doi/pdfdirect/10.1155/atr/3995437" }, { "id": "https://openalex.org/W4413193373", "doi": "10.4018/979-8-3373-3643-5.ch012", "title": "Drones and UAVs for Rapid Emergency Relief and Last-Mile Delivery", "year": 2025, "publication_date": "2025-08-12", "type": "book-chapter", "language": "en", "authors": [ "Nazim Atluyev" ], "first_author": "Nazim Atluyev", "venue": "Advances in computational intelligence and robotics book series", "publisher": "IGI Global", "abstract": "This chapter explores the transformative role of Unmanned Aerial Vehicles (UAVs), commonly known as drones, in enhancing emergency relief logistics and last-mile delivery performance. As supply chains face increasing disruption from natural disasters, pandemics, and urban congestion, UAVs offer rapid, flexible, and contactless delivery solutions that improve reach, resilience, and sustainability. The chapter provides a comprehensive review of technological foundations, real-world applications, and strategic innovations, including digital twin systems, IoT integration, and AI-based optimization. Drawing on recent academic and operational literature, it critically examines key challenges such as regulatory constraints, technical limitations, and organizational barriers. The findings emphasize the need for harmonized policy frameworks, infrastructure readiness, and ethical design to support scalable and sustainable drone integration into future logistics systems.", "keywords": [ "Drone", "Mile", "Last mile (transportation)", "Aeronautics", "Medical emergency", "Geography", "Engineering", "Medicine", "Geodesy", "Biology" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.4018/979-8-3373-3643-5.ch012" }, { "id": "https://openalex.org/W4380574656", "doi": "10.20944/preprints202306.0982.v1", "title": "Exploring the Viability of Last-Mile Delivery Solutions for Sustainable Supply Chains", "year": 2023, "publication_date": "2023-06-14", "type": "preprint", "language": "en", "authors": [ "Paraschos Maniatis" ], "first_author": "Paraschos Maniatis", "venue": "Preprints.org", "publisher": null, "abstract": "Sustainable supply chains have become increasingly crucial to address environmental and social challenges faced by societies worldwide. Last-mile delivery, which involves delivering products from a distribution center or warehouse to the final destination, typically a consumer's doorstep, is an essential component of the supply chain. However, it faces numerous challenges such as traffic congestion, air and noise pollution, and carbon emissions. This research paper explores the viability of last-mile delivery solutions for sustainable supply chains. The study reviews the literature on last-mile delivery challenges, sustainable last-mile delivery solutions, and case studies of sustainable last-mile delivery solutions. The results indicate that innovative last-mile delivery solutions, such as electric vehicles, cargo bikes, and drones, have the potential to reduce environmental impact and improve the efficiency of last-mile delivery while enhancing social equity. Additionally, partnerships between different stakeholders, including governments, logistics companies, and consumers, can contribute to the development of sustainable last-mile delivery solutions. The study concludes that sustainable last-mile delivery solutions are essential to achieving sustainable supply chains and recommends further research to explore the economic feasibility and scalability of these solutions.", "keywords": [ "Last mile (transportation)", "Business", "Mile", "Supply chain", "Environmental economics", "Sustainability", "Sustainable development", "Marketing", "Economics", "Geography" ], "cited_by_count": 7, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.preprints.org/manuscript/202306.0982/v1/download" }, { "id": "https://openalex.org/W4409795930", "doi": "10.1109/tce.2025.3564412", "title": "Efficient Path-Following for Urban Logistics: A Fuzzy Control Strategy for Consumer UAVs Under Disturbance Constraints", "year": 2025, "publication_date": "2025-04-25", "type": "article", "language": "en", "authors": [ "Xingling Shao", "Jun Du", "Yi Xia", "Zekai Zhang", "Xiangwang Hou", "Mérouane Debbah" ], "first_author": "Xingling Shao", "venue": "IEEE Transactions on Consumer Electronics", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "The adoption of consumer unmanned aerial vehicles (UAVs) for logistics transportation is a key aspect of the emerging low-altitude economy, yet it faces significant challenges. Urban environments introduce complex obstacles, including dense buildings and unpredictable wind disturbances, while existing control methods struggle to balance path-following accuracy, disturbance rejection, and communication efficiency. To meet these demands, this paper proposes a quantized fuzzy learning path-following control for consumer UAVs. Firstly, a hysteresis quantized fuzzy disturbance observer (HQFDO) is proposed where the disturbances are approximately estimated by a neural network. Notably, a hysteresis quantizer is employed to reduce the communication bandwidth occupation by discretizing disturbance observations. Subsequently, a distributed velocity controller and a heading angle controller are designed to tackle the geometric and dynamic tasks separately. Specifically, the velocity controller introduces a projective arc length error to mitigate inefficiencies and safety risks associated with frequent acceleration and deceleration switches. Compared to conventional techniques, the proposed approach improves transient performance, enhances path attractivity, and optimizes communication resource utilization. Theoretical stability analysis is provided, and simulations validate the effectiveness of the proposed control strategy.", "keywords": [ "Disturbance (geology)", "Path (computing)", "Computer science", "Fuzzy logic", "Control theory (sociology)", "Control (management)", "Fuzzy control system", "Engineering", "Control engineering", "Artificial intelligence" ], "cited_by_count": 28, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/tce.2025.3564412" }, { "id": "https://openalex.org/W2955956482", "doi": "10.1109/emr.2019.2926245", "title": "A Simulation-Based Process Model for Managing Drone Deployment to Minimize Total Delivery Time", "year": 2019, "publication_date": "2019-07-01", "type": "article", "language": "en", "authors": [ "David Swanson" ], "first_author": "David Swanson", "venue": "IEEE Engineering Management Review", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "Managers in many countries are exploring innovative solutions for last mile delivery, including using delivery drones. In fact, drone delivery is already improving logistics service in many countries. There are, however, unaddressed nuances when comparing drones with surface vehicles. An assumption often made is that delivery time is independent of delivery processes that are not typically associated with drive time, such as loading. This can be acceptable for comparing alternate types of surface delivery vehicles, but not when comparing surface delivery vehicles with drones. To address this issue, empirical store-to-consumer deliveries are collected from a retail establishment in the United States and used to design and calibrate a discrete event simulation model to match the retailer's delivery process. Using this model, drones are compared with surface vehicles under alternate changes of state. The model is given a range of post hoc tests to further define the dimensions of the model and to generalize it for research applications in multiple contexts.", "keywords": [ "Drone", "Software deployment", "Process (computing)", "Discrete event simulation", "Computer science", "Last mile (transportation)", "Operations research", "Simulation", "Engineering", "Mile", "Geography" ], "cited_by_count": 30, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/emr.2019.2926245" }, { "id": "https://openalex.org/W4410909688", "doi": "10.1016/j.cja.2025.103605", "title": "Integrating wind field analysis in UAV path planning: Enhancing safety and energy efficiency for urban logistics", "year": 2025, "publication_date": "2025-05-31", "type": "article", "language": "en", "authors": [ "Ruijia GU", "Yifei Zhao", "Xinhui Ren" ], "first_author": "Ruijia GU", "venue": "Chinese Journal of Aeronautics", "publisher": "Elsevier BV", "abstract": "Shenzhen, a major city in southern China, has experienced rapid advancements in Unmanned Aerial Vehicle (UAV) technology, resulting in extensive logistics networks with thousands of daily flights. However, frequent disruptions due to its subtropical monsoon climate, including typhoons and gusty winds, present ongoing challenges. Despite the growing focus on operational costs and third-party risks, research on low-altitude urban wind fields remains scarce. This study addresses this gap by integrating wind field analysis into UAV path planning, introducing key innovations to the classical model. First, UAV wind resistance and turbulence constraints are analyzed, mapping high-wind-speed and turbulence-prone zones in the airspace. Second, wind dynamics are incorporated into path planning by considering airspeed and groundspeed variation, optimizing waypoint selection and flight speed adjustments to improve overall energy efficiency. Additionally, a wind-aware Theta* algorithm is proposed, leveraging wind vectors to expedite search process, while Computational Fluid Dynamics (CFD) techniques are employed to calculate wind fields. A case study of Shenzhen, examining wind patterns over the past decade, demonstrates a 6.23% improvement in groundspeed and a 7.69% reduction in energy consumption compared to wind-agnostic models. This framework advances UAV logistics by enhancing route safety and energy efficiency, contributing to more cost-effective operations.", "keywords": [ "Field (mathematics)", "Path (computing)", "Motion planning", "Energy (signal processing)", "Wind power", "Environmental science", "Marine engineering", "Aerospace engineering", "Computer science", "Transport engineering", "Engineering", "Physics" ], "cited_by_count": 28, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.cja.2025.103605" }, { "id": "https://openalex.org/W4312113338", "doi": "10.1126/scirobotics.adf5798", "title": "Testing the delivery of human organ transportation with drones in the real world", "year": 2022, "publication_date": "2022-12-21", "type": "article", "language": "en", "authors": [ "Andrew T. Sage", "Marcelo Cypel", "Mikaël Cardinal", "Jimmy Qiu", "Atul Humar", "Shaf H Keshavjee" ], "first_author": "Andrew T. Sage", "venue": "Science Robotics", "publisher": "American Association for the Advancement of Science", "abstract": "Last-mile transportation of human donor lungs in a densely populated urban environment has been made possible with drones.", "keywords": [ "Drone", "Aeronautics", "Computer science", "Engineering", "Biology" ], "cited_by_count": 24, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1126/scirobotics.adf5798" }, { "id": "https://openalex.org/W2910293963", "doi": "10.1007/978-3-030-10752-9_12", "title": "The Role of Drones in Ambient Assisted Living Systems for the Elderly", "year": 2019, "publication_date": "2019-01-01", "type": "conference-paper", "language": "en", "authors": [ "Radosveta İvanova SOKULLU", "Abdullah Balcı", "Eren Demir" ], "first_author": "Radosveta İvanova SOKULLU", "venue": "Lecture notes in computer science", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Drone", "Assisted living", "Everyday life", "Computer science", "Health care", "Engineering", "Business", "Computer security", "Medicine", "Political science", "Gerontology" ], "cited_by_count": 19, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-030-10752-9_12" }, { "id": "https://openalex.org/W2973485708", "doi": "10.25394/pgs.8028974.v1", "title": "Analysis of Energy Efficiency in Truck-Drone “Last Mile” Delivery Systems", "year": 2019, "publication_date": "2019-06-10", "type": "dissertation", "language": "en", "authors": [ "Juan Carlos Moreno Orozco" ], "first_author": "Juan Carlos Moreno Orozco", "venue": "Figshare", "publisher": "Figshare (United Kingdom)", "abstract": "Truck-drone delivery systems have the potential to improve how the logistics industry approaches the “last mile problem”. For the purposes of this study, the “last mile” refers to the portion of the journey between the last transportation hub and the individual customer that will consume the product. Drones can deliver packages directly, without the need for an underlying transportation network but are limited by their range and payload capacity. Studies have developed multiple truck-drone configurations, each with different approaches to leverage the benefits and mitigate the limitations of drones. Existing research has also established the drone’s reduction to package delivery time over the traditional truck only model. Two key model factors that have not been considered in previous research are the distribution of package demand, and the distribution of package weight. This study analyzes the drone’s impact to the energy efficiency of a package delivery system, which has taken a backseat to minimizing delivery time. Demand distribution dictates the travel distances required for package delivery, as well as the proportion of delivery locations that are in range for drone delivery. Package weight determines the energy consumption of a delivery and further restricts the proportion of drone eligible packages. The major contributions of this study are the development of a truck-drone tandem mathematical model which minimizes energy consumption, the construction of a population-based package demand distribution, a realistic package weight distribution, and a genetic algorithm used to solve the mathematical model developed for problems that are too computationally expensive to be solved optimally using an exact method. Results show that drones can only have a significant impact to energy efficiency in package delivery systems if implemented under the right conditions. Using truck-drone tandem systems in areas with lower package demand density affords the drone the potential for larger energy savings as larger portions of the truck distance can be replaced. Further, the lower density translates to greater differences between the road-restricted driving distance and the flying distance between delivery points. Finally, energy savings are highly dependent on the underlying package weight distribution of the system. A heavier average package weight increases the energy consumption of the system, but more importantly the portion of packages above the drone’s payload capacity severely limit the savings afforded by the incorporation of drones.", "keywords": [ "Drone", "Last mile (transportation)", "Truck", "Energy consumption", "Payload (computing)", "Engineering", "Transport engineering", "Population", "Leverage (statistics)", "Computer science", "Operations research", "Mile" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://figshare.com/articles/Analysis_of_Energy_Efficiency_in_Truck-Drone_Last_Mile_Delivery_Systems/8028974" }, { "id": "https://openalex.org/W3211859692", "doi": "10.1016/j.eswa.2021.116160", "title": "Cluster-based WSA method to elicit expert knowledge for Bayesian reasoning—Case of parcel delivery with drone", "year": 2021, "publication_date": "2021-11-17", "type": "article", "language": "en", "authors": [ "Imen Ben Brahim", "Sid-Ali Addouche", "Abderrahman El Mhamedi", "Younes Boujelbène" ], "first_author": "Imen Ben Brahim", "venue": "Expert Systems with Applications", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Computer science", "Expert elicitation", "Cluster (spacecraft)", "Drone", "Expert system", "Bayesian probability", "Artificial intelligence", "Machine learning", "Data mining", "Task (project management)", "Coherence (philosophical gambling strategy)", "Statistics" ], "cited_by_count": 11, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.eswa.2021.116160" }, { "id": "https://openalex.org/W7152242972", "doi": "10.1371/journal.pone.0344897", "title": "Rural first-mile pickup and last-mile delivery: A bus-assisted heterogeneous-drone model", "year": 2026, "publication_date": "2026-04-08", "type": "article", "language": "en", "authors": [ "Song Jin", "Lu Wang", "Yunpeng Gong", "Jingyu Hu" ], "first_author": "Song Jin", "venue": "PLoS ONE", "publisher": "Public Library of Science", "abstract": "With the rapid expansion of rural e-commerce, widely dispersed demand and limited road infrastructure have made conventional truck-based first-mile pickup and last-mile delivery increasingly unsustainable, creating an urgent need for alternative logistics models. We introduce a bus-assisted heterogeneous-drone scheme that treats fixed-route rural buses as mobile hubs while dispatching drones with complementary ranges and payloads for door-to-door service. A mixed-integer programming model captures bus schedules, drone heterogeneity, time-window constraints, and battery limits. To solve this model efficiently, we develop a two-stage framework-bus-stop clustering followed by an Improved Black-Kite Algorithm (IBKA). IBKA incorporates four enhancements: opposition-based learning, adaptive attack probability, random boundary shrinkage, and a Differential Evolution hybrid operator. Numerical experiments on adapted Solomon instances show the proposed method outperforms Gurobi, a standard Genetic Algorithm (GA), an Eel and Grouper Optimizer (EGO), and the original Black-Kite Algorithm (BKA) in terms of cost, stability, and convergence. On average, IBKA reduces total delivery cost by 5% relative to GA, 9% relative to EGO, and 13% relative to BKA, and enhances stability by 23%, 55%, and 23%, respectively. Sensitivity tests highlight the pivotal influence of drone payload and bus headway. A real-world study on the Xunyang-Tongqianguan line in Shaanxi Province further demonstrates substantial cost savings and operational advantages over both truck-only and homogeneous-drone delivery modes, underscoring the practical value of bus-drone collaboration for rural logistics.", "keywords": [ "Drone", "Pickup", "Cluster analysis", "Payload (computing)", "Sensitivity (control systems)", "Scheme (mathematics)", "Boundary (topology)", "Genetic algorithm", "Transit (satellite)", "Toll" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1371/journal.pone.0344897" }, { "id": "https://openalex.org/W4390826113", "doi": "10.1080/23302674.2023.2301610", "title": "Heuristic approach for optimising reliable supply chain network using drones in last-mile delivery under uncertainty", "year": 2024, "publication_date": "2024-01-11", "type": "article", "language": "en", "authors": [ "Ali Tolooie", "Ashesh Kumar Sinha", "Aida Khosh Raftar Nouri" ], "first_author": "Ali Tolooie", "venue": "International Journal of Systems Science Operations & Logistics", "publisher": "Taylor & Francis", "abstract": "In addressing the challenges of last-mile logistics, the reliability of the supply chain network becomes paramount. These challenges are intensified due to drone performance limitations and various uncertainties in supply chain operations. While recent literature recognises the potential of drones for last-mile delivery, it falls short in effectively considering these uncertainties in drone-enabled supply chain models. Our research addresses this gap with two major contributions: first, a novel stochastic mixed-integer programming model is developed to construct a feasible delivery network, including warehouses and recharging stations, enhancing both coverage and reliability. Second, a modification in the genetic algorithm by considering each scenario independently improves computational efficiency, outperforming commercial software by an average of 40% and up to 55%. Empirical findings reveal that strategic investments in system hardening can yield substantial improvements in reliability. Despite the absence of real-world stochastic parameters as a limitation, this research pioneers the design of reliable networks under uncertainties and extends drone coverage through strategic charging stations. This work sets a significant milestone for future optimization in drone logistics, offering practical implications for supply chain managers considering drone adoption.", "keywords": [ "Drone", "Last mile (transportation)", "Supply chain", "Computer science", "Supply chain network", "Reliability (semiconductor)", "Operations research", "Stochastic programming", "Heuristic", "Supply chain management", "Reliability engineering", "Engineering" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1080/23302674.2023.2301610" }, { "id": "https://openalex.org/W4406752813", "doi": "10.1016/j.trd.2025.104610", "title": "Optimizing drone logistics in complex urban industrial infrastructure", "year": 2025, "publication_date": "2025-01-23", "type": "article", "language": "en", "authors": [ "Tomasz Dudek", "Karolina Kaśkosz" ], "first_author": "Tomasz Dudek", "venue": "Transportation Research Part D Transport and Environment", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Business", "City logistics", "Transport engineering", "Critical infrastructure", "Engineering", "Aeronautics", "Computer science", "Computer security" ], "cited_by_count": 26, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.trd.2025.104610" }, { "id": "https://openalex.org/W4411628128", "doi": "10.1016/j.trc.2025.105189", "title": "Collaborative vessel–unmanned aerial vehicle routing for time-window-constrained offshore parcel delivery", "year": 2025, "publication_date": "2025-06-25", "type": "article", "language": "en", "authors": [ "Yantong Li", "Shengjie Wang", "Hairui Sun", "Shanshan Zhou" ], "first_author": "Yantong Li", "venue": "Transportation Research Part C Emerging Technologies", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Submarine pipeline", "Window (computing)", "Marine engineering", "Vehicle routing problem", "Routing (electronic design automation)", "Transport engineering", "Computer science", "Aeronautics", "Environmental science", "Engineering", "Real-time computing", "Computer network" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.trc.2025.105189" }, { "id": "https://openalex.org/W4409325710", "doi": "10.71146/kjmr366", "title": "AUTONOMOUS DRONE NAVIGATION USING AI: REVOLUTIONIZING LAST-MILE DELIVERY IN E-COMMERCE AND LOGISTICS", "year": 2025, "publication_date": "2025-03-29", "type": "article", "language": "en", "authors": [ "Nadia Mustaqim Ansari", "Naib Khan", "Rizwan Iqbal", "Muhammad Fahmi Ismail", "Hassam Gul" ], "first_author": "Nadia Mustaqim Ansari", "venue": "Kashf Journal of Multidisciplinary Research", "publisher": null, "abstract": "In integrating artificial intelligence (AI) into autonomous drone technology, researcher seen how autonomous drone technology switched from last mile delivery for delivering goods to enhance efficiency, reduce the operational costs, and minimize environmental impact. In this study, researcher try to understand the applying of AI ways on navigation systems based on deep learning, reinforcement learning, and computer vision that make it possible to optimize a route and also to keep away from obstacles. In the design and testing of AI-powered drones, the research uses a mix of simulated environments and actual environments to determine the performance in urban and rural settings. Results show that AI based navigation greatly enhances the delivery speed, energy and accuracy as opposed to conventional delivery approaches. The discussion also enumerates the advantages and disadvantages of using AI powered drones given the regulatory barriers, weather constraints, public acceptance issues. Future research can be focused on making AI more adaptable in dynamic environments by blockchain integration for the secure tracking of package as well as use of 5G network for real-time control. The recommendations are: to develop standardized regulations for drone logistics, enhance the collaboration between the AI researcher and the policymakers and also improve the public awareness of the autonomous drone operations. The results lay the grounds for smart logistics to arrive at sustainable solutions for the last mile delivery of the future.", "keywords": [ "Drone", "Mile", "Last mile (transportation)", "Aeronautics", "Computer science", "Engineering", "Transport engineering", "Geography" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://kjmr.com.pk/kjmr/article/download/366/463" }, { "id": "https://openalex.org/W4220897283", "doi": "10.2139/ssrn.4065011", "title": "Feasibility of Drones & Agvs in the Last Mile Delivery: Lessons from Germany", "year": 2022, "publication_date": "2022-01-01", "type": "preprint", "language": "en", "authors": [ "Christian Fehling", "Adriana Saraceni" ], "first_author": "Christian Fehling", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Mile", "Last mile (transportation)", "Drone", "Business", "Process (computing)", "Service delivery framework", "Analytic hierarchy process", "Service (business)", "Process management", "Transport engineering", "Operations management", "Engineering" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.4065011" }, { "id": "https://openalex.org/W4372279241", "doi": "10.3390/drones7050300", "title": "Drone High-Rise Aerial Delivery with Vertical Grid Screening", "year": 2023, "publication_date": "2023-05-04", "type": "article", "language": "en", "authors": [ "Avishkar Seth", "Alice James", "Endrowednes Kuantama", "Subhas Chandra Mukhopadhyay", "Richard Y. Han" ], "first_author": "Avishkar Seth", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Delivery drones typically perform delivery by suspending the parcel vertically or landing the drone to drop off the package. However, because of the constrained landing area and the requirement for precise navigation, delivering items to customers who reside in multi-story apartment complexes poses a unique challenge. This research paper proposes a novel drone delivery system for multi-story apartment buildings with balconies that employ two methods for Vertical Grid Screening (VGS), i.e., Grid Screening (GS) and Square Screening (SS), to detect unique markers to identify the precise balcony that needs to receive the product. The developed drone has a frame size of 295 mm and is equipped with a stereo camera and a ranging sensor. The research paper also explores the scanning and trajectory methods required for autonomous flight to accurately approach the marker location. The proposed machine learning system is trained on a YOLOv5 model for image recognition of the marker, and four different models and batch sizes are compared. The 32-batch size with a 960 × 1280 resolution model provides an average of 0.97 confidence for an extended range. This system is tested outdoors and shows an accuracy of 95% for a planned trajectory with 398 ms detection time as a solution for last-mile delivery in urban areas.", "keywords": [ "Drone", "Grid", "Computer science", "Real-time computing", "Trajectory", "Frame (networking)", "Artificial intelligence", "Range (aeronautics)", "Simulation", "Computer vision", "Geography", "Engineering" ], "cited_by_count": 23, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2504-446X/7/5/300/pdf?version=1683283556" }, { "id": "https://openalex.org/W4406206408", "doi": "10.1016/j.tranpol.2025.01.016", "title": "Truck-drone joint delivery network for rural area: Optimization and implications", "year": 2025, "publication_date": "2025-01-10", "type": "article", "language": "en", "authors": [ "Jing Lu", "Yuman Liu", "Changmin Jiang", "Weiwei Wu" ], "first_author": "Jing Lu", "venue": "Transport Policy", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Truck", "Drone", "Joint (building)", "Transport engineering", "Computer science", "Business", "Engineering", "Civil engineering", "Automotive engineering" ], "cited_by_count": 27, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.tranpol.2025.01.016" }, { "id": "https://openalex.org/W4283072798", "doi": "10.3390/app12126140", "title": "A Decision System for Routing Problems and Rescheduling Issues Using Unmanned Aerial Vehicles", "year": 2022, "publication_date": "2022-06-16", "type": "article", "language": "en", "authors": [ "I-Ching Lin", "Tsan-Hwan Lin", "Sheng-Hung Chang" ], "first_author": "I-Ching Lin", "venue": "Applied Sciences", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "In recent years, consumers have come to expect faster and better delivery services. Logistics companies, therefore, must implement innovative technologies or services in their logistics processes. It is critical to adopt unmanned aerial vehicles (UAV) in last mile delivery and urban logistics. The service provider applies the characteristics of UAVs to complete more requests, benefiting more revenue. However, it may not be a satisfactory solution, because the customers will be dissatisfied if the actual delivery time does not align with their expectations. This study constructs a revenue maximization model subject to time windows and customer satisfaction. Instead of addressing the traveling salesmen problem, this model takes new customer requests during the delivery process into account. We solved the problem using a genetic algorithm. The results show: (1) the model found an approximate and effective solution in the real-time delivery environment; (2) customer satisfaction is inversely proportional to the total delivery distance; (3) regarding the result of the sensitivity analysis of this study, investment in UAV has no influence on total profit and customer satisfaction. Moreover, the customer is a key factor in the logistics decision-making platform, not the provider’s investment in UAVs.", "keywords": [ "Customer satisfaction", "Revenue", "Maximization", "Profit (economics)", "Vehicle routing problem", "Business", "Operations research", "Computer science", "Process management", "Marketing", "Routing (electronic design automation)", "Engineering" ], "cited_by_count": 14, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2076-3417/12/12/6140/pdf?version=1655433261" }, { "id": "https://openalex.org/W4390504588", "doi": "10.4103/sja.sja_506_23", "title": "Drone a technological leap in health care delivery in distant and remote inaccessible areas: A narrative review", "year": 2024, "publication_date": "2024-01-01", "type": "review", "language": "en", "authors": [ "Sanjana Sharma", "Hunny Sharma" ], "first_author": "Sanjana Sharma", "venue": "Saudi Journal of Anaesthesia", "publisher": "Medknow", "abstract": "In developing countries, last-mile delivery of medical products is a challenge, especially in hilly and rural areas where there is no road connectivity. As helicopters or other air services are not affordable all the time, drones can be used for the supply of medical products. They are cost-effective as compared to other air or road transport. However, the carrying capacity of drone is less, it is not able to carry heavier payloads. Also, operating drones requires trained operators, and it is a new venture in a developing country so possibilities of confusion and lack of clarity on operating procedures are there. Drones are becoming increasingly reliable for the health care delivery. This narrative review explores the use of drones in healthcare delivery globally.", "keywords": [ "Drone", "Confusion", "CLARITY", "Medicine", "Narrative", "Health care", "Last mile (transportation)", "Developing country", "Medical emergency", "Mile", "Economic growth", "Geography" ], "cited_by_count": 34, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.4103/sja.sja_506_23" }, { "id": "https://openalex.org/W4387914459", "doi": "10.1109/codit58514.2023.10284110", "title": "Automatic Control of Drones' Missions in a Hybrid Truck-Drone Delivery System", "year": 2023, "publication_date": "2023-07-03", "type": "conference-paper", "language": "en", "authors": [ "Silvia Proia", "Graziana Cavone", "Giulia Tresca", "Raffaele Carli", "Mariagrazia Dotoli" ], "first_author": "Silvia Proia", "venue": null, "publisher": null, "abstract": "Last-mile delivery is one of the most discussed problems of the last decade due to the growing importance of e-commerce and the development of Industry 4.0. In particular, this problem regards the delivery of parcels from the warehouse to the final customers. In order to bring efficiency and innovation, in this paper a hybrid delivery architecture is considered, which takes advantage of the combined use of a drone and a truck to perform a sequence of pick-ups and deliveries, and the problem of optimal control of the drones' missions is addressed. The reference scenario is the smart city where the drone of the hybrid delivery architecture is in charge of three different pick-up and delivery missions: truck to point (i.e., pick-up from the truck and delivery to the customer), point to point (i.e., delivery to a customer and pick-up from the subsequent customer), and point to truck (i.e., reentry from a customer to the truck). From the control point of view, the drone is optimally guided in all the operating modes, i.e., ascent and descent from/to truck mode, free flight mode with/without payload, and descent for pick-up/delivery mode, by a receding horizon linear quadratic regulator (LQR), which is able to manage the drone in the dynamic landing on a movable vehicle and to allow the changing in real time of the landing point on the truck. Simulation results of the truck-drone delivery architecture are presented and discussed in detail, proving the effectiveness of the proposed method.", "keywords": [ "Drone", "Truck", "Payload (computing)", "Controller (irrigation)", "Computer science", "Engineering", "Point (geometry)", "Automotive engineering", "Computer security", "Network packet" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dx.doi.org/10.1109/codit58514.2023.10284110" }, { "id": "https://openalex.org/W2949390931", "doi": "10.2514/6.2019-2861", "title": "Wind and Weather Facility for Testing Free-Flying Drones", "year": 2019, "publication_date": "2019-06-14", "type": "conference-paper", "language": "en", "authors": [ "Flavio Noca", "Guillaume Catry", "Nicolas Bosson", "Luca Bardazzi", "Sergio Márquez", "Albéric Gros" ], "first_author": "Flavio Noca", "venue": "AIAA Aviation 2019 Forum", "publisher": null, "abstract": "Drones will soon fill our aerial ecosystem in the field of imaging/cartography, parcel delivery, and passenger transport, and will need to operate around the clock in arbitrary atmospheric conditions, especially in adverse weather conditions during emergency situations. Drones are much smaller than conventional aircraft and are thus more sensitive to weather conditions. Today, traditional drone testing techniques are of poor quality. Drones are either tested outdoors quite remote from the observer, in not well documented, uncontrolled and unpredictable weather conditions, or tightly strapped onto a support in a conventional wind tunnel with laminar and uniform wind flows, which are inadequate representations of atmospheric conditions relevant to drones. Existing tests, thus, entail a high risk of reaching false conclusions about drone performance. In order to resolve the issues associated with traditional wind tunnels or outdoor testing protocols, a real weather simulator for testing flying vehicles in various and controllable atmospheric conditions was developed. The wind and weather facility consists of an array of a large number of fans that may be arranged in various patterns on demand. It subjects drones to winds of variable intensity and direction as well as various weather conditions (such as rain, snow, hail, fog etc.) that reflect real world situations. These tests can rate drones according to their capacity in maintaining a proper flight attitude and tackling flight perturbations in an urban, countryside, or high altitude environment.", "keywords": [ "Drone", "Meteorology", "Aeronautics", "Weather modification", "Aerospace engineering", "Environmental science", "Computer science", "Engineering", "Physics" ], "cited_by_count": 25, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2514/6.2019-2861" }, { "id": "https://openalex.org/W4404882388", "doi": "10.1016/j.tre.2024.103877", "title": "Logic-based benders decomposition algorithm for robust parallel drone scheduling problem considering uncertain travel times for drones", "year": 2024, "publication_date": "2024-11-30", "type": "article", "language": "en", "authors": [ "Shakoor Barzanjeh", "Fardin Ahmadizar", "Jamal Arkat" ], "first_author": "Shakoor Barzanjeh", "venue": "Transportation Research Part E Logistics and Transportation Review", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Benders' decomposition", "Scheduling (production processes)", "Decomposition", "Computer science", "Mathematical optimization", "Algorithm", "Operations research", "Engineering", "Mathematics" ], "cited_by_count": 20, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.tre.2024.103877" }, { "id": "https://openalex.org/W4213439636", "doi": "10.1109/wsc52266.2021.9715506", "title": "Parcel Delivery For Smart Cities: A Synchronization Approach For Combined Truck-Drone-Street Robot Deliveries", "year": 2021, "publication_date": "2021-12-12", "type": "conference-paper", "language": "en", "authors": [ "Berry Gerrits", "Peter C. Schuur" ], "first_author": "Berry Gerrits", "venue": null, "publisher": null, "abstract": "The past decade saw many novel concepts for last-mile delivery. Particularly interesting is the concept in which trucks dispatch - from designated points - drones or robots that do the actual delivery. Generally, the role of the truck driver is underexposed. This paper proposes a last-mile delivery concept in which a truck carries a mixed fleet of drones and robots, and where the driver also has an active role in the delivery process. Our aim is to efficiently align the service time required by the truck driver with the back-and-forth delivery times of the drones and the robots. To this end, we design a street delivery model using continuous approximation. We evaluate our approach using a flexible and reusable simulation model to enable tactical decision-making for logistics service providers to determine which neighborhoods are suitable for drones, street robots, or combinations, in terms of makespan and energy consumption.", "keywords": [ "Drone", "Truck", "Robot", "Service (business)", "Computer science", "Synchronization (alternating current)", "Process (computing)", "Last mile (transportation)", "Transport engineering", "Mile", "Engineering", "Automotive engineering" ], "cited_by_count": 7, "is_retracted": false, "is_paratext": false, "open_access_url": "https://research.utwente.nl/en/publications/327fd0c6-b44e-4c3d-8a11-163617458975" }, { "id": "https://openalex.org/W4387643390", "doi": "10.1016/j.ejor.2023.10.015", "title": "Drone routing problem with swarm synchronization", "year": 2023, "publication_date": "2023-10-14", "type": "article", "language": "en", "authors": [ "Hongqi Li", "Feilong Wang", "Zhuopeng Zhan" ], "first_author": "Hongqi Li", "venue": "European Journal of Operational Research", "publisher": "Elsevier BV", "abstract": "The advantages of rotary-wing drone (RWD) delivery modes have already been delineated. However, single-unit RWDs do not completely solve real problems in last-mile parcel deliveries because of limited payload capacity and flight endurance. Currently, drone swarm technology is being rapidly developed. An RWD swarm can deliver heavy or multiple packages to customers; therefore, the RWD swarm strategy can address the payload capacity limitation of RWDs. Herein, an RWD delivery mode that involves dynamic swarms of RWDs in addition to single-unit RWDs is explored. The “dynamic” characteristic permits the RWD members in swarms to vary by coupling/decoupling operations at nodes. From the routing plan perspective, using RWD swarms for last-mile parcel deliveries is challenging; accordingly, we introduce a swarm synchronization mode that involves interactions among RWD routes. We formally define the drone routing problem with swarm synchronization (DRP-SS) and develop a mixed-integer linear programming model, which considers the decision on RWD swarms and multi trips. An adaptive large neighborhood search heuristic with specific operators is proposed. In the computational experiments, both small- and large-scale instances are used to validate the effectiveness of the mathematical formulation and the heuristic. Several managerial insights are obtained regarding the influence of detours, the utilization of RWD swarms, and the benefits of multi trips. The DRP-SS model and solution method can be used to estimate the performance of the selection of RWD swarms in practical situations.", "keywords": [ "Swarm behaviour", "Computer science", "Payload (computing)", "Mathematical optimization", "Drone", "Heuristic", "Scheduling (production processes)", "Synchronization (alternating current)", "Reservation", "Distributed computing", "Artificial intelligence", "Mathematics" ], "cited_by_count": 14, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.ejor.2023.10.015" }, { "id": "https://openalex.org/W4417531829", "doi": "10.1016/j.trd.2025.105176", "title": "Decarbonizing last-mile delivery: A life cycle comparison of low-altitude and traditional logistics", "year": 2025, "publication_date": "2025-12-21", "type": "article", "language": "en", "authors": [ "Jun-jie Dong", "Yuan-kai Huang" ], "first_author": "Jun-jie Dong", "venue": "Transportation Research Part D Transport and Environment", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Carbon footprint", "Payload (computing)", "Life-cycle assessment", "Ecological footprint", "Key (lock)", "Sustainability", "Drone", "Service (business)" ], "cited_by_count": 7, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.trd.2025.105176" }, { "id": "https://openalex.org/W3185300736", "doi": "10.1016/j.phycom.2021.101373", "title": "Smart delivery and retrieval of swab collection kit for COVID-19 test using autonomous Unmanned Aerial Vehicles", "year": 2021, "publication_date": "2021-07-05", "type": "article", "language": "en", "authors": [ "Fahad Saeed", "Amjad Mehmood", "Muhammad Faran Majeed", "Carsten R. Maple", "Khalid Saeed", "Muhammad Kashif Khattak", "Huihui Helen Wang", "Gregory Epiphaniou" ], "first_author": "Fahad Saeed", "venue": "Physical Communication", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Health care", "Computer science", "Pandemic", "Government (linguistics)", "Computer security", "Business", "Coronavirus disease 2019 (COVID-19)", "Test (biology)", "Medical emergency", "Medicine" ], "cited_by_count": 19, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/8256669" }, { "id": "https://openalex.org/W4405931602", "doi": "10.1016/j.jairtraman.2024.102736", "title": "MILP-based cost and time-competitive vehicle routing problem for last-mile delivery service using a swarm of UAVs and UGVs", "year": 2025, "publication_date": "2025-01-01", "type": "article", "language": "en", "authors": [ "Sunghun Jung" ], "first_author": "Sunghun Jung", "venue": "Journal of Air Transport Management", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Vehicle routing problem", "Last mile (transportation)", "Mile", "Routing (electronic design automation)", "Computer science", "Swarm behaviour", "Service (business)", "Mathematical optimization", "Operations research", "Transport engineering", "Engineering", "Business" ], "cited_by_count": 7, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.jairtraman.2024.102736" }, { "id": "https://openalex.org/W4388033706", "doi": "10.1038/s41598-023-45371-1", "title": "Robots at your doorstep: acceptance of near-future technologies for automated parcel delivery", "year": 2023, "publication_date": "2023-10-29", "type": "article", "language": "en", "authors": [ "Maher Said", "Spencer Aeschliman", "Amanda Stathopoulos" ], "first_author": "Maher Said", "venue": "Scientific Reports", "publisher": "Nature Portfolio", "abstract": "The logistics and delivery industry is undergoing a technology-driven transformation, with robotics, drones, and autonomous vehicles expected to play a key role in meeting the growing challenges of last-mile delivery. To understand the public acceptability of automated parcel delivery options, this U.S. study explores customer preferences for four innovations: autonomous vehicles, aerial drones, sidewalk robots, and bipedal robots. We use an Integrated Nested Choice and Correlated Latent Variable (INCLV) model to reveal substitution effects among automated delivery modes in a sample of U.S. respondents. The study finds that acceptance of automated delivery modes is strongly tied to shipment price and time, underscoring the importance of careful planning and incentives to maximize the trialability of innovative logistics options. Older individuals and those with concerns about package handling exhibit a lower preference for automated modes, while individuals with higher education and technology affinity exhibit greater acceptance. These findings provide valuable insights for logistics companies and retailers looking to introduce automation technologies in their last-mile delivery operations, emphasizing the need to tailor marketing and communication strategies to meet customer preferences. Additionally, providing information about appropriate package handling by automated technologies may alleviate concerns and increase the acceptance of these modes among all customer groups.", "keywords": [ "Automation", "Drone", "Robot", "Computer science", "Robotics", "Delivery Performance", "Emerging technologies", "Business", "Preference", "Marketing", "Process management", "Engineering management" ], "cited_by_count": 23, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.nature.com/articles/s41598-023-45371-1.pdf" }, { "id": "https://openalex.org/W4410987870", "doi": "10.1109/iscait64916.2025.11010309", "title": "Smart City Logistics: Leveraging AI for Last-Mile Delivery Efficiency", "year": 2025, "publication_date": "2025-03-21", "type": "conference-paper", "language": "en", "authors": [ "Junbo Wang" ], "first_author": "Junbo Wang", "venue": null, "publisher": null, "abstract": "Urban environments face significant challenges in last-mile delivery, which is a crucial component of logistics. To address these challenges, we introduce a framework called Smart City Logistics, utilizing artificial intelligence (AI) to enhance delivery efficiency. The framework optimizes routing and improves resource allocation by analyzing real-time data from multiple sources, including traffic patterns and user preferences. The system employs machine learning algorithms for predictive analytics, enabling it to adjust in response to evolving demand. Furthermore, we investigate the integration of AI with advanced infrastructure, such as smart traffic signals and autonomous delivery drones, fostering a more agile delivery network. Case studies across various cities reveal substantial improvements in both delivery speed and operational costs, showcasing AI’s transformative impact on logistics and its potential for sustainable urban delivery solutions.", "keywords": [ "Last mile (transportation)", "Mile", "Computer science", "Geography" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/iscait64916.2025.11010309" }, { "id": "https://openalex.org/W4321063507", "doi": "10.1109/ictmod55867.2022.10041873", "title": "Optimization of Hybrid Truck-Drone Delivery Systems: A bibliometric analysis", "year": 2022, "publication_date": "2022-11-23", "type": "conference-paper", "language": "en", "authors": [ "Batool Madani", "Malick Ndiaye" ], "first_author": "Batool Madani", "venue": null, "publisher": null, "abstract": "With the continued rise of e-commerce, logistics providers' responsibility for tackling the last-mile delivery (LMD) challenge grows. The LMD is the most polluting, costly, and inefficient component of the e-commerce supply chain. As a result, logistics firms are looking for innovative ways to improve delivery operations. To help reduce LMD challenges, a hybrid truck-drone delivery system (HTDDS) has emerged, combining drones with conventional delivery trucks. This approach has gotten a lot of attention in the literature because of its ability to reduce costs, minimize the environmental impact, and boost customer satisfaction. As a result, the purpose of this research is to conduct a rigorous bibliometric and network analysis of the HTDDS modeling and optimization problems. The research identifies emerging styles in article and journal performance, collaboration patterns, and main research areas.", "keywords": [ "Drone", "Truck", "Last mile (transportation)", "Supply chain", "Delivery Performance", "Computer science", "Component (thermodynamics)", "Business", "Operations research", "Process management", "Marketing", "Engineering" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/ictmod55867.2022.10041873" }, { "id": "https://openalex.org/W4211068785", "doi": "10.1109/ccnc49033.2022.9700528", "title": "4G/5G performance of a multi-RAT UAV for medical parcel delivery", "year": 2022, "publication_date": "2022-01-08", "type": "conference-paper", "language": "en", "authors": [ "Henrique Carvalho de Resende", "Joao Francisco Nunes Pinheiro", "Philippe Reiter", "Cristiano Bonato Both", "Johann M. Márquez-Barja" ], "first_author": "Henrique Carvalho de Resende", "venue": null, "publisher": null, "abstract": "Unmanned Aerial Vehicles (UAVs) are being developed and researched to be used in different areas and diverse use cases. The delivery of medical parcels is an emergency service that avails from the air traffic for the fast delivery of critical health content. Besides 5G networks being the target communication technology for the UAV parcel transport, the infrastructure is still not optimized for ground-level User Equipments (UEs). Using multiple Radio Access Technology (RAT) by UAVs is an alternative solution for scenarios where the 5G network is unstable or not yet fully deployed. In this paper, we present bench-marking of 5G and 4G networks on unideal radio scenarios to understand the real latency and throughput delivered by these technologies in worst-case scenarios. The results show that the 5G network can provide the necessary Quality of Service (QoS) for UAV operations even with Reference Signal Received Power (RSRP) in the order of -90. In comparison, the preliminary data for the 4G network validates that the 4G network could not provide the required latency.", "keywords": [ "Computer science", "Latency (audio)", "Quality of service", "Computer network", "Throughput", "Cellular network", "Real-time computing", "Telecommunications", "Wireless" ], "cited_by_count": 12, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/ccnc49033.2022.9700528" }, { "id": "https://openalex.org/W3016576502", "doi": "10.1007/978-3-030-44783-0_36", "title": "Drone Delivery Using Public Transport: An Agent-Based Modelling and Simulation Approach", "year": 2020, "publication_date": "2020-01-01", "type": "conference-paper", "language": "en", "authors": [ "Raheen Khalid", "Stanislav M. Chankov" ], "first_author": "Raheen Khalid", "venue": "Lecture notes in logistics", "publisher": "Springer Nature", "abstract": null, "keywords": [ "Drone", "Public transport", "Truck", "Last mile (transportation)", "Transport engineering", "Computer science", "Key (lock)", "Delivery Performance", "Food delivery", "Mode (computer interface)", "Engineering", "Operations research" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-030-44783-0_36" }, { "id": "https://openalex.org/W4410346172", "doi": "10.3390/s25103087", "title": "Research on Collaborative Delivery Path Planning for Trucks and Drones in Parcel Delivery", "year": 2025, "publication_date": "2025-05-13", "type": "article", "language": "en", "authors": [ "Ting Ting Fu", "Sheng Li", "Zhiguo Li" ], "first_author": "Ting Ting Fu", "venue": "Sensors", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "With the rapid development of e-commerce, the logistics industry faces multiple challenges, including high delivery costs, long delivery times, and a shortage of delivery personnel. Truck-drone collaborative delivery combines the high load capacity of trucks with the flexibility and speed of drones, offering an innovative and practical solution. This paper proposes the Truck-Drone Collaborative Delivery Routing Problem (TDCRPTW) and develops a multi-objective optimization model that minimizes delivery costs and maximizes time reliability under capacity and time window constraints in multi-truck, multi-drone scenarios. To solve the model, an innovative two-stage solution strategy that combines the adaptive k-means++ clustering algorithm with temperature-controlled memory simulated annealing (TCMSA) is proposed. The experimental results demonstrate that the proposed model reduces delivery costs by 10% to 50% and reduces delivery time by 15% to 40%, showcasing the superiority of the truck-drone collaborative delivery model. Moreover, the proposed algorithm demonstrates outstanding performance and reliability across multiple dimensions. Therefore, the proposed approach provides an efficient solution to the truck-drone collaborative delivery problem and offers valuable insights for enhancing the efficiency and reliability of e-commerce logistics systems.", "keywords": [ "Drone", "Truck", "Path (computing)", "Delivery system", "Transport engineering", "Computer science", "Engineering", "Simulation", "Aerospace engineering", "Computer network", "Biomedical engineering" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/1424-8220/25/10/3087/pdf?version=1747146885" }, { "id": "https://openalex.org/W4212962419", "doi": "10.5220/0010983100003117", "title": "Addressing the Challenges of Last-mile: The Drone Routing Problem with Shared Fulfillment Centers", "year": 2022, "publication_date": "2022-01-01", "type": "conference-paper", "language": "en", "authors": [ "Maria Elena Bruni", "Sara Khodaparasti" ], "first_author": "Maria Elena Bruni", "venue": null, "publisher": null, "abstract": null, "keywords": [ "Drone", "Mile", "Routing (electronic design automation)", "Last mile (transportation)", "Computer science", "Computer network", "Geography" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5220/0010983100003117" }, { "id": "https://openalex.org/W3016666592", "doi": "10.25394/pgs.12132867.v1", "title": "OPTIMIZATION MODELS AND ANALYSIS OF TRUCK-DRONE HYBRID ROUTING FOR LAST MILE DELIVERY", "year": 2020, "publication_date": "2020-04-17", "type": "dissertation", "language": "en", "authors": [ "Patchara Kitjacharoenchai" ], "first_author": "Patchara Kitjacharoenchai", "venue": "Purdue e-Pubs (Purdue University System)", "publisher": "Purdue University System", "abstract": "E-commerce and retail companies are seeking ways to cut delivery time and cost by exploring opportunities to use drones for making last-mile deliveries. In recent years, drone routing and scheduling have become a highly active area of research. This research addresses the concept of a truck-drone combined delivery by allowing autonomous drones to fly from delivery trucks, make deliveries, and fly to delivery trucks nearby. The first part of the research considers the synchronized truck drone routing model by allowing multiple drones to fly from any truck, serve customers and immediately return to any available truck or depot in the system. The goal is to find the optimal routes of both trucks and drones which minimize the arrival time of both trucks and drones at the depot after completing the deliveries. The problem can be solved by the formulated Mixed Integer Programming (MIP) for the small-size problems and our proposed heuristic called Adaptive Insertion Heuristics (ADI) which is based on the insertion technique for the medium/large-size problems. The second part of the research extends the first studied problem by allowing drones to serve multiple customers before merging with trucks as well as considering the capacity requirement for both vehicles. The problem is mathematically formulated and two efficient heuristic algorithms are designed to solve the large-size problems: Drone Truck Route Construction (DTRC) and Large Neighborhood Search (LNS). In the third study, the goal is to study the potential benefits of combining different types of fleet vehicles to deliver packages to the customers. Three types of vehicles are considered in this study including large drones, traditional trucks and hybrid trucks. The problem can be optimally solved by a mathematical formulation on a small scale. Two efficient metaheuristics based on Variable Neighborhood Search (VNS) and Large Neighborhood Search (LNS) are proposed to solve for approximate solutions of the large-size problems. A case study and numerical analysis demonstrate the better delivery time of the proposed model when compared with the delivery time of other delivery models with a single fleet type.", "keywords": [ "Drone", "Truck", "Heuristics", "Vehicle routing problem", "Integer programming", "Routing (electronic design automation)", "Heuristic", "Computer science", "Transport engineering", "Scheduling (production processes)", "Operations research", "Last mile (transportation)" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://figshare.com/articles/thesis/OPTIMIZATION_MODELS_AND_ANALYSIS_OF_TRUCK-DRONE_HYBRID_ROUTING_FOR_LAST_MILE_DELIVERY/12132867" }, { "id": "https://openalex.org/W4410204728", "doi": "10.1016/j.apm.2025.116175", "title": "A drone-assisted last-mile delivery framework for shipment prioritization in post-disaster and high demand periods", "year": 2025, "publication_date": "2025-05-08", "type": "article", "language": "en", "authors": [ "Omar Abou Kasm", "Meredith Raymer", "Ali H. Diabat" ], "first_author": "Omar Abou Kasm", "venue": "Applied Mathematical Modelling", "publisher": "Elsevier BV", "abstract": "The aftermath of a disaster can observe increased demands for online purchases and deliveries due to a decreased local supply. The increased demands may then induce delays in item delivery. Natural disasters may also result in epidemics and critical medical situations. Thus, the deliveries can include both life-essential items, such as medicine or medical equipment, and non-essential items, such as toys or decor products. Similar circumstances may be induced by other abnormal situations, such as pandemics or wars. It is then important to prioritize shipments to ensure early delivery of life-essential products at the expense of delaying non-essential items. In this work, we introduce a framework to prioritize shipments with application on drone-assisted last-mile deliveries. While the framework is general and can be used in different last-mile delivery types, the selection of drone-assisted deliveries is important for post-disaster situations to allow for contactless deliveries in case of epidemic outbreaks, and to reach destinations inaccessible by vehicles due to damaged roads and infrastructure. We consider different priority levels ranging from high priority to low priority items. High priority items must be delivered as soon as possible, medium priority items must be delivered within a certain time frame, and low priority items can be delivered after the high and medium priority conditions are met. We introduce a mixed integer program to model the drone-assisted last-mile deliveries with the prioritization scheme and propose a solution framework to systematically solve the problem. Finally, we illustrate the benefits of the model through numerical cases and simulations, and discuss its implications. • Drone-assisted last-mile deliveries in post-disaster and high-demand periods. • A prioritization scheme to ensure the prompt delivery of essential items. • A mixed integer program formulation is designed and presented. • A solution framework that aids in solving the problem is designed and presented. • Numerical cases and simulations are presented and discussed.", "keywords": [ "Drone", "Mile", "Last mile (transportation)", "Prioritization", "Business", "Aeronautics", "On demand", "Computer science", "Transport engineering", "Engineering", "Geography", "Process management" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.apm.2025.116175" }, { "id": "https://openalex.org/W7149469427", "doi": "10.1016/j.eiar.2026.108456", "title": "Sustainability assessment of unmanned aerial vehicles-based last-mile delivery: An integrated life cycle analysis", "year": 2026, "publication_date": "2026-04-04", "type": "article", "language": "en", "authors": [ "Xinhui Yang", "Yingying Ke", "Qianfa Gao", "Fu Gu" ], "first_author": "Xinhui Yang", "venue": "Environmental Impact Assessment Review", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Sustainability", "Life-cycle assessment", "Energy consumption", "Environmental impact assessment", "Consumption (sociology)", "Battery electric vehicle" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.eiar.2026.108456" }, { "id": "https://openalex.org/W2993476017", "doi": "10.1155/2019/6214359", "title": "A Contract Coordination Model of Dual-Channel Delivery between UAVs and Couriers Considering the Uncertainty of Delivery for Last Mile", "year": 2019, "publication_date": "2019-12-08", "type": "article", "language": "en", "authors": [ "Mingfei Chen", "Yanqiu Liu", "Yang Song", "Qi Sun" ], "first_author": "Mingfei Chen", "venue": "Discrete Dynamics in Nature and Society", "publisher": "Hindawi Publishing Corporation", "abstract": "For the traditional last-kilometer delivery, commodities are sent from the distribution center to the distribution transfer station, which are then delivered by the couriers from the distribution transfer station to the consumers. This single distribution channel cannot prioritize services for consumers closer to the distribution center, resulting in a waste of distribution resources. To deal with the last-kilometer delivery problem, this paper attempts to build a dual-channel distribution system consisting of UAVs and couriers. And the impact of the market allocation proportion on the expected profit of the last mile service through the two different distribution channels is discussed and analyzed. It is proved that the improved revenue sharing contract can realize the coordination of the dual-channel distribution between UAVs and the couriers. The numerical examples verify the effectiveness of the model and contract coordination. According to the research, first, with the increase in the uncertainties of the delivery quantity, the profit of the UAV channel shows a downward trend, while the profit of the courier channel remains basically the same. This is because the labor cost of the courier channel is relatively stable. Second, with the increase in the market allocation proportion of the last-kilometer delivery, the optimal delivery quantity of the UAV channel and the optimal order quantity of the courier channel are both increasing, while the overall profit of the last-kilometer is first rising and then decreasing. Third, the expected profit of the UAV channel and the courier channel can achieve Pareto improvement through the improved revenue sharing contract.", "keywords": [ "Kilometer", "Revenue", "Profit (economics)", "Channel (broadcasting)", "Last mile (transportation)", "Business", "Channel coordination", "Pareto principle", "Computer science", "Distribution (mathematics)", "Transport engineering", "Mile" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "https://downloads.hindawi.com/journals/ddns/2019/6214359.pdf" }, { "id": "https://openalex.org/W4417381766", "doi": "10.3390/drones9120868", "title": "GIS-Enabled Truck–Drone Hybrid Systems for Agricultural Last-Mile Delivery: A Multidisciplinary Review with Insights from a Rural Region", "year": 2025, "publication_date": "2025-12-16", "type": "article", "language": "en", "authors": [ "Imran Badshah", "Raj Bridgelall", "Emmanuel Anu Thompson" ], "first_author": "Imran Badshah", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Efficient last-mile delivery remains a critical challenge for rural agricultural logistics, globally, particularly in cold-climate regions with dispersed agricultural operations. Truck–drone hybrids can reduce delivery times but face payload limits, cold-weather battery loss, and beyond-visual-line-of-sight regulations. This review evaluates the potential of GIS-enabled truck–drone hybrid systems to overcome infrastructural, environmental, and operational barriers in such settings. This study uses the state of North Dakota (USA) as a representative case because of its cold climate, low density, and weak connectivity. These conditions require different routing and system assumptions than typical regions. The study conducts a systematic review of 81 high-quality publications. It identifies seven interconnected research domains: GIS analytics, truck–drone coordination, smart agriculture integration, rural implementation, sustainability assessment, strategic design, and data security. The findings stipulate that GIS enhances hybrid logistics through route optimization, launch site planning, and real-time monitoring. Additionally, this study emphasizes the rural, low-density context and identifies specific gaps related to cold-weather performance, restrictions to line-of-sight operations, and economic feasibility in ultra-low-density delivery networks. The study concludes with a roadmap for research and policy development to enable practical deployment in cold-climate agricultural regions.", "keywords": [ "Sustainability", "Context (archaeology)", "Agriculture", "Multidisciplinary approach", "Software deployment", "Hybrid system", "Work (physics)" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2504-446X/9/12/868/pdf?version=1765895761" }, { "id": "https://openalex.org/W3125951091", "doi": "10.1109/syscon48628.2021.9447142", "title": "Flying Robots for Safe and Efficient Parcel Delivery Within the COVID-19 Pandemic", "year": 2021, "publication_date": "2021-04-15", "type": "conference-paper", "language": "en", "authors": [ "Manuel Patchou", "Benjamin Sliwa", "Christian Wietfeld" ], "first_author": "Manuel Patchou", "venue": null, "publisher": null, "abstract": "The integration of small-scale Unmanned Aerial Vehicles (UAVs) into Intelligent Transportation Systems (ITSs) will empower novel smart-city applications and services. After the unforeseen outbreak of the COVID-19 pandemic, the public demand for delivery services has increased. Mobile robotic systems inherently offer the potential for minimizing the amount of direct human-to-human interactions within the parcel delivery process. In this paper, we apply a system-level perspective for identifying key challenges and promising solution approaches for modeling, analysis, and optimization of UAV-aided parcel delivery. We present a system-of-systems model for UAV-enhanced parcel delivery to cope with higher capacity requirements induced by the COVID-19. The proposed system-of-systems consists of various complex aspects such as assigning and distributing delivery jobs, establishing and maintaining reliable communication links between the vehicles, as well as path planning and mobility control. In a proof of concept evaluation, we simulate the logistics performance and network communications of a hybrid delivery fleet composed of ground and aerial vehicles operating under pandemic induced constraints, such as the prioritization of time-critical deliveries like medical goods. Results indicate that network requirements for reliable communications within the delivery fleet can be met by existing network communications approaches. Furthermore, gains in delivery capacity induced by drone usage and a trade-off caused by prioritizing time-critical goods, therefore, resulting in a penalty for regular deliveries, are observed.", "keywords": [ "Drone", "Software deployment", "Computer science", "Process (computing)", "Prioritization", "Coronavirus disease 2019 (COVID-19)", "Risk analysis (engineering)", "Business", "Process management" ], "cited_by_count": 23, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2101.07877" }, { "id": "https://openalex.org/W4285102862", "doi": "10.24200/sci.2022.57891.5460", "title": "A stochastic-fuzzy multi-objective model for the last-mile delivery problem using drones and ground vehicles, a case study", "year": 2022, "publication_date": "2022-05-11", "type": "article", "language": "en", "authors": [ "Kimiya Rahmani Mokarrari", "Shadi Shirazian", "Amir Aghsami", "Fariborz Jolai" ], "first_author": "Kimiya Rahmani Mokarrari", "venue": "Scientia Iranica", "publisher": "Sharif University of Technology", "abstract": "Drone delivery as a novel approach for parcel delivery has been under the focus of many scholars and practitioners. In this regard, this paper introduces a stochastic-fuzzy multi-objective optimization model for designing a last-mile delivery system with drones and ground vehicles. The first two objective functions aim to minimize the detrimental effects of the delivery system on the environment and the total costs. The last objective function maximized the system's reliability by considering the breakdown probability of both drones and ground vehicles. Then, AUGMECON2 is utilized as an exact method to solve the proposed model. besides determining the number of required drones and ground vehicles, the model indicates locations and capacities of facilities where vehicles start their one-to-one trips to meet the customer demands. The proposed model is then validated by applying it to a real case study of an e-commerce company in Karaj, Iran. The findings suggest that the system's total cost rises when the reliability increases and the environmental impacts decrease. Furthermore, when both drones and ground vehicles are considered for meeting the customer demands, the delivery system functions better in terms of costs, environmental impacts, and reliability than when only one mode of delivery is considered.", "keywords": [ "Drone", "Reliability (semiconductor)", "Fuzzy logic", "Computer science", "Mile", "Last mile (transportation)", "TRIPS architecture", "Operations research", "Transport engineering", "Reliability engineering", "Engineering", "Geography" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "http://scientiairanica.sharif.edu/article_22767_c25ba252bee5d69f1bfd61f75db7c3ec.pdf" }, { "id": "https://openalex.org/W4311111778", "doi": "10.1002/nav.22087", "title": "Branch‐price‐and‐cut for the truck–drone routing problem with time windows", "year": 2022, "publication_date": "2022-12-01", "type": "article", "language": "en", "authors": [ "Hongqi Li", "Feilong Wang" ], "first_author": "Hongqi Li", "venue": "Naval Research Logistics (NRL)", "publisher": "Wiley", "abstract": "Abstract Considering the important realistic benefits of drones combined with trucks for last‐mile parcel deliveries, we define the truck–drone routing problem with time windows (TDRP‐TW). The TDRP‐TW has the characteristics of time windows, synchronization en route, direct delivery, multiple trucks, and multiple drones carried by each truck. Customers covered by truck routes can be used as drone launch/retrieval locations, which are called satellites in this study. The synchronization en route enables drones to launch from trucks to return to paired trucks at nodes other than departure sites if necessary. We present an effective branch‐price‐and‐cut algorithm, in which a concept named candidate forward‐satellite (CFS) is introduced to manage the labeling challenge caused by the synchronization en route. In addition, the branch‐price‐and‐cut algorithm is combined with an adaptive large neighborhood search to obtain approximation solutions for large‐scale instances. In the computational experiments, instances with up to 50 customers are solved to optimality, and approximation solutions of large‐scale instances with 100 customers are presented.", "keywords": [ "Truck", "Drone", "Computer science", "Routing (electronic design automation)", "Synchronization (alternating current)", "Vehicle routing problem", "Scale (ratio)", "Real-time computing", "Operations research", "Computer network", "Engineering", "Automotive engineering" ], "cited_by_count": 21, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1002/nav.22087" }, { "id": "https://openalex.org/W4405636588", "doi": "10.1007/s10668-024-05867-w", "title": "Designing and analyzing drone-based city logistics solutions for Kochi using a Fuzzy Multi-Criteria decision making framework", "year": 2024, "publication_date": "2024-12-20", "type": "article", "language": "en", "authors": [ "B. Nila", "Dragan Stevan Pamucar", "Jagannath Roy" ], "first_author": "B. Nila", "venue": "Environment Development and Sustainability", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Drone", "Computer science", "Fuzzy logic", "Sustainable development", "Operations research", "City logistics", "Stakeholder", "Business", "Transport engineering", "Engineering", "Economics", "Artificial intelligence" ], "cited_by_count": 10, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/s10668-024-05867-w" }, { "id": "https://openalex.org/W4411785926", "doi": "10.3390/math13132143", "title": "PSL-IoD: PUF-Based Secure Last-Mile Drone Delivery in Supply Chain Management", "year": 2025, "publication_date": "2025-06-30", "type": "article", "language": "en", "authors": [ "Mohammad D. Alahmadi", "Ahmed Alzahrani", "Azeem Irshad", "Shehzad Ashraf Chaudhry" ], "first_author": "Mohammad D. Alahmadi", "venue": "Mathematics", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "The conventional supply chain management has undergone major advancements following IoT-enabled revolution. The IoT-enabled drones in particular have ignited much recent attention for package delivery in logistics. The service delivery paradigm in logistics has seen a surge in drone-assisted package deliveries and tracking. There have been a lot of recent research proposals on various aspects of last-mile delivery systems for drones in particular. Although drones have largely changed the logistics landscape, there are many concerns regarding security and privacy posed to drones due to their open and vulnerable nature. The security and privacy of involved stakeholders needs to be preserved across the whole chain of Supply Chain Management (SCM) till delivery. Many earlier studies addressed this concern, however with efficiency limitations. We propose a Physical Uncloneable Function (PUF)-based secure authentication protocol (PSL-IoD) using symmetric key operations for reliable last-mile drone delivery in SCM. PSL-IoD ensures mutual authenticity, forward secrecy, and privacy for the stakeholders. Moreover, it is protected from machine learning attacks and drone-related physical capture threats due to embedded PUF installations along with secure design of the protocol. The PSL-IoD is formally analyzed through rigorous security assessments based on the Real-or-Random (RoR) model. The PSL-IoD supports 26.71% of enhanced security traits compared to other comparative studies. The performance evaluation metrics exhibit convincing findings in terms of efficient computation and communication along with enhanced security features, making it viable for practical implementations.", "keywords": [ "Drone", "Mile", "Last mile (transportation)", "Computer security", "Computer science", "Business", "Geography", "Biology" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2227-7390/13/13/2143/pdf?version=1751290984" }, { "id": "https://openalex.org/W3088248088", "doi": "10.1109/ecbios50299.2020.9203635", "title": "Functional Deployment of Drone Logistics", "year": 2020, "publication_date": "2020-05-01", "type": "conference-paper", "language": "en", "authors": [ "Mei-Chiao Lai", "Dan Liu", "Wu-Der Tsay" ], "first_author": "Mei-Chiao Lai", "venue": null, "publisher": null, "abstract": "In the era of the Internet, the logistics industry continues to grow. Only fast, convenient, modern and automated logistics distribution can match the growing logistics demand. Drone logistics has these unique characteristics, which provides advantages for drone logistics distribution. Amazon, Wal-Mart, Google and many other companies, such as UPS, Wal-Mart, 7-Eleven, and Domino have tested drones for delivering small packages or groceries, and they will probably become common in the near future. The high benefits brought by drones are visible, and play an important role in the distribution of “last mile” in far and traffic inconvenient areas. Although public perceives benefits from the use of drones to perform a variety of functions throughout society. It is also clear that drones are perceived as presenting risks to personal safety, public safety, and privacy. Therefore, drone delivery providers need to understand potential consumers' demands to adopt drone delivery. However, a systematic study to characterize the public's demand of drone logistics, or to identify its broader concerns, has yet to be undertaken. Therefore, this paper used KANO model to investigate the customers demand for drone logistics service from 23 indicators of six dimensions in terms of national regulations, technical security, technical equipment, the efficiency of drone logistics, risk prevention and audience preference constraints.", "keywords": [ "Drone", "Business", "Software deployment", "Variety (cybernetics)", "Last mile (transportation)", "Service (business)", "Distribution (mathematics)", "Humanitarian Logistics", "Computer security", "Marketing", "Computer science", "Industrial organization" ], "cited_by_count": 12, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/ecbios50299.2020.9203635" }, { "id": "https://openalex.org/W2970900800", "doi": "10.1007/978-3-030-18500-8_3", "title": "Integration of Drones in Last-Mile Delivery: The Vehicle Routing Problem with Drones", "year": 2019, "publication_date": "2019-01-01", "type": "book-chapter", "language": "en", "authors": [ "Daniel Schermer" ], "first_author": "Daniel Schermer", "venue": "Operations research proceedings", "publisher": "Springer Nature", "abstract": null, "keywords": [ "Drone", "Solver", "Integer programming", "Vehicle routing problem", "Computer science", "Job shop scheduling", "Routing (electronic design automation)", "Set (abstract data type)", "Mathematical optimization", "Operations research", "Engineering", "Mathematics" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-030-18500-8_3" }, { "id": "https://openalex.org/W4399171374", "doi": "10.1016/j.tra.2024.104126", "title": "Drone logistics’ resilient development: impacts of consumer choice, competition, and regulation", "year": 2024, "publication_date": "2024-05-30", "type": "article", "language": "en", "authors": [ "Baozhuang Niu", "Jianhua Zhang", "Fengfeng Xie" ], "first_author": "Baozhuang Niu", "venue": "Transportation Research Part A Policy and Practice", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Competition (biology)", "Drone", "Business", "Industrial organization", "Economics", "Transport engineering", "Engineering" ], "cited_by_count": 24, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.tra.2024.104126" }, { "id": "https://openalex.org/W7117165054", "doi": "10.1016/j.cie.2025.111786", "title": "Multi-objective optimization of a truck–drone delivery system for fair and efficient humanitarian logistics under disruption and disinformation", "year": 2025, "publication_date": "2025-12-24", "type": "article", "language": "en", "authors": [ "Ramin Talebi Khameneh", "Nafiseh Ghorbani-Renani", "José Emmanuel Ramírez-Márquez" ], "first_author": "Ramin Talebi Khameneh", "venue": "Computers & Industrial Engineering", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Humanitarian aid", "Multi-objective optimization", "Stackelberg competition", "Humanitarian Logistics", "Pareto principle", "Sorting", "Drone", "Resilience (materials science)", "Genetic algorithm" ], "cited_by_count": 9, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.cie.2025.111786" }, { "id": "https://openalex.org/W3125516798", "doi": "10.1109/tvt.2021.3052551", "title": "Improving Throughput of 5G Cellular Networks via 3D Placement Optimization of Logistics Drones", "year": 2021, "publication_date": "2021-01-20", "type": "article", "language": "en", "authors": [ "Saeid Iranmanesh", "Forough Shirin Abkenar", "Raad Raad", "Abbas Jamalipour" ], "first_author": "Saeid Iranmanesh", "venue": "IEEE Transactions on Vehicular Technology", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "This paper addresses the congestion problem in cellular networks through the assistance of parcel delivery drones. Drones have the primary task of parcel delivery and a secondary task of acting as relay nodes (hop) to add extra capacity in a cellular network. Base transceiver stations in the cells can admit ground user equipment to communicate directly with the drone (device-to-device communications) through in-band signalling between them. This paper proposes a novel algorithm, called CARLO, which optimizes trajectory and altitude of the drones in order to deliver physical parcels, maximize network capacity and reduce network interference. Our simulation studies show that our solution improves the network throughput while the energy inefficiency of taking detour path to assist congested cells is kept to a minimum. Moreover, it is guaranteed that parcels are delivered before deadline.", "keywords": [ "Drone", "Relay", "Computer network", "Computer science", "Throughput", "Cellular network", "Base station", "Transceiver", "Inefficiency", "Distributed computing", "Wireless", "Telecommunications" ], "cited_by_count": 29, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/tvt.2021.3052551" }, { "id": "https://openalex.org/W2959212319", "doi": "10.3390/su11143871", "title": "Stochastic Drone Fleet Deployment and Planning Problem Considering Multiple-Type Delivery Service", "year": 2019, "publication_date": "2019-07-16", "type": "article", "language": "en", "authors": [ "Ming Liu", "Xin Liu", "Maoran Zhu", "Feifeng Zheng" ], "first_author": "Ming Liu", "venue": "Sustainability", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Drone delivery has a great potential to change the traditional parcel delivery service in consideration of cost reduction, resource conservation, and environmental protection. This paper introduces a novel drone fleet deployment and planning problem with uncertain delivery demand, where the delivery routes are fixed and couriers work in collaboration with drones to deliver surplus parcels with a relatively higher labor cost. The problem involves the following two-stage decision process: (i) The first stage determines the drone fleet deployment (i.e., the numbers and types of drones) and the drone delivery service module (i.e., the time segment between two consecutive departures) on a tactical level, and (ii) the second stage decides the numbers of parcels delivered by drones and couriers on an operational level. The purpose is to minimize the total cost, including (i) drone deployment and operating cost and (ii) expected labor cost. For the problem, a two-stage stochastic programming formulation is proposed. A classic sample average approximation method is first applied. To achieve computational efficiency, a hybrid genetic algorithm is further developed. The computational results show the efficiency of the proposed approaches.", "keywords": [ "Drone", "Software deployment", "Operations research", "Computer science", "Service (business)", "Stochastic programming", "Mathematical optimization", "Engineering", "Economics", "Mathematics" ], "cited_by_count": 26, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2071-1050/11/14/3871/pdf?version=1563498296" }, { "id": "https://openalex.org/W4200525079", "doi": "10.1007/s10479-021-04459-5", "title": "Joint optimisation of drone routing and battery wear for sustainable supply chain development: a mixed-integer programming model based on blockchain-enabled fleet sharing", "year": 2021, "publication_date": "2021-12-10", "type": "article", "language": "en", "authors": [ "Yang Xia", "Wenjia Zeng", "Xinjie Xing", "Yuanzhu Zhan", "Kim Hua Tan", "Ajay Kumar" ], "first_author": "Yang Xia", "venue": "Annals of Operations Research", "publisher": "Springer Science+Business Media", "abstract": "Abstract Alongside the rise of ‘last-mile’ delivery in contemporary urban logistics, drones have demonstrate commercial potential, given their outstanding triple-bottom-line performance. However, as a lithium-ion battery-powered device, drones’ social and environmental merits can be overturned by battery recycling and disposal. To maintain economic performance, yet minimise environmental negatives, fleet sharing is widely applied in the transportation field, with the aim of creating synergies within industry and increasing overall fleet use. However, if a sharing platform’s transparency is doubted, the sharing ability of the platform will be discounted. Known for its transparent and secure merits, blockchain technology provides new opportunities to improve existing sharing solutions. In particular, the decentralised structure and data encryption algorithm offered by blockchain allow every participant equal access to shared resources without undermining security issues. Therefore, this study explores the implementation of a blockchain-enabled fleet sharing solution to optimise drone operations, with consideration of battery wear and disposal effects. Unlike classical vehicle routing with fleet sharing problems, this research is more challenging, with multiple objectives (i.e., shortest path and fewest charging times), and considers different levels of sharing abilities. In this study, we propose a mixed-integer programming model to formulate the intended problem and solve the problem with a tailored branch-and-price algorithm. Through extensive experiments, the computational performance of our proposed solution is first articulated, and then the effectiveness of using blockchain to improve overall optimisation is reflected, and a series of critical influential factors with managerial significance are demonstrated.", "keywords": [ "Drone", "Computer science", "Integer programming", "Supply chain", "Blockchain", "Operations research", "Routing (electronic design automation)", "Computer security", "Business", "Computer network", "Engineering", "Marketing" ], "cited_by_count": 47, "is_retracted": false, "is_paratext": false, "open_access_url": "https://link.springer.com/content/pdf/10.1007/s10479-021-04459-5.pdf" }, { "id": "https://openalex.org/W4403880852", "doi": "10.1049/smc2.12093", "title": "Review of the application of drones for smart cities", "year": 2024, "publication_date": "2024-10-28", "type": "article", "language": "en", "authors": [ "Hana Důbravová", "Vladimír Bureš", "Lukáš Velfl" ], "first_author": "Hana Důbravová", "venue": "IET Smart Cities", "publisher": "Institution of Engineering and Technology", "abstract": "Abstract In an area of increasing digitalisation and rapid technological development, information management is becoming essential for the effective functioning of modern organisations and cities. One of the new trends is the gradual expansion and use of drones in parts of the smart cities concept. In security, drones are used to protect public order, monitor traffic in cities and municipalities, and control sub‐activities falling under the competence of police forces. In rescue work, drones allow us to efficiently and quickly manage accidental emergencies in hard‐to‐reach areas. At the same time, in the transport sector, drones have become an important component for the efficient delivery of parcels, reducing overall costs and time savings. This paper systematically overviews the importance of drones in various fields such as security, transportation, rescue operations or parcel delivery. Drones are becoming key elements for public order, crisis management and enhancing the quality of life of citizens in smart cities due to their capabilities of rapid response, monitoring hard‐to‐reach locations and autonomous data collection. Particular attention is then paid to comparing the use of drones in the Czech Republic and abroad and their benefits in the field of innovative technologies that help improve the safety and efficiency of urban services. The article also analyses the technical specifications of drones and their role in the public and private sectors, considering the legislative framework for their use. Overall, the article offers an overview of how drones contribute to integrating modern technologies into the daily functioning of smart cities and improving residents' quality of life.", "keywords": [ "Drone", "Computer science", "Aeronautics", "Computer security", "Engineering", "Biology" ], "cited_by_count": 17, "is_retracted": false, "is_paratext": false, "open_access_url": "https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/smc2.12093" }, { "id": "https://openalex.org/W4399724243", "doi": "10.1016/j.trc.2024.104698", "title": "Reliable truck-drone routing with dynamic synchronization: A high-dimensional network programming approach", "year": 2024, "publication_date": "2024-06-17", "type": "article", "language": "en", "authors": [ "Jiahao Xing", "Tong Guo", "Lu Carol Tong" ], "first_author": "Jiahao Xing", "venue": "Transportation Research Part C Emerging Technologies", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Computer science", "Truck", "Synchronization (alternating current)", "Routing (electronic design automation)", "Time synchronization", "Computer network", "Engineering", "Automotive engineering" ], "cited_by_count": 28, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.trc.2024.104698" }, { "id": "https://openalex.org/W3047683512", "doi": "10.1109/itec48692.2020.9161517", "title": "The Uncertainty of Autonomous Unmanned Aerial Vehicles’ Energy consumption", "year": 2020, "publication_date": "2020-06-01", "type": "conference-paper", "language": "en", "authors": [ "Mo. ElSayed", "Moataz Mohamed" ], "first_author": "Mo. ElSayed", "venue": null, "publisher": null, "abstract": "Battery-operated autonomous unmanned aerial vehicles (UAVs) promise enormous potential for delivery electrification. However, airspace regulations have a significant impact on the determination of UAV energy consumption. This paper investigates the impact of several degrees of international UAV policies and regulations on the energy consumption of autonomous UAV last-mile delivery operations in rural and urban contexts. Simulations based on quadcopter kinematics and Newton-Euler derived differential equations for control are performed for three degrees of regulation strictness. Energy consumption based on the brushless DC rotors' thrust and angular acceleration shows up to a 400% increase in energy demand due to flight path changes.", "keywords": [ "Energy consumption", "Quadcopter", "Electrification", "Thrust", "Drone", "Automotive engineering", "Kinematics", "Computer science", "Aerospace engineering", "Electricity", "Engineering", "Electrical engineering" ], "cited_by_count": 9, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/itec48692.2020.9161517" }, { "id": "https://openalex.org/W4291940579", "doi": "10.3390/su14169978", "title": "A Variable Neighborhood Descent Matheuristic for the Drone Routing Problem with Beehives Sharing", "year": 2022, "publication_date": "2022-08-12", "type": "article", "language": "en", "authors": [ "Maria Elena Bruni", "Sara Khodaparasti" ], "first_author": "Maria Elena Bruni", "venue": "Sustainability", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "In contemporary urban logistics, drones will become a preferred transportation mode for last-mile deliveries, as they have shown commercial potential and triple-bottom-line performance. Drones, in fact, address many challenges related to congestion and emissions and can streamline the last leg of the supply chain, while maintaining economic performance. Despite the common conviction that drones will reshape the future of deliveries, numerous hurdles prevent practical implementation of this futuristic vision. The sharing economy, referred to as a collaborative business model that foster sharing, exchanging and renting resources, could lead to operational improvements and enhance the cost control ability and the flexibility of companies using drones. For instance, the Amazon patent for drone beehives, which are fulfilment centers where drones can be restocked before flying out again for another delivery, could be established as a shared delivery systems where different freight carriers jointly deliver goods to customers. Only a few studies have addressed the problem of operating such facilities providing services to retail companies. In this paper, we formulate the problem as a deterministic location-routing model and derive its robust counterpart under the travel time uncertainty. To tackle the computational complexity of the model caused by the non-linear energy consumption rates in drone battery, we propose a tailored matheuristic combining variable neighborhood descent with a cut generation approach. The computational experiments show the efficiency of the solution approach especially compared to the Gurobi solver.", "keywords": [ "Drone", "Computer science", "Variable (mathematics)", "Operations research", "Flexibility (engineering)", "Sharing economy", "Economics", "Engineering" ], "cited_by_count": 21, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2071-1050/14/16/9978/pdf?version=1669295644" }, { "id": "https://openalex.org/W4403848911", "doi": "10.2139/ssrn.5002638", "title": "Beyond the Last-Mile: Environmental and Economic Assessment of the Upcoming Drone Takeaway Delivery System", "year": 2024, "publication_date": "2024-01-01", "type": "preprint", "language": "en", "authors": [ "Zhiwei Li", "Siqi Zhou", "Bohan Wang", "Tingxi Zhang", "Shuang Guo" ], "first_author": "Zhiwei Li", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Drone", "Mile", "Last mile (transportation)", "Environmental planning", "Environmental resource management", "Environmental science", "Geography", "Business" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.5002638" }, { "id": "https://openalex.org/W4283701116", "doi": "10.1080/19427867.2022.2082006", "title": "Collaborative traveling salesman problem with ground vehicle as a charger for unmanned aerial vehicle", "year": 2022, "publication_date": "2022-06-28", "type": "article", "language": "en", "authors": [ "Hyungjoo Cha", "DongKyun Kim", "Joonyup Eun", "Taesu Cheong" ], "first_author": "Hyungjoo Cha", "venue": "Transportation Letters", "publisher": "Taylor & Francis", "abstract": "Innovation in logistics industry is now implicit, and the use of unmanned aerial vehicles is at the center of logistics innovation. To effectively utilize unmanned aerial vehicles for last-mile delivery, collaborative delivery using ground vehicles and unmanned aerial vehicles has recently been investigated. However, previous studies neglected proper management of batteries, assuming their constant replacement whenever unmanned aerial vehicles land, and hence numerous batteries are required for unmanned aerial vehicles. Given this research gap, we present a new routing model for collaborative delivery wherein an unmanned aerial vehicle uses a ground vehicle as a power source. A mathematical formulation is presented, and noticing the complexity, a heuristic algorithm is developed. We present a case study to verify the operational efficiency of the model. We test the performance of the heuristic and draw insights based on geographical locations of customers via computational experiments.", "keywords": [ "Heuristic", "Travelling salesman problem", "Drone", "Computer science", "Vehicle routing problem", "Routing (electronic design automation)", "Simulation", "Artificial intelligence", "Embedded system" ], "cited_by_count": 14, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1080/19427867.2022.2082006" }, { "id": "https://openalex.org/W3146811573", "doi": "10.36227/techrxiv.14347655.v1", "title": "A Roadmap to Nextgen Aircraft Control for Last Mile Delivery Drones", "year": 2021, "publication_date": "2021-04-06", "type": "preprint", "language": "en", "authors": [ "Cesar Augusto Cavalheiro Marcondes", "Denis Silva Loubach", "Elton Felipe Sbruzzi", "Filipe Alves Neto Verri", "Johnny Marques", "Lourenço Alves Pereira", "Marcos R. O. A. Máximo", "Vitor Curtis" ], "first_author": "Cesar Augusto Cavalheiro Marcondes", "venue": null, "publisher": null, "abstract": "According to recent studies, unmanned aerial vehicles (UAV) can play a game-changing part in terms of cost reduction and speed increase to address the last-mile delivery (LMD) problem and also to attend emergencies. Last-mile delivery services are getting more and more relevant, especially when in times where social distance is required. Given this scenario, our paper introduces a cyber-physical (CPS) system roadmap propose applicable for last-mile delivery drones. The proposed CPS guidelines are based on the concept of system of systems to enable an emerging behavior towards smart cities’ governance. In this paper, we also discuss topics from air space control and reservation throughout communication infrastructure and decentralized control supported on a blockchain.", "keywords": [ "Drone", "Mile", "Last mile (transportation)", "Reservation", "Computer science", "Air traffic control", "Aeronautics", "Control (management)", "Command and control", "Resilience (materials science)", "Telecommunications", "Computer security" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.techrxiv.org/articles/preprint/A_Roadmap_to_Nextgen_Aircraft_Control_for_Last_Mile_Delivery_Drones/14347655/1/files/27386258.pdf" }, { "id": "https://openalex.org/W4415284787", "doi": "10.1109/worldsuas66815.2025.11199241", "title": "Drone Swarm Intelligence: AI-Driven Autonomous Coordination for Aerial Applications", "year": 2025, "publication_date": "2025-08-22", "type": "conference-paper", "language": null, "authors": [ "Anurag Shrivastava", "RVS Praveen", "Bangalore N. Gangadhar", "Hari Krishna Vemuri", "Shaik Rasool", "Ramy Riad Al–Fatlawy" ], "first_author": "Anurag Shrivastava", "venue": null, "publisher": null, "abstract": "Autonomous coordination, adaptiveness, and efficiency in dynamic environments—all made possible by artificial intelligence (AI)—are transforming aerial applications through the use of drone swarm intelligence. Swarm intelligence enables decentralized control, allowing drones to work as a cohesive unit, enhancing search and rescue, environmental monitoring, military operations, and logistics applications through real-time data analysis, inter-drone communication, and distributed processing. Drone swarms can be used across different fields such as, disaster response, environmental monitoring, precision agriculture, military reconnaissance and urban logistics. We also identify challenges including communication latency, energy limitations, and security vulnerabilities as well as discuss how robust AI models and edge computing can increase swarm autonomy. What the Next Generation of AI-Powered Drone Swarms Means for Airmen.", "keywords": [ "Drone", "Swarm behaviour", "Swarm robotics", "Swarm intelligence", "Search and rescue", "Enhanced Data Rates for GSM Evolution" ], "cited_by_count": 25, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/worldsuas66815.2025.11199241" }, { "id": "https://openalex.org/W4366598904", "doi": "10.1186/s42252-023-00040-4", "title": "Evaluating the sustainability of lightweight drones for delivery: towards a suitable methodology for assessment", "year": 2023, "publication_date": "2023-04-20", "type": "article", "language": "en", "authors": [ "Sinéad Mitchell", "Juliana Steinbach", "Tomás Flanagan", "Pouyan Ghabezi", "Noel M. Harrison", "Simon O’Reilly", "Stephen Killian", "William G. Finnegan" ], "first_author": "Sinéad Mitchell", "venue": "Functional Composite Materials", "publisher": "Springer Nature", "abstract": "Drone technology is widely available and is rapidly becoming a crucial instrument in the functions of businesses and government agencies worldwide. The demand for delivery services is accelerating particularly since the Covid-19 pandemic. Both companies and customers want these services to be efficient, timely, safe, and sustainable, but these are major challenges. Last-mile delivery by lightweight short-range drones has the potential to address these challenges. However, there is a lack of consistency and transparency in assessing and reporting the sustainability of last-mile delivery services and drones. This paper critically reviews published papers on Life Cycle Assessments of drones to date. The study reveals a lack of comprehensive studies, and a need to examine composite and battery manufacturing developments and provides key considerations for future study development.", "keywords": [ "Drone", "Sustainability", "Computer science", "Biology" ], "cited_by_count": 20, "is_retracted": false, "is_paratext": false, "open_access_url": "https://functionalcompositematerials.springeropen.com/counter/pdf/10.1186/s42252-023-00040-4" }, { "id": "https://openalex.org/W7172112131", "doi": "10.1108/ijpdlm-05-2025-0232", "title": "Exploring opportunities and challenges for aerial drones at the interface of warehouse and last-mile delivery operations", "year": 2026, "publication_date": "2026-08-01", "type": "article", "language": "en", "authors": [ "Mujtaba Hussain", "Rico Merkert", "Michiel C.J. Bliemer" ], "first_author": "Mujtaba Hussain", "venue": "International Journal of Physical Distribution & Logistics Management", "publisher": "Emerald Publishing Limited", "abstract": "Purpose This paper investigates the technological, organizational and environmental factors that shape the adoption of aerial drone operations at the warehouse–last-mile delivery interface, addressing benefits and barriers of drone adoption, most suitable functional areas, stakeholder collaboration and support of a low-altitude airspace management (LAAM) system for successful integration. Design/methodology/approach Drawing on the technology–organization–environment (TOE) theoretical framework, we employ a qualitative research design, analyzing data collected during semi-structured interviews with 38 managers representing warehousing companies and drone operators around the world. Findings Successful drone integration in retail logistics depends not only on one firm's technology, organization and external context, but also on a collaborative effort across the supply chain. While drones offer clear benefits in logistics, full-scale adoption requires inter-organizational ecosystem coordination (and, depending on the governance structure, also collaboration) to overcome challenges such as infrastructural readiness and regulatory coordination. Practical implications Examining operations at the interface of warehouse operations and last-mile delivery yields management insights that can drive effective strategies for implementing drones. Building on the TOE framework, we conceptualize inter-organizational ecosystem coordination as a critical enabler of drone integration and innovation adoption in logistics. Based on our findings, we suggest that drone operators should integrate more deeply into logistics services rather than focusing solely on delivery, and warehouses should explore current uses for drone technology rather than simply considering future applications. We identify three governance models for integrating drones into logistics ecosystems: vertical integration, inter-organizational collaboration and partial adoption with intra-organizational collaboration. Originality/value We contribute to the evolving understanding of technology adoption and drone integration at the warehouse–last-mile delivery interface by examining managers' perspectives for the first time. Extending the application of TOE from the firm level to inter-organizational coordination better captures infrastructural enablers of innovation adoption and active collaboration between organizations in logistics operations.", "keywords": [ "Drone", "Enabling", "Interface (matter)", "Corporate governance", "Stakeholder", "Supply chain", "Stakeholder engagement" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1108/ijpdlm-05-2025-0232" }, { "id": "https://openalex.org/W7142667653", "doi": "10.1016/j.tre.2026.104817", "title": "Last-mile delivery with drone resupply at transshipment platforms", "year": 2026, "publication_date": "2026-03-29", "type": "article", "language": "en", "authors": [ "Juan C. Pina-Pardo", "Matthias Winkenbach" ], "first_author": "Juan C. Pina-Pardo", "venue": "Transportation Research Part E Logistics and Transportation Review", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Transshipment (information security)", "Drone", "Truck", "Exploit", "TRIPS architecture", "Vehicle routing problem" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.tre.2026.104817" }, { "id": "https://openalex.org/W2949957913", "doi": "10.1007/978-3-030-21803-4_101", "title": "A Genetic Algorithm for Solving the Truck-Drone-ATV Routing Problem", "year": 2019, "publication_date": "2019-06-14", "type": "conference-paper", "language": "en", "authors": [ "Mahdi Moeini", "Hagen Salewski" ], "first_author": "Mahdi Moeini", "venue": "Advances in intelligent systems and computing", "publisher": "Springer Nature", "abstract": null, "keywords": [ "Drone", "Truck", "Grid", "Computer science", "Vehicle routing problem", "Merge (version control)", "Routing (electronic design automation)", "Engineering", "Operations research", "Computer network", "Geography", "Aerospace engineering" ], "cited_by_count": 20, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-030-21803-4_101" }, { "id": "https://openalex.org/W2968343056", "doi": null, "title": "Introduction of drones in the last-mile logistic process of medical product delivery: A feasibility assessment applied to the case study of Benu 't Slag", "year": 2019, "publication_date": "2019-01-01", "type": "dissertation", "language": "en", "authors": [ "Irene Zubin" ], "first_author": "Irene Zubin", "venue": "Research Repository (Delft University of Technology)", "publisher": "Delft University of Technology", "abstract": "The term last-mile delivery refers to the final leg of a business-to-customer service, in which a product is shipped from a depot to a final destination point by means of land transportation, such as vans and small trucks. Although they provide a common and easy way to consign products, companies are striving for new transport technologies to reduce congestion problems, infrastructure limitations and air pollution. Recently, a valid alternative to road-bounded vehicles that is has gained attention is the adoption of drones in the delivery fleet. Drone applications range from military training, surveillance, path recognition and shipment of perishable products in emergency situations. Research on drones as delivery vehicles is still on its early stages, with some practical trials carried out by leader companies such as Google and Amazon. However, the application of drones in the pharmaceutical sector, and specifically for home deliveries of medical products, has not been investigated yet. To gain new insights into the feasibility of introducing drones in the delivery fleet composition, drone applications were studied for the delivery operations of the pharmacy BENU t Slag, in Rotterdam. Two scenario alternatives were tested using the Vehicle Routing Problem (VRP) formulation and a Large- scale Neighbourhood Search (LNS) algorithm was implemented to estimate the performance indicators associated with each scenario. Performances were then analysed through a comparative analysis. Conclusively, indicators were found to improve the delivery performance when drones are included in the fleet composition, with gains in environmental aspects, service time and delivery costs. Results provide important information for further research on the implications of using drones in the pharmaceutical sector. Moreover, results are also useful for BENU t Slag, providing a valid feasibility assessment of using drones for their home delivery operations.", "keywords": [ "Drone", "Truck", "Transport engineering", "Service (business)", "Last mile (transportation)", "Product (mathematics)", "Operations research", "Business", "Computer science", "Operations management", "Engineering", "Marketing" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "http://resolver.tudelft.nl/uuid:0db692ce-5685-4499-bf84-d570544de795" }, { "id": "https://openalex.org/W3138818188", "doi": null, "title": "Optimization of last-mile deliveries with synchronous truck and drones", "year": 2020, "publication_date": "2020-07-01", "type": "dissertation", "language": "en", "authors": [ "Aysu Özel" ], "first_author": "Aysu Özel", "venue": "Bilkent University Institutional Repository (Bilkent University)", "publisher": "Bilkent University", "abstract": "Cataloged from PDF version of article.", "keywords": [ "Mile", "Drone", "Truck", "Aeronautics", "Last mile (transportation)", "Engineering", "Computer science", "Transport engineering", "Automotive engineering", "Geography", "Geodesy", "Biology" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "http://hdl.handle.net/11693/53949" }, { "id": "https://openalex.org/W4323906519", "doi": "10.1007/978-3-031-23721-8_98", "title": "The Use of Drones in City Logistics—A Case Study Application", "year": 2023, "publication_date": "2023-01-01", "type": "conference-paper", "language": "en", "authors": [ "Theonymphi Xydianou", "Eftihia Nathanail" ], "first_author": "Theonymphi Xydianou", "venue": "Lecture notes in intelligent transportation and infrastructure", "publisher": "Springer International Publishing", "abstract": null, "keywords": [ "Drone", "Business", "Aeronautics", "Engineering", "Biology" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-031-23721-8_98" }, { "id": "https://openalex.org/W4220945331", "doi": "10.1541/ieejeiss.142.499", "title": "Drone Delivery Scheduling by a Genetic Algorithm: Simultaneous Optimization for Multiple Different Numbers of Drones", "year": 2022, "publication_date": "2022-03-31", "type": "article", "language": "en", "authors": [ "Yohei Hazama", "Hitoshi Iima", "Yoshiyuki Karuno" ], "first_author": "Yohei Hazama", "venue": "IEEJ Transactions on Electronics Information and Systems", "publisher": null, "abstract": "Drones, or unmanned aerial vehicles (UAVs), have the potential to considerably reduce the cost and time required to deliver parcels. This paper addresses a parcel delivery scheduling problem, which is to find the optimal assignment of customers to the drones and their takeoff points. In this problem, depending on the number of the drones, the optimal solution changes. We propose a genetic algorithm to find near-optimal solutions simultaneously for multiple different numbers of the drones. The performance of the proposed method is evaluated through conducting experiments.", "keywords": [ "Drone", "Takeoff", "Scheduling (production processes)", "Computer science", "Genetic algorithm", "Mathematical optimization", "Job shop scheduling", "Real-time computing", "Engineering", "Mathematics", "Aerospace engineering", "Computer network" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1541/ieejeiss.142.499" }, { "id": "https://openalex.org/W4386837110", "doi": "10.3390/drones7090582", "title": "An Operational Capacity Assessment Method for an Urban Low-Altitude Unmanned Aerial Vehicle Logistics Route Network", "year": 2023, "publication_date": "2023-09-15", "type": "article", "language": "en", "authors": [ "Yi Jia", "Honghai Zhang", "Fei Wang", "Changyuan Ning", "Hao Liu", "Gang Zhong" ], "first_author": "Yi Jia", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "The Federal Aviation Administration introduced the concept of urban air mobility (UAM), a new three-dimensional transport system that operates with a fusion of manned/unmanned aerial vehicles on an urban or intercity scale. The rapid development of UAM has brought innovation and dynamism to many industries, especially in the field of logistics. Various types of unmanned aerial vehicles (UAVs) for use in transport logistics are being designed and produced. UAV logistics refers to the use of UAVs, usually carrying goods and parcels, to achieve route planning, identify risk perception, facilitate parcel delivery, and carry out other functions. This research provides a method for assessing the operational capacity of a UAV logistics route network. The concept of “logistics UAV route network operation capacity” is defined, and a bi-objective optimization model for assessing the route network’s operating capacity is developed. The first objective is to maximize the number of UAV logistics delivery plans that can be executed in a fixed operation time. The second objective is to minimize the total operational impedance value in a fixed operation time. To solve the bi-objective optimization model, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is utilized. A UAV logistics route network with 62 nodes is developed to assess the rationale and validity of the proposed concept. The experiments show that with an increase in operation time, the route network’s optimal operational capacity gradually increases, the convergence speed of the algorithm slows down, and the optimization magnitude gradually reduces. Two key parameters—operational safety interval and flight speed—are further analyzed in the experiments. According to the experiments, as the safety interval increases, the route network’s average operational capacity steadily diminishes, as does its sensitivity to the safety interval. The average operational capacity steadily rose with the rise in flight speed, especially when the UAV logistics flight speed was between 10 m/s and 10.5 m/s. In that range, the operational capacity of the route network was substantially impacted by the flight speed.", "keywords": [ "Computer science", "Transport engineering", "Dynamism", "Key (lock)", "Sorting", "Operations research", "Engineering" ], "cited_by_count": 18, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2504-446X/7/9/582/pdf?version=1694776013" }, { "id": "https://openalex.org/W4388774605", "doi": "10.1080/23249935.2023.2282963", "title": "Coordinated logistics with trucks and drones for premium delivery", "year": 2023, "publication_date": "2023-11-17", "type": "article", "language": "en", "authors": [ "Dongwook Kim", "Chang Seong Ko", "Ilkyeong Moon" ], "first_author": "Dongwook Kim", "venue": "Transportmetrica A Transport Science", "publisher": "Taylor & Francis", "abstract": "Last-mile delivery, the final stage of the delivery process before a package arrives at a customer's address, has emerged as an important business opportunity for inland transportation. As people's perception of last-mile delivery has changed, more customers are using premium delivery services to get the delivery at a specific time rather than just pursuing free shipping. In order to satisfy the new trend of logistics, we propose a delivery system in which several drones and trucks work together to provide service to customers within given time windows. In addition, pair constraints, which considered a truck and a drone as a pair, are relaxed to determine more flexible delivery plans. A three-stage savings-based heuristic procedure was also developed based on the concept of savings to determine the operations of trucks and drones in real-world settings. We found that the drone efficiently responds to urgent delivery requests from customers and that a delivery system that utilises both trucks and drones provides substantial benefits.", "keywords": [ "Drone", "Truck", "Last mile (transportation)", "Transport engineering", "Service (business)", "Order (exchange)", "Process (computing)", "Business", "Computer science", "Marketing", "Engineering", "Mile" ], "cited_by_count": 15, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1080/23249935.2023.2282963" }, { "id": "https://openalex.org/W4213160096", "doi": "10.1093/imaman/dpab039", "title": "An efficient routing heuristic for a drone-assisted delivery problem", "year": 2021, "publication_date": "2021-10-27", "type": "article", "language": "en", "authors": [ "Abhishake Kundu", "Ricardo Gatica Escobar", "Timothy I. Matis" ], "first_author": "Abhishake Kundu", "venue": "IMA Journal of Management Mathematics", "publisher": "Oxford University Press", "abstract": "Abstract In this paper, we present a routing heuristic for the Flying Sidekick Traveling Salesman Problem. This problem combines a single truck with a single drone. The drone may be launched from the truck from a customer location and can make a single package delivery before being recovered by the truck. While the drone is in flight, the truck can deliver packages independently. The problem is one of simultaneously assigning the customers to each vehicle and determining the synchronized routes for both vehicles to satisfy all customer demands in the shortest time possible. The problem is NP-hard. Our main contribution lies in developing a novel split algorithm that utilizes a shortest path approach for determining the optimal routing solution to a given order of customer locations. This split algorithm has polynomial complexity. We develop a heuristic solution to the problem by combining the split algorithm with local search techniques. This overall heuristic is shown to be accurate and computationally scalable to large, realistically sized problems applicable in last-mile logistics.", "keywords": [ "Drone", "Travelling salesman problem", "Computer science", "Heuristic", "Truck", "Routing (electronic design automation)", "Vehicle routing problem", "Mathematical optimization", "Shortest path problem", "Scalability", "Algorithm", "Mathematics" ], "cited_by_count": 18, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1093/imaman/dpab039" }, { "id": "https://openalex.org/W4417050610", "doi": "10.1016/j.cie.2025.111735", "title": "Battery drones versus hydrogen fuel cell drones for last-mile delivery", "year": 2025, "publication_date": "2025-12-05", "type": "article", "language": "en", "authors": [ "Jayasurya Ragupati", "Srinjoy Chatterjee", "Byeongmok Kim", "Seokcheon Lee" ], "first_author": "Jayasurya Ragupati", "venue": "Computers & Industrial Engineering", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Battery (electricity)", "Fuel cells", "Hydrogen", "Fuel tank" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.cie.2025.111735" }, { "id": "https://openalex.org/W3135977922", "doi": null, "title": "Simulation Test Bed for Drone-Supported Logistics Systems", "year": 2019, "publication_date": "2019-01-01", "type": "article", "language": "en", "authors": [ "Brent A. McCunney", "Kristof P. Van Cauwenberghe" ], "first_author": "Brent A. McCunney", "venue": "DSpace@MIT (Massachusetts Institute of Technology)", "publisher": "Massachusetts Institute of Technology", "abstract": "Same-day delivery is an increasingly relevant logistics service because of a continued boom in e- commerce. As customer orders are submitted dynamically during the day, companies need to dispatch vehicles from distribution centers to fulfill these orders as they come in. Given their advantages over conventional vehicles such as direct flight, no road traffic, and no driver/operator requirements, autonomous drones have recently been proposed for last mile package delivery. This study examines in what situations drones could be used to resupply trucks to reduce the time and/or cost of delivery. Drones can be used to dispatch packages from the distribution center to transshipment points where trucks can pick up packages instead of returning to the distribution center. The use of drones requires a new transportation network and routing. A simulation using SIMIO was developed to assess the impact of using drones to resupply trucks through transshipment points. Both a subset of the city of Boston and a portion of the rural area around Pittsfield, MA were used with package delivery orders arriving throughout the simulated time. Through dynamic dispatching for same-day delivery, orders were delivered significantly faster when transshipment points were resupplied by drones than in conventional last mile delivery. In the Pittsfield region analysis, the baseline with one transshipment point was 36% faster than conventional package delivery. The total truck distance traveled while delivering packages was also reduced by 24% or on average 80km per 8-hour work day. In the Boston case study, the baseline scenario of 4 transshipment points was 66% faster (2 hours) at delivering packages than conventional package delivery. Four transshipment points also resulted in a 10% or 60km a day less distance traveled for the trucks. Our research indicates that last mile package delivery companies can use drones and transshipment points to reduce package delivery time as well as truck travel distance.", "keywords": [ "Drone", "Test (biology)", "Aeronautics", "Computer science", "Engineering", "Geology" ], "cited_by_count": 16, "is_retracted": false, "is_paratext": false, "open_access_url": "https://hdl.handle.net/1721.1/121281" }, { "id": "https://openalex.org/W4414768492", "doi": "10.3390/en18195242", "title": "AI-Enhanced Eco-Efficient UAV Design for Sustainable Urban Logistics: Integration of Embedded Intelligence and Renewable Energy Systems", "year": 2025, "publication_date": "2025-10-02", "type": "article", "language": "en", "authors": [ "Luigi Bibbò", "Filippo Laganà", "Giuliana Bilotta", "Giuseppe Maria Meduri", "Giovanni Angiulli", "Francesco Cotroneo" ], "first_author": "Luigi Bibbò", "venue": "Energies", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "The increasing use of UAVs has reshaped urban logistics, enabling sustainable alternatives to traditional deliveries. To address critical issues inherent in the system, the proposed study presents the design and evaluation of an innovative unmanned aerial vehicle (UAV) prototype that integrates advanced electronic components and artificial intelligence (AI), with the aim of reducing environmental impact and enabling autonomous navigation in complex urban environments. The UAV platform incorporates brushless DC motors, high-density LiPo batteries and perovskite solar cells to improve energy efficiency and increase flight range. The Deep Q-Network (DQN) allocates energy and selects reference points in the presence of wind and payload disturbances, while an integrated sensor system monitors motor vibration/temperature and charge status to prevent failures. In urban canyon and field scenarios (wind from 0 to 8 m/s; payload from 0.35 to 0.55 kg), the system reduces energy consumption by up to 18%, increases area coverage by 12% for the same charge, and maintains structural safety factors > 1.5 under gust loading. The approach combines sustainable materials, efficient propulsion, and real-time AI-based navigation for energy-conscious flight planning. A hybrid methodology, combining experimental design principles with finite-element-based structural modelling and AI-enhanced monitoring, has been applied to ensure structural health awareness. The study implements proven edge-AI sensor fusion architectures, balancing portability and telemonitoring with an integrated low-power design. The results confirm a reduction in energy consumption and CO2 emissions compared to traditional delivery vehicles, confirming that the proposed system represents a scalable and intelligent solution for last-mile delivery, contributing to climate resilience and urban sustainability. The findings position the proposed UAV as a scalable reference model for integrating AI-driven navigation and renewable energy systems in sustainable logistics.", "keywords": [ "Payload (computing)", "Drone", "Energy consumption", "Software portability", "Renewable energy", "Efficient energy use", "Scalability", "Testbed", "Smart city" ], "cited_by_count": 20, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/1996-1073/18/19/5242/pdf?version=1759403480" }, { "id": "https://openalex.org/W2796778093", "doi": "10.1557/mre.2018.5", "title": "Energy use by air taxis and drones for parcel delivery, is it practical? Is it sustainable?", "year": 2018, "publication_date": "2018-04-10", "type": "article", "language": "en", "authors": [ "Peter Rez" ], "first_author": "Peter Rez", "venue": "MRS Energy & Sustainability", "publisher": "Springer Nature", "abstract": null, "keywords": [ "Taxis", "Drone", "Sustainable energy", "Energy (signal processing)", "Environmental science", "Transport engineering", "Architectural engineering", "Engineering", "Renewable energy", "Electrical engineering", "Physics" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.cambridge.org/core/services/aop-cambridge-core/content/view/DE880B3AC1FE69132C3C627C7D3B8660/S2329222918000053a.pdf/div-class-title-energy-use-by-air-taxis-and-drones-for-parcel-delivery-is-it-practical-is-it-sustainable-div.pdf" }, { "id": "https://openalex.org/W4385557955", "doi": "10.11627/jksie.2023.46.2.160", "title": "Research of the Delivery Autonomy and Vision-based Landing Algorithm for Last-Mile Service using a UAV", "year": 2023, "publication_date": "2023-06-30", "type": "article", "language": "en", "authors": [ "Hanseob Lee", "Hoon Jung" ], "first_author": "Hanseob Lee", "venue": "Journal of Society of Korea Industrial and Systems Engineering", "publisher": null, "abstract": null, "keywords": [ "Mile", "Computer science", "Service (business)", "Last mile (transportation)", "Aeronautics", "Computer vision", "Algorithm", "Artificial intelligence", "Engineering", "Business", "Geography", "Geodesy" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.11627/jksie.2023.46.2.160" }, { "id": "https://openalex.org/W4412967503", "doi": "10.1016/j.ifacol.2025.07.033", "title": "Vertical Drone Delivery with Obstacle Avoidance", "year": 2025, "publication_date": "2025-01-01", "type": "article", "language": "en", "authors": [ "Nasser Al-Musallami", "Ryan O’Neil", "Abdelhakim Khatab", "Uday Venkatadri", "Claver Diallo" ], "first_author": "Nasser Al-Musallami", "venue": "IFAC-PapersOnLine", "publisher": "Elsevier BV", "abstract": "This paper introduces a hybrid drone-truck delivery model to optimize UAV-based logistics in last-mile delivery settings. The study combines energy consumption dynamics with advanced route optimization, considering factors such as payload weight, elevation changes, and obstacle avoidance. Utilizing the Theta ⁎ algorithm, the model achieves efficient path-finding while optimizing energy use through a scheme that applies energy over-consumption or under-consumption for ascent and descent, respectively. Additionally, the study incorporates profit modeling for successful deliveries to assess energy-revenue trade-offs. Results confirm the model’s ability to improve the operational efficiency of last-mile delivery systems, thus advancing sustainable urban logistics and addressing limitations in current drone delivery systems.", "keywords": [ "Drone", "Obstacle avoidance", "Obstacle", "Computer science", "Aeronautics", "Psychology", "Artificial intelligence", "Engineering", "History", "Biology", "Mobile robot", "Robot" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.ifacol.2025.07.033" }, { "id": "https://openalex.org/W4360914230", "doi": "10.1007/978-3-031-27915-7_36", "title": "Multiple Trip Drones-Truck Combined Operations for Last-Mile Delivery Applications. A Literature Review", "year": 2023, "publication_date": "2023-01-01", "type": "conference-paper", "language": "en", "authors": [ "David Sánchez-Wells", "Pedro Luis González Rodríguez", "José Luis Andrade-Pineda" ], "first_author": "David Sánchez-Wells", "venue": "Lecture notes on data engineering and communications technologies", "publisher": "Springer International Publishing", "abstract": null, "keywords": [ "Drone", "Truck", "Popularity", "Mile", "Last mile (transportation)", "Globe", "Transport engineering", "Engineering", "Aeronautics", "Computer science", "Operations research", "Operations management" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-031-27915-7_36" }, { "id": "https://openalex.org/W4293791090", "doi": "10.3390/app12168255", "title": "A Proactive Approach to Extended Vehicle Routing Problem with Drones (EVRPD)", "year": 2022, "publication_date": "2022-08-18", "type": "article", "language": "en", "authors": [ "Paweł Sitek", "Jarosław Wikarek", "Mieczysław Jagodziński" ], "first_author": "Paweł Sitek", "venue": "Applied Sciences", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Unmanned aerial vehicles (UAVs), also known as drones, are increasingly common and popular due to their relatively low prices and high mobility. The number of areas for their practical applications is rapidly growing. The most promising are: last-mile delivery, emergency response, the inspection of technical devices and installations, etc. In these applications, it is often necessary to solve vehicle routing problems, formulated as a variant of the vehicle routing problems with drones (VRPD). This study presents a proactive approach to a modified and extended VRPD, including: the dynamic selection of drone take-off points, bidirectional delivery (delivery and pick up), various types of shipments, allocation of shipments to drones and drones to vehicles, the selection of the optimal number of drones, etc. Moreover, a formal model of constraints and questions for the extended vehicle routing problem with drones (EVRPD) and exact and approximate methods for solving it have been proposed. The proposed model can be the basis for supporting proactive and reactive decisions regarding last-mile delivery, particularly the selection of the necessary fleet, starting points, the identification of specific shipments that prevent delivery with available resources, etc. The study also includes the results of numerous computational experiments verifying the effectiveness of the implementation methods. The time to obtain a solution is at least 20 times shorter for the proposed DGA (dedicated genetic algorithm) than for the mathematical programming solvers such as Gurobi or LINGO. Moreover, for larger-sized data instances, these solvers do not allow obtaining any solution in an acceptable time, or they obtain worse solutions.", "keywords": [ "Drone", "Computer science", "Vehicle routing problem", "Routing (electronic design automation)", "Selection (genetic algorithm)", "Operations research", "Identification (biology)", "Last mile (transportation)", "Mathematical optimization", "Mile", "Computer network", "Engineering" ], "cited_by_count": 14, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2076-3417/12/16/8255/pdf?version=1660824443" }, { "id": "https://openalex.org/W3110519825", "doi": "10.1109/fists46898.2020.9264901", "title": "Comparison of Time-Area Requirements of Parcel Lockers vs. Home Delivery: A Cyber-Physical System of Last Mile Delivery", "year": 2020, "publication_date": "2020-11-03", "type": "conference-paper", "language": "en", "authors": [ "Maren Schnieder", "Andrew A West" ], "first_author": "Maren Schnieder", "venue": null, "publisher": null, "abstract": "Last mile delivery is seen as the most expensive part of the supply chain, and causes significant external effects (e.g. pollution, land use, noise). Therefore, much research has been devoted to finding alternative vehicles to deliver parcels such as drones and cargo bicycles and to finding alternative delivery locations such as parcel lockers. Parcel lockers are seen in most simulation studies as the most sustainable option. However, most of these studies ignore that customers might drive a car to pick up parcels. Also, parcel lockers require space 24/7 whereas a delivery van only requires a parking space for a few minutes while the parcel is handed over. However, during home delivery more space is required for the delivery van while driving due to the increased vehicle kilometer traveled compared with delivery to parcel lockers. The contribution of the study reported in this paper is a simulation tool to evaluate different parcel delivery strategies and customer movements based on real parcel delivery trip data and statistics about the parcel receiving habits. Secondly, the study uses the resulting data to compare the Time-Area requirements of home delivery and parcel lockers.", "keywords": [ "Food delivery", "Last mile (transportation)", "City logistics", "Transport engineering", "Kilometer", "Delivery Performance", "Delivery system", "Computer science", "Mile", "Simulation", "Engineering", "Business" ], "cited_by_count": 7, "is_retracted": false, "is_paratext": false, "open_access_url": "https://figshare.com/articles/conference_contribution/Comparison_of_time-area_requirements_of_parcel_lockers_vs_home_delivery_a_cyber-physical_system_of_last_mile_delivery/19657476" }, { "id": "https://openalex.org/W4205744269", "doi": "10.2514/6.2022-1506", "title": "Crash Area Estimation for Ground Risk of Small Unmanned Aerial Vehicles Due to Propulsion System Failures.", "year": 2022, "publication_date": "2022-01-03", "type": "conference-paper", "language": "en", "authors": [ "Mohd Hasrizam Che Man", "Hu Haoliang", "Kin Huat Low" ], "first_author": "Mohd Hasrizam Che Man", "venue": "AIAA SCITECH 2022 Forum", "publisher": null, "abstract": "View Video Presentation: https://doi.org/10.2514/6.2022-1506.vid Drones or Unmanned Aerial Vehicles (UAVs) are expected to be used for different applications like parcel delivery, inspection, and aerial photography in the urban area. However, UAVs usually uses an electric system to power up the propulsion, communications, navigation, and flight control system, which means it is not as reliable as the manned aircraft system and may result in failure during operation and then crash to the ground. At present, there is almost no publication about the high-fidelity modeling used by UAVs to calculate the crash trajectory and point of crash. The experimental data for modeling and simulation verification of multi-rotor aircraft is limited. So far, crash trajectory prediction has been limited to point mass or ballistic methods, and these methods are usually only suitable for complete power failure and without any control system. This study intends to investigate the effects of different UAV failure modes on its crash trajectory and crash area compared to the ballistic model by using ADAMS and MATLAB co-simulation methods. Conclusions from the study show the crash trajectory, flight distance and impact speed of the UAV under four failure modes, which are quite different from the ballistic trajectory. The findings can potentially contribute to better risk assessment of the multi-rotor sUAV in the urban environment operation.", "keywords": [ "Crash", "Trajectory", "Propulsion", "Computer science", "Rotor (electric)", "Aerospace engineering", "Simulation", "Engineering", "Aeronautics" ], "cited_by_count": 14, "is_retracted": false, "is_paratext": false, "open_access_url": "https://dr.ntu.edu.sg/bitstream/10356/155948/3/Ground%20Risk%20Estimation%20AIAA%20Scitech%202022.pdf" }, { "id": "https://openalex.org/W4389058205", "doi": "10.3390/su152316342", "title": "Truck-Drone Pickup and Delivery Problem with Drone Weight-Related Cost", "year": 2023, "publication_date": "2023-11-27", "type": "article", "language": "en", "authors": [ "Yang Xia", "Tingying Wu", "Beixin Xia", "Junkang Zhang" ], "first_author": "Yang Xia", "venue": "Sustainability", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Truck-drone delivery is widely used in logistics distribution for achieving sustainable development, in which drone weight greatly affects transportation cost. Thus, we consider a new combined truck-drone pickup and delivery problem with drone weight-related cost in the context of last-mile logistics. A system of integer programming is formulated with the objective of minimizing the total cost of the drone weight-related cost, fixed vehicle cost and travel distance cost. An improved adaptive large neighborhood search algorithm (IALNS) is designed based on the characteristics of the problem, several effective destroy and repair operators are designed to explore the solution space, and a simulated annealing strategy is introduced to avoid falling into the local optimal solution. To evaluate the performance of the IALNS algorithm, 72 instances are randomly generated and tested. The computational results on small instances show that the proposed IALNS algorithm performs better than CPLEX both in efficiency and effectiveness. When comparing the truck-drone pickup and delivery problem with drone weight-related cost to the problem without drone weight-related cost, it is found that ignoring the drone weight constraints leads to an underestimate of the total travel cost by 12.61% based on the test of large instances.", "keywords": [ "Drone", "Truck", "Computer science", "Context (archaeology)", "Pickup", "Simulated annealing", "Mathematical optimization", "Operations research", "Simulation", "Engineering", "Automotive engineering", "Mathematics" ], "cited_by_count": 9, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2071-1050/15/23/16342/pdf?version=1701093881" }, { "id": "https://openalex.org/W4362727385", "doi": "10.1007/978-3-031-29871-4_48", "title": "Vertical Trajectory Analysis Using QR Code Detection for Drone Delivery Application", "year": 2023, "publication_date": "2023-01-01", "type": "conference-paper", "language": "en", "authors": [ "Avishkar Seth", "Alice James", "Endrowednes Kuantama", "Subhas Chandra Mukhopadhyay", "Richard Y. Han" ], "first_author": "Avishkar Seth", "venue": "Lecture notes in electrical engineering", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Drone", "Trajectory", "Computer science", "Real-time computing", "Code (set theory)", "Tracking (education)", "Computer vision", "Artificial intelligence", "Simulation" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-031-29871-4_48" }, { "id": "https://openalex.org/W2938352754", "doi": "10.1016/j.trpro.2020.03.160", "title": "A vehicle routing problem with movement synchronization of drones, sidewalk robots, or foot-walkers", "year": 2020, "publication_date": "2020-01-01", "type": "article", "language": "en", "authors": [ "Puyuan Deng", "Glareh Amirjamshidi", "Matthew J. Roorda" ], "first_author": "Puyuan Deng", "venue": "Transportation research procedia", "publisher": "Elsevier BV", "abstract": "The vehicle routing problem (VRP) and its variants have many city logistics applications, such as goods delivery. The VRP extension with movement synchronization (VRPMS) has potential applications of drone and robot technologies to assist with the delivery of parcels. VRPMS seeks the optimal route for a set of composite resources, e.g. delivery van with drones, or delivery van with sidewalk robots. This paper proposes an exact formulation of the problem, and a metaheuristic approach to solve larger instances of the VRPMS in order to assess the economic benefits of the different technologies. It is shown that with the current physical constraints of drone technology, assisted delivery with drones has some challenges because of its capacity. Sidewalk robots and walkers, however, do contribute a cost savings compared to truck deliveries. As the technology matures, the presented metaheuristic approach can be used to evaluate improved economic benefits and cost benefit ratios.", "keywords": [ "Drone", "Robot", "Vehicle routing problem", "Metaheuristic", "Routing (electronic design automation)", "Computer science", "Synchronization (alternating current)", "Truck", "Transport engineering", "Set (abstract data type)", "Operations research", "Simulation" ], "cited_by_count": 25, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.sciencedirect.com/science/article/pii/S2352146520303604/pdf" }, { "id": "https://openalex.org/W7122643066", "doi": "10.1016/j.ejor.2026.01.013", "title": "Improving last-mile delivery efficiency using cargo bikes and autonomous robots", "year": 2026, "publication_date": "2026-01-12", "type": "article", "language": "en", "authors": [ "Biao Yuan", "Bingjie Yang", "Na Geng", "Roberto Baldacci" ], "first_author": "Biao Yuan", "venue": "European Journal of Operational Research", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Vehicle routing problem", "Lagrangian relaxation", "Robot", "Heuristic", "Drone", "Routing (electronic design automation)", "Bounded function", "Focus (optics)" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.sciencedirect.com/science/article/pii/S0377221726000329" }, { "id": "https://openalex.org/W2602527578", "doi": null, "title": "Aerial Transport System Design: Design of an Aerial Transport System for last mile parcel distribution with Unmanned Aerial Vehicles", "year": 2016, "publication_date": "2016-11-02", "type": "dissertation", "language": "en", "authors": [ "Doornebosch, E.W.L.J. (author)" ], "first_author": "Doornebosch, E.W.L.J. (author)", "venue": "Research Repository (Delft University of Technology)", "publisher": "Delft University of Technology", "abstract": null, "keywords": [ "Mile", "Aerial photography", "Aerial survey", "Last mile (transportation)", "Marine engineering", "Computer science", "Engineering", "Environmental science", "Remote sensing", "Geography", "Geodesy" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "http://resolver.tudelft.nl/uuid:9dd1c20f-0de8-4b69-8501-325ae07baeb3" }, { "id": "https://openalex.org/W4407777942", "doi": "10.1016/j.pdisas.2025.100411", "title": "A combined fleet size and mix vehicle routing model for last-mile distribution in disaster relief", "year": 2025, "publication_date": "2025-02-20", "type": "article", "language": "en", "authors": [ "Katharina Eberhardt", "Florian Diehlmann", "Markus Lüttenberg", "Florian Klaus Kaiser", "Frank Schultmann" ], "first_author": "Katharina Eberhardt", "venue": "Progress in Disaster Science", "publisher": "Elsevier BV", "abstract": "Disasters pose a significant challenge for last-mile operations, straining emergency logistics systems' ability to provide efficient aid and support. In this context, a Fleet Size and Mix Vehicle Routing Problem for Disaster Management (FSMVRP-DM) is formulated, incorporating a fleet composition decision tailored to the specifics of disaster relief logistics. The model aims to optimize routing and analyze fleet decisions to minimize the sum of operating costs and population deprivation costs. Moreover, a prioritization approach is introduced to monitor deprivation time during transport resource scarcity, adjusting routes periodically to prevent extended supply gaps and minimize suffering costs. In addition, a case study is conducted in the German state of Baden-Württemberg to illustrate the potential applicability of the model. The findings highlight the advantages of integrating diverse and innovative fleet types, such as drones, and prioritizing the supply of multiple demand points when resources are scarce. Overall, the research offers decision support for authorities by enhancing information transparency, facilitating resource management, strengthening the effectiveness of disaster response capabilities, and providing resilient and adaptive strategies for last-mile distribution. • Preparedness and adaptive strategies increase resilience in last-mile operations. • Optimal fleet decisions ensure timely response and strengthen delivery performance. • Diversifying fleet composition enhances cost efficiency and operational capability. • Strategic prioritization minimizes human suffering through improved demand coverage.", "keywords": [ "Mile", "Vehicle routing problem", "Transport engineering", "Distribution (mathematics)", "Last mile (transportation)", "Routing (electronic design automation)", "Business", "Computer science", "Engineering", "Geography", "Computer network", "Mathematics" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.pdisas.2025.100411" }, { "id": "https://openalex.org/W3107336733", "doi": "10.1016/j.eastsj.2020.100028", "title": "A Monte Carlo tree search for traveling salesman problem with drone", "year": 2020, "publication_date": "2020-01-01", "type": "article", "language": "en", "authors": [ "Minh Anh Nguyen", "Kazushi Sano", "Vu Tu Tran" ], "first_author": "Minh Anh Nguyen", "venue": "Asian Transport Studies", "publisher": "Elsevier BV", "abstract": "The use of drones and trucks working collaboratively has gained drastically attentions in recent years. We develop a new Monte Carlo Tree Search algorithm (MCTS) to solve the Traveling Salesman Problem with Drone (TSP-D) arising in the management of parcel last-mile-delivery systems. The approach seeks to find optimal decisions by taking random samples in decision space and building a search tree based on the results. We address several major issues in adapting the tree search concept to solve the TSP-D in this paper. The solution approach, aimed to minimize the total completion time, is tested on various problem instances derived from the well-known TSPLIB benchmark. Experimental results show a very promising performance of the proposed search method where it outperforms the compared heuristic method, providing new best solutions for 23 instances and an average 12% improvement in terms of solution quality.", "keywords": [ "Travelling salesman problem", "Monte Carlo tree search", "Drone", "Benchmark (surveying)", "Computer science", "Heuristic", "Mathematical optimization", "Tree (set theory)", "Monte Carlo method", "Heuristics", "Local search (optimization)", "Truck" ], "cited_by_count": 18, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.sciencedirect.com/science/article/pii/S2185556020300286/pdf" }, { "id": "https://openalex.org/W3186804584", "doi": "10.2514/6.2021-2379", "title": "A Preliminary Path Planning Algorithm for the Last-mile Delivery by an Unmanned Aerial Vehicle in Urban Town Areas", "year": 2021, "publication_date": "2021-07-28", "type": "conference-paper", "language": "en", "authors": [ "Mingcheng Zhang", "Kin Huat Low" ], "first_author": "Mingcheng Zhang", "venue": "AIAA AVIATION 2021 FORUM", "publisher": null, "abstract": "View Video Presentation: https://doi.org/10.2514/6.2021-2379.vid The applications by means of drones or unmanned aerial vehicles (UAVs) are increasing in areas from military to industry, commercial, and civil. Examples of applications include surveillance, photography, agriculture, and parcel delivery. Increasing applications results in more people will experience drones in their daily lives. However, not all people are comfortable with the drones around them, this is especially true in terms of urban last mile (LM) drone delivery, people tend to get annoyed when noisy drones pass by their window. This paper presents a novel 3D path planning algorithm that is applicable to the last mile (LM) delivery in an urban environment. The algorithm is called the modified Jump Point Search (MJPS) algorithm. It is developed based on the original Jump Point Search (JPS) and combined with the A* search algorithm. The result of the MJPS algorithm shows an obstacle-free path with reduced residences’ perceived annoyance due to drone operation noise and operational risk. It can be used as a way to increase public acceptance for drone operations in close vicinity of their neighborhood.", "keywords": [ "Drone", "Last mile (transportation)", "Motion planning", "Mile", "Computer science", "Jump", "Path (computing)", "Point (geometry)", "Noise pollution", "Obstacle", "Real-time computing", "Simulation" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2514/6.2021-2379" }, { "id": "https://openalex.org/W3202428833", "doi": "10.48550/arxiv.2103.04118", "title": "Towards Fully Autonomous Drone-Based Last-Mile Delivery", "year": 2021, "publication_date": "2021-03-06", "type": "preprint", "language": "en", "authors": [ "Navid Mohammad Imran", "Sabyasachee Mishra", "Myounggyu Won" ], "first_author": "Navid Mohammad Imran", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "The drone-based last-mile delivery is an emerging technology to deliver parcels using drones loaded on a truck. As more and more autonomous vehicles (AVs) will be available for delivery services, an opportunity is arising to fully automate drone-based last-mile delivery. In this paper, we integrate AVs with drone-based last-mile delivery aiming to fully automate the last-mile delivery process. We define a new problem called the autonomous vehicle routing problem with drones (A-VRPD). A-VRPD is to select AVs from a pool of available AVs and to schedule them to serve customers with an objective of minimizing the total operational cost. We formulate A-VRPD as an Integer Linear Programming (ILP) and propose a greedy algorithm to solve the problem based on real-world operational costs for different types of AVs, traveling distances calculated considering the current traffic conditions, and varying load capacities of AVs. Extensive simulations performed under various random delivery scenarios demonstrate that the proposed algorithm effectively increases profits for both the delivery company and AV owners compared with traditional VRP-D (and TSP-D) algorithm-based approaches.", "keywords": [ "Drone", "Last mile (transportation)", "Mile", "Computer science", "Schedule", "Integer programming", "Vehicle routing problem", "Truck", "Linear programming", "Real-time computing", "Routing (electronic design automation)", "Process (computing)" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2103.04118" }, { "id": "https://openalex.org/W3004178595", "doi": "10.1109/indin41052.2019.8972296", "title": "Control of a Novel Parcel Delivery System Consisting of a UAV and a Public Train", "year": 2019, "publication_date": "2019-07-01", "type": "conference-paper", "language": "en", "authors": [ "Hailong Huang", "Andrey V. Savkin", "Chao Huang" ], "first_author": "Hailong Huang", "venue": null, "publisher": null, "abstract": "A parcel delivery system with a unmanned aerial vehicle (UAV) and a public train is presented, where the train moves naturally as it is and the UAV can depart the train to deliver parcels. The UAV can travel with the train and replace its battery on the roof. An optimization problem is formulated to minimize the total delivery time and two algorithms are proposed. The exact algorithm gives the optimal schedule, but it is not scalable. The developed suboptimal algorithm is computationally efficient and achieves close performance to that of the exact algorithm. Realistic simulations are conducted to evaluate the proposed algorithms and they are compared with existing schemes.", "keywords": [ "Schedule", "Computer science", "Scalability", "Real-time computing", "Mathematical optimization", "Simulation" ], "cited_by_count": 9, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/indin41052.2019.8972296" }, { "id": "https://openalex.org/W4285151401", "doi": "10.1007/978-3-030-96935-6_27", "title": "Drones and Delivery Robots: Models and Applications to Last Mile Delivery", "year": 2022, "publication_date": "2022-01-01", "type": "book-chapter", "language": "en", "authors": [ "Cheng Chen", "Emrah Demir" ], "first_author": "Cheng Chen", "venue": null, "publisher": null, "abstract": null, "keywords": [ "Last mile (transportation)", "Drone", "Vehicle routing problem", "Robot", "Mile", "Routing (electronic design automation)", "Transport engineering", "Value proposition", "Engineering", "Computer science", "Business", "Marketing" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-030-96935-6_27" }, { "id": "https://openalex.org/W2910885869", "doi": "10.2514/6.2019-1063", "title": "Morphing Concept for Multirotor UAVs Enabling Stability Augmentation and Multiple-Parcel Delivery", "year": 2019, "publication_date": "2019-01-06", "type": "conference-paper", "language": "en", "authors": [ "Damian Cheng", "Aravinda C. Charles", "Spot Srigrarom", "Henrik Hesse" ], "first_author": "Damian Cheng", "venue": "AIAA Scitech 2019 Forum", "publisher": null, "abstract": "This paper presents a novel morphing concept for multirotor Unmanned Aerial Vehicles (UAVs) to optimize the vehicle ight performance during multi-parcel deliveries. Abrupt changes in the vehicle weight distribution during a parcel delivery can cause the UAVs to be unbalanced. This is usually compensated by the vehicle ight control system but the motors may need to operate outside their design range which can deteriorate the stability and performance of the system. Morphing the geometry of a conventional multirotor airframe enables the vehicle to continuously re-balanced itself which improves the overall vehicle performance and safety. The paper derives expressions for the static stability of multirotor UAVs and discusses the experimental implementation of the morphing technology on a Y6 tricopter configuration. Flight test results of multi-parcel delivery scenarios demonstrate the capability of the proposed technology to balance the throttle outputs of all rotors.", "keywords": [ "Multirotor", "Morphing", "Computer science", "Stability (learning theory)", "Aerospace engineering", "Engineering", "Computer graphics (images)" ], "cited_by_count": 10, "is_retracted": false, "is_paratext": false, "open_access_url": "http://eprints.gla.ac.uk/view/author/38965.html>" }, { "id": "https://openalex.org/W2981260772", "doi": "10.23919/chicc.2019.8866511", "title": "Optimal Control of a Hybrid UAV/Train Parcel Delivery System", "year": 2019, "publication_date": "2019-07-01", "type": "conference-paper", "language": "en", "authors": [ "Hailong Huang", "Andrey V. Savkin", "Chao Huang" ], "first_author": "Hailong Huang", "venue": null, "publisher": null, "abstract": "This paper presents a novel parcel delivery system consisting of an unmanned aerial vehicle (UAV) and a public train. The train moves along a fixed route following a predefined timetable and carries a set of parcels. The UAV departs from the train, delivers a parcel to a customer, returns to the train, and picks another parcel. The UAV can also travel with the train and replace the battery on the roof of it. Since the train is not controlled by the parcel delivery system, its timetable should be taken into account to efficiently deliver parcels. An optimal control problem is formulated with the objective of minimizing the total delivery time and a suboptimal algorithm is developed, which is computationally efficient. A case study is conducted to evaluate the proposed algorithm and compare it with existing schemes. The proposed approach is more cost efficient than the Truck only scheme and the Truck plus UAV scheme, since there is no cost associated with the truck. Moreover, it improves delivery time and covers a larger area compare to the UAV only delivery scheme.", "keywords": [ "Truck", "Scheme (mathematics)", "Computer science", "Real-time computing", "Delivery Performance", "Control (management)", "Set (abstract data type)", "Simulation", "Automotive engineering", "Engineering", "Artificial intelligence", "Industrial engineering" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.23919/chicc.2019.8866511" }, { "id": "https://openalex.org/W4367838509", "doi": "10.1109/sm57895.2023.10112569", "title": "Optimal Placement of Drone Delivery Stations and Demand Allocation using Bio-inspired Algorithms", "year": 2023, "publication_date": "2023-03-19", "type": "conference-paper", "language": "en", "authors": [ "Feras Elsaid", "Enrique Torres Sanchez", "Yilun Li", "Alaa M. Khamis" ], "first_author": "Feras Elsaid", "venue": null, "publisher": null, "abstract": "The last-mile delivery problem has recently received more attention due to dramatic increase in e-commerce and one-day delivery options. One delivery method has been in the forefront of last mile delivery research: drone delivery. Particularly, hybrid truck-drone delivery systems which attempt to overcome the limitations of only using drones. Researchers have attempted to improve drone routing and scheduling, but not many have studied the required infrastructure, including drone stations for recharging and pick-up of packages. This paper tackles the placement of drone delivery stations using bio-inspired optimization algorithms. The solution framework consists of two stages. The first stage tackles the location planning problem of stations, while the second stage deals with the allocation of delivery demand to located stations. The conventional k-means algorithm is used as a baseline for the location planning problem, while the greedy algorithm is used as a baseline for the demand allocation problem. The results shows that the simulated annealing algorithm achieves a 14% reduction in total cost and 6.2× run time improvement, whereas the genetic algorithm achieves a reduction of 10% in total cost and 1.8× run time improvement.", "keywords": [ "Drone", "Computer science", "Simulated annealing", "Last mile (transportation)", "Baseline (sea)", "Delivery Performance", "Real-time computing", "Scheduling (production processes)", "Vehicle routing problem", "Greedy algorithm", "Genetic algorithm", "Routing (electronic design automation)" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/sm57895.2023.10112569" }, { "id": "https://openalex.org/W4415499191", "doi": "10.1007/978-3-031-92251-0_7", "title": "Cooperative Use of Truck—Multi-Trip Drones in Last-Mile Logistics Combined Operations: A Brief Review", "year": 2025, "publication_date": "2025-10-24", "type": "book-chapter", "language": "en", "authors": [ "David Sánchez-Wells", "Pedro Luis González Rodríguez", "José Luis Andrade-Pineda" ], "first_author": "David Sánchez-Wells", "venue": "Lecture notes in management and industrial engineering", "publisher": "Springer International Publishing", "abstract": null, "keywords": [ "Drone", "Truck", "Humanitarian Logistics" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-031-92251-0_7" }, { "id": "https://openalex.org/W3153526484", "doi": "10.1016/j.ifacol.2020.12.2004", "title": "A Novel Mathematical Model for a Cloud-Based Drone Enabled Vehicle Routing Problem considering Multi-Echelon Supply Chain", "year": 2020, "publication_date": "2020-01-01", "type": "article", "language": "en", "authors": [ "Fatemeh Farajzadeh", "Amirhossein Moadab", "Omid Fatahi Valilai", "Mahmoud Houshmand" ], "first_author": "Fatemeh Farajzadeh", "venue": "IFAC-PapersOnLine", "publisher": "Elsevier BV", "abstract": "In recent years, the product delivery system has faced a significant amount of changes with reducing cost and waiting time of last-mile delivery. Unmanned aerial vehicles, or drones, have the potential to deal with these issues effectively. This paper developed a novel mathematical formulation to investigate the optimum routing between various types of nodes in a multi-echelon supply chain, including third-party logistics companies (3PL), warehouses, and consumers. A conceptual model is represented in the paper to comprehend the readers better, and eventually, the novelty of the developed model is discussed further by explaining the concept of solving.", "keywords": [ "Drone", "Supply chain", "Computer science", "Novelty", "Vehicle routing problem", "Cloud computing", "Operations research", "Product (mathematics)", "Routing (electronic design automation)", "Stackelberg competition", "Conceptual model", "Industrial engineering" ], "cited_by_count": 21, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.ifacol.2020.12.2004" }, { "id": "https://openalex.org/W3033016869", "doi": "10.1016/j.ast.2020.105887", "title": "Relay manoeuvre based fixed-time synchronized tracking control for UAV transport system", "year": 2020, "publication_date": "2020-06-05", "type": "article", "language": "en", "authors": [ "Jawhar Ghommam", "Maarouf Saad", "Steve Wright", "Quanmin Zhu" ], "first_author": "Jawhar Ghommam", "venue": "Aerospace Science and Technology", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Control theory (sociology)", "Relay", "Underactuation", "Decoupling (probability)", "Computer science", "Synchronization (alternating current)", "Control engineering", "Engineering", "Control (management)", "Artificial intelligence" ], "cited_by_count": 49, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.ast.2020.105887" }, { "id": "https://openalex.org/W4323021620", "doi": "10.26552/tac.c.2022.2.3", "title": "Last Mile and its Latest Changes in Express, Courier and Postal Services Bound to E-commerce", "year": 2022, "publication_date": "2022-11-30", "type": "article", "language": "en", "authors": [ "Dominika Koncová", "Iveta Kremeňová", "Juraj Fabuš" ], "first_author": "Dominika Koncová", "venue": "Transport and Communications", "publisher": "University of Zulia", "abstract": "Last mile delivery is influenced by both technology as well as customer requirements and their preferences. Recently, technologies such as drones, automated guided vehicles (AGVs) or robots have come to the fore to deliver parcels to their addressees or authorised recipients, instead of delivering them solely by couriers or into letters or parcel lockers. It cannot be argued that delivery by courier or to letter or parcel lockers is unattractive, but, in terms of innovation, it is already possible to regard these forms of delivery as a standard that has been in practice for some time. It is therefore necessary to discuss technologies that have both automated elements and sensors or a certain degree of implemented intelligence. Drones and AGVs are just such devices. Thus, this paper is devoted to comparing the advantages and disadvantages of using drones and AGVs.", "keywords": [ "Drone", "Last mile (transportation)", "Mile", "Computer science", "Robot", "Artificial intelligence", "Geography" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "http://tac.uniza.sk/doi/10.26552/tac.C.2022.2.3.pdf" }, { "id": "https://openalex.org/W4404915168", "doi": "10.1109/scisisis61014.2024.10760235", "title": "Integer Programming Approaches to an Issue of Scheduling for a Truck-Drone Last-Mile Delivery System with Parcel Pickup Service", "year": 2024, "publication_date": "2024-11-09", "type": "conference-paper", "language": "en", "authors": [ "Kotaro Yamada", "Yoshiyuki Karuno", "Hitoshi Iima" ], "first_author": "Kotaro Yamada", "venue": null, "publisher": null, "abstract": "In this paper, we consider a scheduling issue for parcel delivery and pickup services by a truck-drone last-mile delivery system. We are given a single carrier truck and multiple identical drones to serve a finite set of customers. The single carrier truck plays the role of a mobile depot for the drones, and it visits several stops in a given order of them. The given order of stops constitutes a fixed truck route, and the carrier truck is allowed to launch (resp., to retrieve) a drone to (resp., from) a customer at a stop. For each sortie of a drone, the launching stop and the retrieving stop must be the same. The load capacity of a drone is limited to one, and for a sortie of a drone at a stop, there are three options: (i) only deliver a parcel to a customer, (ii) only pick up a parcel from a customer, and (iii) deliver a parcel to a customer, and then pick up a parcel from a customer. The scheduling problem asks to find an assignment of customers to drones with sortie options and a choice of a truck stop for each sortie in the assignment. The objective is to minimize the total duration of the carrier truck over all the stops, which is equivalent to minimizing the makespan. In this paper, we first propose an extended integer program for the case with both delivery and pickup services from an existing one for the case with delivery service only. We also conduct numerical experiments to demonstrate the solution quality of the proposed integer program by utilizing an integer programming solver, and report the results.", "keywords": [ "Pickup", "Drone", "Truck", "Last mile (transportation)", "Integer programming", "Computer science", "Mile", "Scheduling (production processes)", "Job shop scheduling", "Mathematical optimization", "Engineering", "Computer network" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/scisisis61014.2024.10760235" }, { "id": "https://openalex.org/W3174775258", "doi": "10.1109/istas50296.2020.9462187", "title": "“Killing Two Birds with One Stone”? A Case Study of Development Use of Drones", "year": 2020, "publication_date": "2020-11-12", "type": "conference-paper", "language": "en", "authors": [ "Ning Wang" ], "first_author": "Ning Wang", "venue": null, "publisher": null, "abstract": "With the rise of the “humanitarian drone” in recent years, drones have become one of the most controversial public interest technologies that have gained increasing media attention. It is worth noting that, although there is a perception in the aid sector that drones hold the promise to reinvent the health supply logistics, to date, routine drone delivery is still relatively new and largely unproven. This paper presents a recent field study conducted in 2019, where drones were deployed in Malawi to help address the last mile challenge in medical supply delivery, and where a noticeable mentality of “killing two birds with one stone” around the attempt of using drones in resource-poor settings is observed. The objective of the paper is to shed light, through a real-world case study and from the ethical perspective, on the impacts of implementing such a systemic change in the existing health supply chain systems. As conclusion, a call for more reflexive approaches for the critical examination, as well as more structured guidance for the responsible evaluation, of medical cargo drones is raised.", "keywords": [ "Drone", "Supply chain", "Business", "Perspective (graphical)", "Reflexivity", "Perception", "Public relations", "Marketing", "Political science", "Computer science", "Sociology", "Psychology" ], "cited_by_count": 17, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.zora.uzh.ch/id/eprint/204596/1/IEEE_ISTAS_2020_Zora.pdf" }, { "id": "https://openalex.org/W7130643550", "doi": "10.1016/j.tre.2026.104759", "title": "Economic and environmental impacts of integrated drone delivery systems", "year": 2026, "publication_date": "2026-02-20", "type": "article", "language": "en", "authors": [ "Juan Zhang", "Liu Yang", "James F. Campbell" ], "first_author": "Juan Zhang", "venue": "Transportation Research Part E Logistics and Transportation Review", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Payload (computing)", "Integer programming", "Key (lock)", "Work (physics)", "Linear programming", "Economic impact analysis", "Pareto principle", "Total cost", "Multi-objective optimization" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.tre.2026.104759" }, { "id": "https://openalex.org/W4390394662", "doi": "10.3390/drones8010006", "title": "Paving the Way for Last-Mile Delivery in Greece: Data-Driven Performance Analysis with a Customized Quadrotor", "year": 2023, "publication_date": "2023-12-29", "type": "article", "language": "en", "authors": [ "Charalabos Ioannidis", "A.-M. Boutsi", "Georgios Tsingenopoulos", "Sofia Soile", "Regina Chliverou", "Chryssy Potsiou" ], "first_author": "Charalabos Ioannidis", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "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.", "keywords": [ "Drone", "Payload (computing)", "Software deployment", "Last mile (transportation)", "Energy consumption", "Mile", "Computer science", "Data collection", "Range (aeronautics)", "Transport engineering", "Operations research", "Aeronautics" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2504-446X/8/1/6/pdf?version=1703863758" }, { "id": "https://openalex.org/W3213342001", "doi": "10.1016/j.clet.2021.100322", "title": "Energy demand of parcel delivery services with a mixed fleet of electric vehicles", "year": 2021, "publication_date": "2021-11-08", "type": "article", "language": "en", "authors": [ "Thomas Kirschstein" ], "first_author": "Thomas Kirschstein", "venue": "Cleaner Engineering and Technology", "publisher": "Elsevier BV", "abstract": "Drone logistics is considered as a disruptive business model reshaping logistics in the next decades. Most prominent potential advantages of drone delivery are cost savings, high speed, and high flexibility. Additionally, drones are also considered as a means of green transportation as they are electric vehicles which are potentially emission-free. To which extent these claimed potentials exist depends on the application scenario as well as the environmental and technological conditions. In this study a stationary drone delivery system is considered where parcels are delivered from a central depot to customers either by drone or electric truck. The minimal total energy consumption for serving all customers is determined when using only an electric truck or a mixed fleet of electric trucks and drones. In a simulation study the effects of structural characteristics (like numbers of customers and customer density) and environmental conditions (like wind speed and traffic conditions) on potential energy savings using drones are estimated. The results indicate that structural characteristics and environmental conditions heavily affect the energy saving potential of drones. In urban settings with high customer density, the energy saving potential is limited to at most 1% while in rural settings drones can help to save 5% of total energy. Under drone-favoring conditions like calm winds and heavy traffic, the energy saving potential can double.", "keywords": [ "Drone", "Truck", "Flexibility (engineering)", "Energy consumption", "Electricity", "Transport engineering", "Automotive engineering", "Electric vehicle", "Environmental economics", "Environmental science", "Business", "Engineering" ], "cited_by_count": 15, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.clet.2021.100322" }, { "id": "https://openalex.org/W4408559999", "doi": "10.1016/j.trpro.2025.02.005", "title": "Urban case studies on optimized truck-drone last-mile distribution", "year": 2025, "publication_date": "2025-01-01", "type": "article", "language": "en", "authors": [ "Rui Zhang", "Junfeng Xiao", "Florian M. Heinitz" ], "first_author": "Rui Zhang", "venue": "Transportation research procedia", "publisher": "Elsevier BV", "abstract": "Evaluating an urban/sub-urban freight transport system with targeted drone sorties from and in addition to delivery trucks at the micro level, we present a computational application of a single-truck, multiple-drones routing problem with self-developed heuristics for a set of real urban networks using a program solver. Results from five cities worldwide demonstrate by example that the new approach – compared to optimum road-based delivery – is more carbon and cost efficient, as it can save 43 to 51% in delivery time at a scale of 20 customer nodes. Further to this, a quantitative analysis of the impact of customer node aggregation and varying drone-to-truck speed ratios on the profitability and negative externalities of the collaborative delivery system is posed. In effect, the solution quality increases at a lower computational effort.", "keywords": [ "Drone", "Mile", "Truck", "Transport engineering", "Last mile (transportation)", "Aeronautics", "Engineering", "Environmental science", "Computer science", "Geography", "Automotive engineering", "Biology" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.trpro.2025.02.005" }, { "id": "https://openalex.org/W7140082409", "doi": "10.1016/j.cstp.2026.101795", "title": "Integrated drone delivery network design under policy-induced restrictions", "year": 2026, "publication_date": "2026-03-23", "type": "article", "language": "en", "authors": [ "Shuang Wang", "Sebastian Wandelt", "Xiaoqian Sun", "Eike Stumpf" ], "first_author": "Shuang Wang", "venue": "Case Studies on Transport Policy", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Network planning and design", "Flow network", "Key (lock)" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.cstp.2026.101795" }, { "id": "https://openalex.org/W4410015585", "doi": "10.1016/j.ress.2025.111178", "title": "Dynamic UAV trajectory optimisation for parcel delivery with integrated third-party risk mitigation", "year": 2025, "publication_date": "2025-05-01", "type": "article", "language": "en", "authors": [ "Enrique Aldao", "Fernando Veiga-López", "Caroline Ponzoni Carvalho Chanel", "Yoko Watanabe", "H. González-Jorge" ], "first_author": "Enrique Aldao", "venue": "Reliability Engineering & System Safety", "publisher": "Elsevier BV", "abstract": "International audience", "keywords": [ "Trajectory", "Computer science", "Operations research", "Risk analysis (engineering)", "Engineering", "Business", "Physics" ], "cited_by_count": 12, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.ress.2025.111178" }, { "id": "https://openalex.org/W4407871417", "doi": "10.1007/s44250-025-00187-w", "title": "Beyond numbers: a novel IoT and drone framework for enhancing healthcare logistics", "year": 2025, "publication_date": "2025-02-24", "type": "article", "language": "en", "authors": [ "Tejinder Singh Lakhwani", "Yerasani Sinjana", "Anuj Pal Kapoor" ], "first_author": "Tejinder Singh Lakhwani", "venue": "Discover Health Systems", "publisher": "Springer Science+Business Media", "abstract": "Efficient healthcare logistics, particularly for perishable medical supplies like blood bags, require innovative solutions to overcome the limitations of traditional cost-focused models. This study introduces an IoT-Drone framework that enhances adaptability, reliability, and sustainability in healthcare logistics. IoT sensors ensure continuous monitoring for quality assurance, while drones enable rapid last-mile delivery, bypassing challenges such as traffic congestion and limited infrastructure. Simulation results reveal a 78% reduction in delivery times and a 65% decrease in carbon emissions, demonstrating the framework's transformative potential in revolutionizing healthcare logistics. A hypothetical scenario illustrates its capacity to navigate logistical barriers in remote settings, ensuring timely delivery of life-saving supplies and improved patient care. Beyond healthcare, the framework’s adaptability extends to emergency response, vaccine distribution, and other critical sectors, offering scalable solutions for diverse logistical challenges. By addressing technical, regulatory, and infrastructural barriers through modular drone designs, hybrid transport models, and AI-driven route optimization, this study provides a robust foundation for real-world applications. Prioritizing real-time decision-making, operational resilience, and environmental sustainability, the IoT-Drone framework sets a new standard for global logistics. It offers a future-ready solution for delivering essential supplies to remote and unpredictable locations, redefining efficiency, reliability, and equity across industries.", "keywords": [ "Drone", "Internet of Things", "Health care", "Computer science", "Business", "Computer security", "Political science" ], "cited_by_count": 18, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/s44250-025-00187-w" }, { "id": "https://openalex.org/W4387010663", "doi": "10.1109/jiot.2023.3318524", "title": "Collaborative Delivery Optimization With Multiple Drones via Constrained Hybrid Pointer Network", "year": 2023, "publication_date": "2023-09-25", "type": "article", "language": "en", "authors": [ "Fanhui Kong", "Bin Jiang", "Jian Wang", "Huihui Helen Wang", "Houbing Herbert Song" ], "first_author": "Fanhui Kong", "venue": "IEEE Internet of Things Journal", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "Drone participation in truck delivery is a potential booster for the last-mile logistics system, which has been an emerging hot research field. Among that, how to arrange a fleet of drones from the truck and optimize the vehicle routing problem with drones (VRPDs) is a key issue. However, most existing studies fail to derive the feasible solutions due to unordered customer distributions and multivariant drone feature constraints. In this article, we propose a novel self-driven reinforcement learning structure, named constraint-based hybrid pointer network (CH-Ptr-Net) model, which is a hybrid pointer network approach composed of graph neural network (GNN) embedding and attention decoder. We go into developing the simpler embedding version for multiple drones-assisted truck delivery. The CH-Ptr-Net model tends to generate a set of optimal delivery sequence, after constructing the mixed-integer linear program (MILP) formulation. Extensive numerical testing indicates that the proposed method performs better than recent exact and heuristic approaches for collaborative delivery routing optimization with the truck carrying multiple drones.", "keywords": [ "Drone", "Computer science", "Pointer (user interface)", "Vehicle routing problem", "Integer programming", "Truck", "Mathematical optimization", "Linear programming", "Network packet", "Distributed computing", "Routing (electronic design automation)", "Computer network" ], "cited_by_count": 19, "is_retracted": false, "is_paratext": false, "open_access_url": "https://mdsoar.org/bitstreams/79a8f351-50ac-4310-b648-6bce572fb5e2/download" }, { "id": "https://openalex.org/W4413308833", "doi": "10.2139/ssrn.5388681", "title": "Optimal Cruise Speeds and Environmental Impacts on Energy use of Unmanned Aerial Vehicles for Last-Mile Delivery", "year": 2025, "publication_date": "2025-01-01", "type": "preprint", "language": "en", "authors": [ "SungHyuk Choi" ], "first_author": "SungHyuk Choi", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Mile", "Cruise", "Last mile (transportation)", "Aeronautics", "Environmental science", "Energy (signal processing)", "Marine engineering", "Transport engineering", "Engineering", "Geography", "Aerospace engineering", "Geodesy" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.5388681" }, { "id": "https://openalex.org/W4408493979", "doi": "10.1016/j.trc.2025.105082", "title": "Integrating battery-related decisions into truck-drone tandem delivery problem with limited battery resources", "year": 2025, "publication_date": "2025-03-16", "type": "article", "language": "en", "authors": [ "Zhongshan Liu", "Bin Yu", "Tingting Chen", "Li Zhang" ], "first_author": "Zhongshan Liu", "venue": "Transportation Research Part C Emerging Technologies", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Truck", "Battery (electricity)", "Tandem", "Drone", "Engineering", "Transport engineering", "Computer science", "Automotive engineering", "Operations research", "Aeronautics", "Aerospace engineering", "Biology" ], "cited_by_count": 24, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.trc.2025.105082" }, { "id": "https://openalex.org/W4293998542", "doi": "10.48550/arxiv.2208.14304", "title": "Approximation Algorithms for Drone Delivery Packing Problem", "year": 2022, "publication_date": "2022-08-30", "type": "preprint", "language": "en", "authors": [ "Saswata Jana", "Partha Sarathi Mandal" ], "first_author": "Saswata Jana", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Recent advancements in unmanned aerial vehicles, also known as drones, have motivated logistics to use drones for multiple operations. Collaboration between drones and trucks in a last-mile delivery system has numerous benefits and reduces a number of challenges. In this paper, we introduce \\textit{drone-delivery packing problem} (DDP), where we have a set of deliveries and respective customers with their prescribed locations, delivery time intervals, associated cost for deliveries, and a set of drones with identical battery budgets. The objective of the DDP is to find an assignment for all deliveries to the drones by using the minimum number of drones subject to the battery budget and compatibility of the assignment of each drone. We prove that DDP is NP-Hard and formulate the integer linear programming (ILP) formulation for it. We proposed two greedy approximation algorithms for DDP. The first algorithm uses at most $2OPT + (Δ+ 1)$ drones. The second algorithm uses at most $2OPT + ω$ drones, where OPT is the optimum solution for DDP, $ω$ is the maximum clique size, and $Δ$ is the maximum degree of the interval graph $G$ constructed from the delivery time intervals.", "keywords": [ "Drone", "Computer science", "Integer programming", "Linear programming", "Mathematical optimization", "Approximation algorithm", "Set (abstract data type)", "Set packing", "Algorithm", "Mathematics" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2208.14304" }, { "id": "https://openalex.org/W7137217880", "doi": "10.3390/drones10030206", "title": "A Literature Review of Vehicle and Drone Delivery Routing Problems in Different Synchronization Level Scenarios", "year": 2026, "publication_date": "2026-03-15", "type": "article", "language": "en", "authors": [ "Jili Kong", "WEI Litong", "Xuefeng Jiang" ], "first_author": "Jili Kong", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "The increasing demand for efficient last-mile delivery has spurred interest in optimizing vehicle and drone routing. This review presents a novel classification of synchronization levels: (i) non-synchronized scenarios, where vehicles and drones operate independently; (ii) low synchronization level scenarios, where one party is passive in the delivery process; (iii) high synchronization level scenarios, where both parties cooperate using diverse strategies. The primary objective is to identify and classify functional preferences of vehicles and drones across these synchronization scenarios. We offer a unique perspective by analyzing the functional setups of vehicles and drones along with synchronization aspects like drone flight synchronization and vehicle synchronization. To the best of our knowledge, these detailed setups based on the operational functionalities of vehicles and drones in last-mile delivery has not been previously explored in the literature. Through a systematic review of the literature, we identify key challenges and emerging trends in vehicle and drone route planning within these scenarios which enable researchers to systematically understand and design vehicle–drone delivery systems. This paper integrates existing models and solution methods and provides new insights into the interactions between vehicle and drone functionalities in last-mile delivery. By analyzing solutions across different synchronization scenarios, it guides researchers in choosing appropriate methodologies and identifying future research directions. Our work presents a novel classification framework, enabling a comprehensive understanding of how the functional setups of vehicles and drones under different synchronization levels influence route planning, thus offering both theoretical and practical insights for advancing last-mile delivery optimization.", "keywords": [ "Drone", "Synchronization (alternating current)", "Key (lock)", "Work (physics)", "Data synchronization" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2504-446X/10/3/206/pdf" }, { "id": "https://openalex.org/W3174690952", "doi": "10.1007/978-3-030-69265-0_7", "title": "A Simulation-Based Evaluation of Drone Integrated Delivery Strategies for Improving Pharmaceutical Service", "year": 2021, "publication_date": "2021-01-01", "type": "book-chapter", "language": "en", "authors": [ "Alexander Jackson", "Sharan Srinivas" ], "first_author": "Alexander Jackson", "venue": "International series in management science/operations research/International series in operations research & management science", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Drone", "Pharmacy", "Service (business)", "Truck", "Service delivery framework", "Medical prescription", "Business", "Last mile (transportation)", "Operations management", "Engineering", "Medicine", "Marketing" ], "cited_by_count": 13, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-030-69265-0_7" }, { "id": "https://openalex.org/W2965232678", "doi": "10.1109/smartcomp.2019.00062", "title": "Exact and Approximate Drone Warehouse for a Mixed Landscape Delivery System", "year": 2019, "publication_date": "2019-06-01", "type": "conference-paper", "language": "en", "authors": [ "Luca Bartoli", "Francesco Betti Sorbelli", "Federico Corò", "Cristina Maria Pinotti", "Anil M. Shende" ], "first_author": "Luca Bartoli", "venue": null, "publisher": null, "abstract": "We introduce a drone delivery system for the \"last-mile\" logistics of small parcels. The system serves mixed delivery areas modeled as EMs. The shortest path between two destinations of an EM concatenates the Euclidean-and Manhattan-distance metrics. The drone's mission consists in one delivery for each destination of the grid, and, due to the strict payload constraint, the drone returns to the warehouse after each delivery. Our goal is to set the drone's warehouse in the delivery area so as the sum of the distances between the locations to be served and the warehouse is minimized. We exactly solve the problem proposing an algorithm that takes logarithmic time in the length of the Euclidean side of the EM. We also devise two approximate solutions that select the warehouse among a constant number of vertices of the EM. Such solutions are almost as good as the exact solution and we prove a √2-approximation bound in the worst case. Finally, we propose an exact solution for the two warehouse problem in a Manhattan grid, and an approximate solution for EMs.", "keywords": [ "Drone", "Computer science", "Grid", "Shortest path problem", "Euclidean geometry", "Mathematical optimization", "Logarithm", "Set (abstract data type)", "Euclidean distance", "Warehouse", "Payload (computing)", "Path (computing)" ], "cited_by_count": 13, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/smartcomp.2019.00062" }, { "id": "https://openalex.org/W4410556134", "doi": "10.1016/j.sftr.2025.100713", "title": "Assessing sustainability of smart last mile delivery: a simulation-based decision support tool", "year": 2025, "publication_date": "2025-05-21", "type": "article", "language": "en", "authors": [ "Maria Grazia Gnoni", "Lorenzo Rubrichi", "Fabiana Tornese" ], "first_author": "Maria Grazia Gnoni", "venue": "Sustainable Futures", "publisher": "Elsevier BV", "abstract": "The increasing demand of e-commerce is forcing economic and environmental inefficiency in last mile logistics (LML). The adoption of smart and autonomous technologies, such as Unmanned Aerial Vehicles (UAVs) and Autonomous Delivery Robots (ADRs), is being evaluated in LML in order to increase its effectiveness. UAVs offer advantages such as faster delivery times and reduced traffic congestion, but face challenges like weather sensitivity and the need for dedicated take-off and landing infrastructure. ADRs can reduce emissions and operational costs compared to traditional LML systems, but their full application is limited mainly due to slower speeds and complex interactions with pedestrians. Despite their limitations, in future years these technologies could be fully applied for LML: thus, evaluating their environmental impact during LML service is necessary to plan their full-scale application. This study proposes a simulation-based decision support tool for assessing the performance of traditional and smart LML technologies according to economic and environmental points of view. By leveraging advanced simulation models, the proposed tool allows to estimate these impacts under varying operational conditions, providing a comprehensive framework for decision-making the LML field by comparing traditional versus innovative LML services. The tool was validated through a case study application in an urban context, demonstrating its ability to highlight the potential benefits and challenges of applying UAVs and ADRs into LML networks. Results indicate that unmanned delivery vehicles allow for a substantial reduction in carbon emissions in the operational phase, confirming their potential as a more environmentally sustainable solution for urban last mile logistics. In addition, the total cost associated with unmanned systems is found to be comparable to that of conventional vehicles, particularly when these latter operate under medium-to-high traffic conditions. Researchers and logistic companies can use this tool to evaluate and optimize the impact of their innovative LML services strategies and achieve improved economic and environmental sustainability levels.", "keywords": [ "Mile", "Last mile (transportation)", "Sustainability", "Decision support system", "Computer science", "Environmental resource management", "Environmental science", "Environmental planning", "Geography", "Data mining" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.sftr.2025.100713" }, { "id": "https://openalex.org/W4400320015", "doi": "10.3990/1.9789036562010", "title": "Autonomous Multimodal Last-Mile Delivery Systems", "year": 2024, "publication_date": "2024-07-04", "type": "dissertation", "language": "en", "authors": [ "Giovanni Campuzano" ], "first_author": "Giovanni Campuzano", "venue": null, "publisher": null, "abstract": "This dissertation explores the intersection of Last-Mile Delivery, Autonomous Vehicles, and Multimodal Transport, collectively referred to as Autonomous Multimodal Last-Mile Delivery. The research applies Operational Research techniques to assess the potential benefits of autonomous vehicles, such as drones and self-driving robots, in overcoming the challenges faced in last-mile delivery. The main objective is to explore how service providers can leverage multimodal delivery systems, integrating trucks, drones, and self-driving robots, to enhance efficiency. This dissertation follows two main directions. The first involves designing delivery systems that address urban last-mile logistics challenges. This includes optimizing interactions between different vehicles and modeling these systems using Mixed-Integer Linear Programs (MILPs). The second direction is developing and analyzing optimization techniques tailored to solve the autonomous multimodal last-mile delivery problems introduced earlier. Through this research, we provide valuable insights into the integration of autonomous vehicles in multimodal delivery systems, offering potential solutions for improving efficiency and addressing the complexities of last-mile delivery in urban environments.", "keywords": [ "Mile", "Last mile (transportation)", "Computer science", "Engineering", "Geography", "Geodesy" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://research.utwente.nl/en/publications/530a7e6b-fac4-4913-8602-4dad0891fe22" }, { "id": "https://openalex.org/W2898590705", "doi": "10.1002/net.21863", "title": "Preface: Special issue on Drone Delivery Systems", "year": 2018, "publication_date": "2018-10-24", "type": "editorial", "language": "en", "authors": [ "Niels A.H. Agatz", "James F. Campbell" ], "first_author": "Niels A.H. Agatz", "venue": "Networks", "publisher": "Wiley", "abstract": "Recent advances in autonomous vehicle technology create new opportunities to increase the efficiency and effectiveness of transportation systems.The use of drones (or unmanned aerial vehicles, UAVs) to support last-mile delivery operations is a topic receiving considerable attention.While there has been much research on the technical aspects of using autonomous drones, as well as significant financial investments in drone logistics systems, the operations research community has only recently started to focus on delivery drones.This special issue brings together state-of-the-art research related to the design and operations of drone delivery systems.It specifically focusses on optimization approaches (exact and heuristic) to support decision-making in these settings.We received 13 manuscripts, which were carefully reviewed through a rigorous refereeing process.Of the manuscripts submitted, six were accepted for publication in this special issue.In \"Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey,\" Otto et al. [5] provide an extensive literature survey on optimization approaches to civil applications of UAVs.The paper describes the most promising aerial drone applications and outlines characteristics of aerial drones relevant to operations planning.It reviews and classifies relevant literature across the key areas of drone operations and highlights novel modeling aspects related to drone use.Two papers consider a setting in which deliveries are performed by either a delivery vehicle or a drone, both departing from a common depot.The vehicles can serve multiple customers on a delivery tour, while the drones are constrained to perform back and forth trips.In \"An iterative two-step heuristic for the Parallel Drone Scheduling Traveling Salesman Problem,\" Saleu et al. [4] present a heuristic solution for the static version of this problem, where the objective is to minimize the completion time.Experiments conducted on benchmark instances show the efficiency of the approach.The paper \"Same-day delivery with a heterogeneous fleet of drones and vehicles\" by Ulmer and Thomas [6] focuses on the dynamic (same-day) version of the problem in which the objective is to maximize the expected number of served customers.To determine whether an order is delivered by a drone or by a vehicle, they present a policy function approximation based on geographical districting, which they show is highly effective.Two papers focus on a setting in which delivery drones are deployed from a regular delivery truck.The drone has to pick up a new parcel from the truck before each new delivery.The paper \"Drone delivery from trucks: Drone scheduling for given truck routes\" by Boysen et al. [2] considers various planning problems that arise in this setting with one or multiple drones.The authors provide an analysis of computational complexity and compare the potential benefits of different operational settings.In \"Dynamic programming approaches for the traveling salesman problem with drone,\" Bouman et al. [1] present exact solution approaches for the setting with one truck and one drone based on dynamic programming and provide an experimental comparison of these approaches.Their numerical experiments show that their solution approaches perform well and that restrictions on the number of locations the truck can visit while the drone is away can significantly reduce the solution times while having little impact on the overall solution quality.The paper \"Drone arc routing problems\" by Campbell et al.[3] presents several drone arc routing problems (Drone ARPs) and studies their relation with well-known postman arc routing problems.The ability of drones to travel directly between any two points in the plane without following the edges of the network leads to new problems and opportunities to reduce costs to serve a set of required edges.Novel problems are presented and solved using an algorithm for the rural postman problem.", "keywords": [ "Erasmus+", "Library science", "SAINT", "Operations research", "Computer science", "Art history", "Art", "Engineering", "The Renaissance" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/net.21863" }, { "id": "https://openalex.org/W4399443282", "doi": "10.1007/978-3-031-61963-2_25", "title": "Towards Sustainable Mobility - Public Acceptance of Automated Last-Mile Deliveries", "year": 2024, "publication_date": "2024-01-01", "type": "conference-paper", "language": "en", "authors": [ "Virpi Oksman", "Minna Kulju" ], "first_author": "Virpi Oksman", "venue": "Communications in computer and information science", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Mile", "Computer science", "Library science", "Geography", "Geodesy" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-031-61963-2_25" }, { "id": "https://openalex.org/W4383335052", "doi": "10.1007/978-3-031-37765-5_10", "title": "Approximation Algorithms for Drone Delivery Scheduling Problem", "year": 2023, "publication_date": "2023-01-01", "type": "conference-paper", "language": "en", "authors": [ "Saswata Jana", "Partha Sarathi Mandal" ], "first_author": "Saswata Jana", "venue": "Lecture notes in computer science", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Drone", "Computer science", "Rounding", "Mathematical optimization", "Approximation algorithm", "Scheduling (production processes)", "Integer programming", "Job shop scheduling", "Polynomial-time approximation scheme", "Algorithm", "Mathematics", "Computer network" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-031-37765-5_10" }, { "id": "https://openalex.org/W2913567047", "doi": "10.1109/rws.2019.8714203", "title": "Micro-UAV Detection with a Low-Grazing Angle Millimeter Wave Radar", "year": 2019, "publication_date": "2019-01-01", "type": "conference-paper", "language": "en", "authors": [ "Martins Ezuma", "Özgür Özdemir", "Chethan Kumar Anjinappa", "Wahab Ali Gulzar", "İsmail Güvenç" ], "first_author": "Martins Ezuma", "venue": null, "publisher": null, "abstract": "Millimeter wave radars are popularly used in last-mile radar-based defense systems. Detection of low-altitude airborne target using these radars at low-grazing angles is an important problem in the field of electronic warfare, which becomes challenging due to the significant effects of clutters in the terrain. This paper provides both experimental and analytical investigation of micro unmanned aerial vehicle (UAV) detection in a rocky terrain using a low grazing angle, surface-sited 24 GHz dual polarized frequency modulated continuous wave (FMCW) radar. The radar backscatter signal from the UAV is polluted by land clutters which is modeled using a uniform Weibull distribution. A constant false alarm rate (CFAR) detector which employs adaptive thresholding is designed to detect the UAV in the rich clutter background. In order to further enhance the discrimination of the UAV from the clutter, the micro-Doppler signature of the rotating propellers and bulk trajectory of the UAV are extracted and plotted in the time-frequency domain.", "keywords": [ "Remote sensing", "Constant false alarm rate", "Extremely high frequency", "Clutter", "Radar", "Terrain", "Continuous-wave radar", "Radar horizon", "Thresholding", "Computer science", "Pulse-Doppler radar", "Weibull distribution" ], "cited_by_count": 54, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/1902.05483" }, { "id": "https://openalex.org/W4405670870", "doi": "10.1016/j.vhri.2024.101066", "title": "Cost-Effectiveness of Aerial Logistics for Immunization: A Model-Based Evaluation of Centralized Storage and Drone Delivery of Vaccines in Ghana Using Empirical Data", "year": 2024, "publication_date": "2024-12-21", "type": "article", "language": "en", "authors": [ "Maria Jose Ospina-Fadul", "Pedro Kremer", "Scott E. Stevens", "Florence Haruna", "Marion Okoh-Owusu", "Godfred Sarpong", "Kingsley Osei-Kwakye", "Billy Joshua", "Osei Sakyi" ], "first_author": "Maria Jose Ospina-Fadul", "venue": "Value in Health Regional Issues", "publisher": "Elsevier BV", "abstract": "OBJECTIVES: In mid-2020, the Ghana Health Service introduced Zipline's aerial logistics (centralized storage and delivery by drones) in the Western North Region to enhance health supply chain resilience. This intervention led to improved vaccination coverage in high-utilization districts. This study assessed the cost-effectiveness of aerial logistics as an intervention to improve immunization coverage. METHODS: An attack rate model, adjusted for vaccination coverage and vaccine efficacy, was used to estimate disease incidence among vaccinated and unvaccinated populations, focusing on 17 022 infants. Incremental cost-effectiveness ratios of US dollar per averted disability-adjusted life-year (DALY) were evaluated from societal and government perspectives, using real-world operations data. Probabilistic sensitivity analysis was performed using Monte Carlo simulations. RESULTS: In 2021, aerial logistics averted 688 disease cases. Incremental cost-effectiveness ratios were $41 and $58 per averted DALY from the societal and government perspectives, respectively. The intervention was cost-saving when at least 20% of vaccines delivered by aerial logistics replaced those that would have been delivered by ground transportation, with potential government savings of up to $250 per averted DALY. Sensitivity analysis confirmed the robustness of these findings. CONCLUSIONS: Under conservative assumptions, aerial logistics was a highly cost-effective intervention to increase immunization coverage. The intervention was cost-saving even with low levels of replacement of traditional last mile delivery. These findings support expanding aerial logistics within the national immunization program and have significant implications for other low- and middle-income countries seeking cost-effective health supply chain solutions.", "keywords": [ "Drone", "Immunization", "Computer science", "Business", "Operations research", "Medicine", "Engineering", "Immunology", "Biology", "Immune system" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.vhri.2024.101066" }, { "id": "https://openalex.org/W3022430566", "doi": "10.1210/jendso/bvaa046.880", "title": "OR30-04 Autonomous Drone Delivery of Insulin", "year": 2020, "publication_date": "2020-04-01", "type": "article", "language": "en", "authors": [ "Derek Timothy O'Keeffe", "Kevin M. Johnson", "Spyridoula Maraka" ], "first_author": "Derek Timothy O'Keeffe", "venue": "Journal of the Endocrine Society", "publisher": "Endocrine Society", "abstract": "Abstract Unmanned aerial vehicles (UAV’s) or drones have become ubiquitous in modern society, predominantly as recreational tools (e.g. racing, photography). However, their use to transport medical products is still nascent, with the best examples seen in emerging economies with underdeveloped infrastructure due to local terrain such as East African jungles or the South Pacific islands. A case in point is the drone operator Zipline, which has pioneered the delivery of blood products in Rwanda since 2016 [1]. Therefore UAV’s have potential in disaster relief operations where there is often significant disruption of health systems [2]. After Ireland experienced Storm Ophelia (Cat 3 Hurricane) in 2017 and then Storm Emma (Winter Blizzard) in 2018, many of our patients with Diabetes had issues with insulin supplies as they remained housebound due to subsequent flooding/snowdrifts. Diabetes Mellitus is one of the world’s most common chronic diseases with approximately 400 million people affected. Insulin is often needed to achieve and maintain glycemic control and therefore is considered a lifesaving medication for patients with diabetes [3]. Consequently, in order to ensure an adequate insulin supply method for patients, after a sentinel weather event, we developed a UAV delivery solution using a vertical take-off and landing (VTOL) Wingcopter 178 drone which we operated under beyond visual line of sight (BVLOS) conditions. After a lengthy planning process, we ensured compliance with all Irish (European) Aviation Aerospace regulations. In addition we complied with regulations surrounding the dispensing of prescribed fridge medications. We had our maiden flight on September 13, 2019 from Galway, Ireland to the Aran Islands (20Km each way) delivering insulin from the pharmacist to the patient’s clinician. This represents the first documented autonomous delivery of insulin for a patient with diabetes. References 1. Ackerman E, Strickland E. Medical delivery drones take flight in east africa. IEEE Spectrum. 2018 Jan 1;55(1):34-5. 2. Rabta B, Wankmüller C, Reiner G. A drone fleet model for last-mile distribution in disaster relief operations. International Journal of Disaster Risk Reduction. 2018 Jun 1;28:107-12. 3. da Rocha Fernandes J, Ogurtsova K, Linnenkamp U, Guariguata L, Seuring T, Zhang P, Cavan D, Makaroff LE. IDF Diabetes Atlas estimates of 2014 global health expenditures on diabetes. Diabetes research and clinical practice. 2016 Jul 1;117:48-54.", "keywords": [ "Drone", "Medicine", "Aeronautics", "Business", "Engineering" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://academic.oup.com/jes/article-pdf/4/Supplement_1/OR30-04/33188173/bvaa046.880.pdf" }, { "id": "https://openalex.org/W4403126829", "doi": "10.1109/wconf61366.2024.10692008", "title": "Optimizing Last Mile Logistics with Machine Learning and Drone Technology in Aerial Solutions", "year": 2024, "publication_date": "2024-07-12", "type": "conference-paper", "language": "en", "authors": [ "Shyamsunder Chitta", "Dankan Gowda V", "Hariprasad Soni", "Madan Jagtap", "D Palanikkumar", "Ibrahim N. Abdullah" ], "first_author": "Shyamsunder Chitta", "venue": null, "publisher": null, "abstract": "The purpose of the present paper is going to focus on one of the most important aspects of last-mile logistics, namely drone technology. It goes further into design of UA Vs in delivery systems, analyzing the present use, the main stakeholders, issues in operation, and trends in the future. This research explores the role of key players such as leading traditional retailers like Amazon and Google as well as emerging innovative companies towards driving the advancement of drone technology for last mile delivery. Furthermore, this paper aims to discuss the new technologies, the current laws, and the customers' readiness to determine the feasibility of the use of the drones as effective, timely, and environmentally friendly last-mile delivery solutions.", "keywords": [ "Drone", "Mile", "Computer science", "Last mile (transportation)", "Aeronautics", "Artificial intelligence", "Engineering", "Geography" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/wconf61366.2024.10692008" }, { "id": "https://openalex.org/W4415399066", "doi": "10.1109/ictle67020.2025.11203681", "title": "Commercial Drone Deliveries: Evolution, Applications, and Market Dynamics", "year": 2025, "publication_date": "2025-08-22", "type": "conference-paper", "language": null, "authors": [ "M. A. Mazur", "Anna Borucka" ], "first_author": "M. A. Mazur", "venue": null, "publisher": null, "abstract": "This article examines the growing use of unmanned aerial vehicles in delivery services, focusing on their potential to transform last-mile logistics. It outlines the advantages of drone deliveries, such as speed, reduced traffic congestion, and lower environmental impact, while also addressing key challenges including regulations, safety concerns, and limited payload capacity. The article provides an overview of current applications, technological developments, and new regulations, offering insights into how drone delivery systems may evolve and be integrated into mainstream logistics in the near future.", "keywords": [ "Drone", "Payload (computing)", "Mainstream", "Key (lock)", "Product (mathematics)" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/ictle67020.2025.11203681" }, { "id": "https://openalex.org/W4401879232", "doi": "10.1109/access.2024.3449283", "title": "Efficient Strategies for Unmanned Aerial Vehicle Flights: Analyzing Battery Life and Operational Performance in Delivery Services Using Stochastic Models", "year": 2024, "publication_date": "2024-01-01", "type": "article", "language": "en", "authors": [ "Francisco Airton Silva", "Vandirleya Barbosa", "Luiz Nelson Lima", "Arthur Sabino", "Paulo A. L. Rêgo", "Luiz F. Bittencourt", "Jae-Woo Lee", "Dugki Min", "Tuấn Anh Nguyễn" ], "first_author": "Francisco Airton Silva", "venue": "IEEE Access", "publisher": "Institute of Electrical and Electronics Engineers", "abstract": "In the rapidly evolving field of autonomous unmanned aerial vehicles (UAVs), commonly referred to as drones, there is burgeoning interest from sectors such as logistics, online retail, and government agencies. The impetus for this interest is largely due to technological progress and innovation. This paper presents a model based on stochastic Petri nets (SPN) for evaluating the operational efficiency of drone delivery systems, with an emphasis on the completion times for tasks executed by either individual or multiple collaborating drones. Recognizing the limitation of drones’ operational duration, chiefly hindered by battery life, the paper suggests the creation of strategic charging stations and the adoption of cooperative delivery tactics as potential solutions, albeit with inherent costs. Two SPN models were developed within this study: one depicting solo drone missions and another depicting cooperative missions with package transfers between drones. These models facilitate the examination of several performance indicators, such as Utilization Level, Mean Mission Time, Delivery Rate, and the probability of delivering a set number of packages within a designated period. By utilizing Design of Experiments (DoE) methods for sensitivity analysis, the study pinpoints essential factors that impact the performance of deliveries and offers strategies for the enhancement of drone delivery networks. The paper concludes by asserting that strategic placement of charging stations coupled with collaborative delivery efforts can effectively mitigate the limitations posed by drones’ battery life, thereby augmenting the overall efficacy and feasibility of drone-based delivery services. The SPN models provide a robust predictive framework for drone delivery performance, aiding in improved planning and allocation of resources. The findings underscore the advantages of cooperative drone operations in urban logistics and call for additional research on the optimization of charging station placement and the refinement of collaborative drone strategies.", "keywords": [ "Computer science", "Battery (electricity)", "Automotive engineering", "Operations research", "Aeronautics", "Engineering" ], "cited_by_count": 10, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/access.2024.3449283" }, { "id": "https://openalex.org/W3044221137", "doi": "10.18104/kaic.2019.34.4.327", "title": "A Study on Last Mile Delivery Strategy in the City", "year": 2019, "publication_date": "2019-12-30", "type": "article", "language": "en", "authors": [ "Jong-Sil Baik" ], "first_author": "Jong-Sil Baik", "venue": "Korean Academy Of International Commerce", "publisher": null, "abstract": "본 논문은 라스트마일 배송의 중요성을 파악하고 도시내 라스트마일 배송 효율화를 위한 방안을 제시하는데 목적이 있다. 전자상거래와 홈쇼핑의 확대로 라스트마일의 중요성이 커지고 있다. 라스트마일 배송을 위하여 전자상거래업체와 유통업체 등이 다양한 기술을 접목하여 연구개발을 추진중이다. 전자상거래 업체와 물류유통업체는 다양한 라스트마일 배송서비스를 제공중이다. 정부와 지방자치단체도 도시내 라스트마일 배송 효율화를 위해 다양한 프로젝트를 추진하는 등 노력하고 있다. 향후 도시물류의 핵심적인 내용은 라스트마일 배송이며, 로봇, 드론, 자율주행차, 무인택배함 등이 널리 활용될 것으로 전망하고 있다. 스마트 시티와 밀접한 관련이 있는 라스트마일 배송의 효율화는 도시의 친환경화, 교통혼잡 완화, 도시물류체계 개선 등을 위해 중요한 과제이다.Purpose : The main purpose of the study is to understand the importance of last mile delivery and to suggest reasonable measures for the efficiency of last mile delivery in the city. Research design, data, methodology : This paper examines the status and cases of last mile delivery of e-commerce and retailer firms, and probes into self-driving robots, drones, unmanned parcels lockers, etc. as technology related to last mile delivery. And I wanted to look at the policies related to last mile delivery at home and abroad countries and suggest ways to streamline last mile delivery in Korea. Results : It is necessary to seek ways to streamline the delivery of last mile in urban areas as demand for last mile in urban areas increases and traffic congestion and air pollution in urban areas increases due to the expansion of e-commerce and upgrading customer needs. The efficiency of last mile delivery is closely related to smart city and is closely related to the efficiency of urban logistics. Conclusions : First, restrictions are placed on city traffic and stops for large and old diesel vehicles. Second, set up a joint logistics center or fulfillment center outside the city, and expand infrastructure to make effective use of urban high-tech logistics complexes. Third, it will actively utilize ecofriendly transportation equipment such as electric vehicles, bicycles and tricycles. Fourth, unmanned parcels lockers are installed and operated in major buildings, apartment complexes, shopping malls, and subway stations.", "keywords": [ "Last mile (transportation)", "Mile", "City logistics", "Transport engineering", "Food delivery", "Business", "Traffic congestion", "Engineering", "Marketing", "Geography" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.18104/kaic.2019.34.4.327" }, { "id": "https://openalex.org/W3045082015", "doi": null, "title": "How Dare You Replace My Deliveryman?! Acceptance of Last-Mile Transportation Innovations – A Qualitative Perspective", "year": 2020, "publication_date": "2020-01-01", "type": "article", "language": "en", "authors": [ "Björn Asdecker" ], "first_author": "Björn Asdecker", "venue": "Journal of the Association for Information Systems", "publisher": "Association for Information Systems", "abstract": "E-commerce continues to grow at double-digit rates. While many e-tailers are presumably delighted with their prospering businesses, this growth also poses challenges. Logistics networks are reaching their capacity limits, which is why firms are looking for new solutions that enable fast, reliable, and cost-efficient delivery. Of special interest is the particularly costly last-mile. Against this background, this paper qualitatively examines which factors influence the acceptance of three last-mile transportation mode innovations, namely, (1) drones, (2) robots, and (3) crowd delivery. To this end, 29 interviews were conducted with e-commerce customers to gain insights into how they view such concepts. Logistics service providers and e-tailers can use the findings to increase acceptance of such solutions by means of targeted communication. The results also form the basis for further research, as they can be used to operationalize the theory of planned behavior (TPB) constructs of “attitude”, “subjective norms”, and “perceived behavioral control”.", "keywords": [ "Mile", "Perspective (graphical)", "Last mile (transportation)", "Computer science", "Transport engineering", "Engineering", "Geography", "Artificial intelligence" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1058&context=amcis2020" }, { "id": "https://openalex.org/W4391449396", "doi": "10.1287/ijoc.2022.0390", "title": "Decremental State-Space Relaxations for the Basic Traveling Salesman Problem with a Drone", "year": 2024, "publication_date": "2024-02-01", "type": "article", "language": "en", "authors": [ "Marcos Blufstein", "Gonzalo Lera‐Romero", "Francisco J. Soulignac" ], "first_author": "Marcos Blufstein", "venue": "INFORMS journal on computing", "publisher": "Institute for Operations Research and the Management Sciences", "abstract": "Truck-and-drone routing problems have become an important research topic in the last decade because of their applications for last-mile deliveries. Despite the many publications in this area, the most efficient exact algorithms designed thus far struggle to solve the benchmark instances with 39 or more customers. This fact holds even for one of the simplest variants involving one truck and one drone whose routes must synchronize at customers’ locations: the basic traveling salesman problem with a drone (TSP-D). In this article, we devise a new algorithm for the TSP-D that solves every benchmark instance with up to 59 customers, and it scales up to 99 customers when the drone is much faster than the truck. The core of our method is a dynamic programming algorithm that is applied for column generation and variable fixing within tailored decremental state-space relaxation strategies. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms–Discrete. Funding: This work was supported by Fondo para la Investigación Científica y Tecnológica [Grant PICT-2018-2961] (Ministry of Science, Argentina). Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijoc.2022.0390 .", "keywords": [ "Travelling salesman problem", "Drone", "Traveling purchaser problem", "2-opt", "State space", "Mathematical optimization", "Bottleneck traveling salesman problem", "Space (punctuation)", "Computer science", "State (computer science)", "Mathematics", "Algorithm" ], "cited_by_count": 16, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1287/ijoc.2022.0390" }, { "id": "https://openalex.org/W4406828789", "doi": "10.18267/j.aip.257", "title": "Optimization in Routing and Vehicle Selection for E-commerce Last Mile Logistics: Bibliometric Analysis", "year": 2025, "publication_date": "2025-01-26", "type": "article", "language": "en", "authors": [ "Sari Andarwati Kunharyanto", "Ratna Mayasari", "Dili Oktaviana" ], "first_author": "Sari Andarwati Kunharyanto", "venue": "Acta Informatica Pragensia", "publisher": "Prague University of Economics and Business", "abstract": "Background: The study investigates the emerging concept of Last Mile Logistics (LML), focusing on optimization routing for e-commerce deliveries.This research addresses the growing demand for efficient delivery systems driven by the rise of online shopping and technological advancements.Objective: This article aims to analyse existing literature, identify trends and highlight gaps in LML research.Methods: A systematic approach was used for the selection of studies and then bibliometric analysis was used to map and evaluate relevant studies.The Scopus citation database was used to gather peerreviewed articles from 1999 to 2023.R-Studio and VOS Viewer were employed for data analysis and visualization.The initial search yielded 3,898 articles, which were filtered down to 1,606 articles.Key findings include the identification of the most productive journals, top-cited articles and influential keywords in LML research. Results:The study significantly emphasizes variables such as distance, costs, energy consumption and delivery time in logistics optimization.E-commerce emerged as a predominant topic, with substantial discussions on technology and transportation within LML.The research underscores the critical role of LML in the success of e-commerce.Conclusion: The findings highlight the need for ongoing innovation and optimization in delivery logistics to enhance efficiency and meet consumer demands.Future research should address the gaps identified, particularly in the context of emerging technologies such as autonomous vehicles and drones, to further improve last mile delivery systems.", "keywords": [ "Vehicle routing problem", "Selection (genetic algorithm)", "Last mile (transportation)", "Routing (electronic design automation)", "Computer science", "Mile", "City logistics", "Operations research", "Business", "Transport engineering", "Engineering", "Computer network" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://aip.vse.cz/doi/10.18267/j.aip.257.pdf" }, { "id": "https://openalex.org/W4391989784", "doi": "10.1299/jamdsm.2024jamdsm0021", "title": "Drone scheduling for parcel delivery with an access grade to stops on a fixed truck route", "year": 2024, "publication_date": "2024-01-01", "type": "article", "language": "en", "authors": [ "Kotaro YAMADA", "Yoshiyuki Karuno", "Riki KATAOKA", "Sota SAWADA" ], "first_author": "Kotaro YAMADA", "venue": "Journal of Advanced Mechanical Design Systems and Manufacturing", "publisher": "Japan Society Mechanical Engineers", "abstract": "In this paper, we consider a parcel delivery scheduling problem of minimizing the total duration for a truck-drone distribution system to serve a given set of customers. The single carrier truck plays the role of a mobile depot of multiple identical drones, and the drones with unit capacity perform the last-mile delivery from the carrier truck to each customer. An ordered set of truck stops for launching/retrieving drones is also given, and it constitutes a fixed route of the carrier truck. For the last-mile delivery of a drone to a customer, the launching stop and the retrieving stop of the drone must be the same. We newly introduce an integral parameter of access grade to an underlying parcel delivery model in order to obtain a flight time bound for each customer. By the flight time bound as a threshold, the given set of truck stops is partitioned into two disjoint subsets, namely sets of nearer stops and of farther stops. The carrier truck is required to choose a stop from the set of nearer stops for launching a drone to each customer. The access grade of nearer stops yields a generalized parcel delivery model, since it involves the case with a sufficiently large bound on the flight time as the underlying parcel delivery model with no access restriction to truck stops. In this paper, we first review an integer programming (IP) formulation of the underlying parcel delivery model, and then we modify it into the generalized version with the access grade of nearer stops. We also conduct numerical experiments to demonstrate the solution quality of the generalized version by utilizing an IP solver, and report the results.", "keywords": [ "Drone", "Truck", "Last mile (transportation)", "Disjoint sets", "Scheduling (production processes)", "Computer science", "Delivery Performance", "Set (abstract data type)", "Integer programming", "Transport engineering", "Operations research", "Computer network" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.jstage.jst.go.jp/article/jamdsm/18/2/18_2024jamdsm0021/_pdf" }, { "id": "https://openalex.org/W4386361533", "doi": "10.1109/icccn58024.2023.10230089", "title": "Environmentally-Aware Robotic Vehicle Networks Routing Computation for Last-mile Deliveries", "year": 2023, "publication_date": "2023-07-01", "type": "conference-paper", "language": "en", "authors": [ "Chengyi Qu", "Rounak Singh", "Sharan Srinivas", "Prasad P. Calyam" ], "first_author": "Chengyi Qu", "venue": null, "publisher": null, "abstract": "For next-generation logistics management, robotic vehicles such as autonomous ground robots and aerial drones can alleviate the strain on last-mile distribution. They can help avoid on-road congestion, navigate hard-to-reach locations, and parallelize delivery operations. However, as the robotic vehicles move in a given delivery area, environmental barriers e.g., trees or buildings, affect air-to-air (A2A), air-to-ground (A2G), ground-to-ground (G2G) network communications on a hybrid truck-drone-robot system. In this paper, we present an environmentally-aware cooperative network routing computation scheme to avoid obstacle blockage in A2A/A2G/G2G network communications for addressing large-scale coordinated operations of the hybrid truck-drone-robot system. Specifically, we propose an offline policy-based routing algorithm and two online extensions (i.e., heuristics and learning-based) to solve the hybrid last-mile delivery vehicles communication problem in order to trade-off between end-to-end communication (i.e., increase network throughput) and delivery efficiencies (i.e., lower parcel delivery time consumption). We evaluate our scheme using state-of-the-art network routing algorithms in a trace-based simulator that integrates both the vehicles and networking sides. Performance evaluation results from our simulations show that: (i) our offline approach is Pareto-optimal among non-learning supported algorithms in a pre-delivery scenario, and (ii) our RL-based online algorithm achieves between 85–96 % of the Oracle strategy performance during delivery procedures.", "keywords": [ "Computer science", "Drone", "Heuristics", "Throughput", "Truck", "Routing (electronic design automation)", "Last mile (transportation)", "Robot", "Vehicle routing problem", "Computation offloading", "Real-time computing", "Distributed computing" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/icccn58024.2023.10230089" }, { "id": "https://openalex.org/W4410601814", "doi": "10.34047/sj/20251102", "title": "The Role of Artificial Intelligence in Last Mile Delivery", "year": 2025, "publication_date": "2025-01-01", "type": "article", "language": "en", "authors": [ "Hirendra M. Soni", "Rajesh Chouksey" ], "first_author": "Hirendra M. Soni", "venue": "MET Srujan", "publisher": null, "abstract": "The emergence of artificial intelligence (AI) offers possibilities to transform last mile delivery operations. It offers various solutions to long-standing challenges in logistics while meeting the increasing demands of consumers. The paper presents a review of the role directly played by AI in the optimisation of last-mile delivery, analysed from the studies available, major applications, benefits, challenges, and futuristic trends. This paper further describes how AI enables predictive analytics through demand forecasting, algorithms for route optimisation, modes of last mile delivery involving autonomous vehicles and drones, customer communication systems, and inventory management. Case studies of the industry leaders like Amazon, FedEx, and DHL, have indicated some salient benefits namely cost reduction, enhanced customer satisfaction, and improved efficiency. However, challenges persist, such as data security, high costs of implementation, and difficulties in having the same rules and regulations met by individual companies. The paper points to the possibility of various developing technologies, including federated learning and edge AI, that can be looked at as possible solutions. There are also suggestions in relation to policy formulation and strategies which companies in last-mile logistics can update to foster the adoption of AI. Noteworthy points are also contained for industry players and policy makers, related to leveraging AI and the attendant challenges, to drive effective transformation of last mile delivery.", "keywords": [ "Mile", "Computer science", "Geography", "Geodesy" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://metsrujan.com/index.php/description/article/download/9/9/31" }, { "id": "https://openalex.org/W3203515346", "doi": "10.1541/ieejeiss.141.1175", "title": "A Parcel Delivery Method by Combining Vehicle and Drone", "year": 2021, "publication_date": "2021-10-31", "type": "article", "language": "en", "authors": [ "Satoru Hori", "Norihisa Komoda", "Takenao Ohkawa" ], "first_author": "Satoru Hori", "venue": "IEEJ Transactions on Electronics Information and Systems", "publisher": null, "abstract": "A parcel delivery method combining a vehicle and a drone is proposed. In a parcel delivery business where delivery time windows exist, the delivery order is determined by considering both keeping the delivery time windows restriction and shorting the total delivery time. Additionally, it is necessary to plan for the vehicle to visit a drone airfield and to load/reload the drone, so that the waiting time of both the vehicle and the drone is reduced. To solve this problem, firstly, an initial plan is greedily searched. Then, the initial plan is successively improved. Through simulation evaluation, it is confirmed that the proposed method can reduce delivery time compared to a method where only the delivery time windows are considered and where the vehicle visits the drone airfield on a priority basis.", "keywords": [ "Drone", "Computer science", "Plan (archaeology)", "Delivery system", "Simulation", "Real-time computing", "Engineering", "Geography" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1541/ieejeiss.141.1175" }, { "id": "https://openalex.org/W7129105495", "doi": "10.1016/j.tre.2026.104741", "title": "Routing optimization for an eVTOL-and-drone delivery system in continuous space with no-fly zones: A reinforcement learning approach", "year": 2026, "publication_date": "2026-02-16", "type": "article", "language": "en", "authors": [ "Shaojun Liu", "Yitong Yu", "Qingyun Tian", "Han Sun" ], "first_author": "Shaojun Liu", "venue": "Transportation Research Part E Logistics and Transportation Review", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Reinforcement learning", "Drone", "Scalability", "Scheduling (production processes)", "Trajectory", "Trajectory optimization", "Routing (electronic design automation)" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.tre.2026.104741" }, { "id": "https://openalex.org/W4398181859", "doi": "10.1016/j.elerap.2024.101411", "title": "Optimization of hybrid delivery by vehicle and drones", "year": 2024, "publication_date": "2024-05-21", "type": "article", "language": "en", "authors": [ "Qiuchen Gu", "Tijun Fan", "Wanke Han" ], "first_author": "Qiuchen Gu", "venue": "Electronic Commerce Research and Applications", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Computer science", "Computer security" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.elerap.2024.101411" }, { "id": "https://openalex.org/W2979415718", "doi": "10.1201/9780367824037-20", "title": "Vehicle routing in drone-based package delivery services", "year": 2019, "publication_date": "2019-09-19", "type": "book-chapter", "language": "en", "authors": [ "Anita Agárdi", "László Kovács", "Tamás Bányai" ], "first_author": "Anita Agárdi", "venue": null, "publisher": null, "abstract": "The Industry 4.0 enables the operation of new logistics equipment and new logistic solutions based on cyber-physical systems. The usage of new transport devices is discussed in this article in case of the Vehicle Routing Problem. In basic Vehicle Routing Problems, the demands of customers must be served. This article discusses a new Vehicle Routing Problem where drones are also used to improve the flexibility, reliability and cost efficiency of the whole last-mile solution. During the problem, a drone is assigned to the truck performing package delivery services, and some customers are visited by the truck while the drone is sent to certain customers located in the near environment of the truck. The drone has a capacity limit, so it is able to visit only a limited number of customers. After visiting the customers, the drone recovers the goods from the truck and serves the demands of new customers. In this article, the above mentioned problem is solved with construction and improvement algorithms. The construction algorithms are the Nearest Neighbour Algorithm and Arbitrary Insertion Algorithm, the improvement algorithms are the Genetic Algorithm and the Hill Climbing Algorithm.", "keywords": [ "Drone", "Routing (electronic design automation)", "Computer science", "Computer network", "Biology" ], "cited_by_count": 12, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1201/9780367824037-20" }, { "id": "https://openalex.org/W4210854451", "doi": "10.1109/iaecst54258.2021.9695662", "title": "Research on Path Planning and Evaluation Method of Urban Logistics UAV", "year": 2021, "publication_date": "2021-12-10", "type": "conference-paper", "language": "en", "authors": [ "Bowen Li", "Honghai Zhang", "Liandong Zhang", "Dikun Feng", "Yuhan Fei" ], "first_author": "Bowen Li", "venue": "2021 3rd International Academic Exchange Conference on Science and Technology Innovation (IAECST)", "publisher": null, "abstract": "Aiming at the problem of path planning and evaluation of logistics UAV (Unmanned Aerial Vehicle) in urban area, the paper plans the flight path of logistics UAV by using the improved A-star algorithm. The improved A-star algorithm considers urban airspace restrictions and UAV performance constraints from the perspectives of delivery efficiency and cost. At the same time, the paper deeply analyzes the influencing factors of path evaluation from four aspects. These four aspects are the external environment, route conditions, logistics distribution, and urban influence. Then, the paper constructs the path evaluation index system according to the characteristics of the urban airspace. Finally, the planned path is evaluated with AHP (the Analytic Hierarchy Process)-entropy method and fuzzy comprehensive evaluation method. The simulation results show that the improved A-star algorithm designed in this paper can quickly plan the feasible path. And the path evaluation model can evaluate the flight path of logistics UAV objectively and effectively.", "keywords": [ "Analytic hierarchy process", "Motion planning", "Computer science", "Path (computing)", "Plan (archaeology)", "Operations research", "Process (computing)", "Fuzzy logic", "Transport engineering", "Engineering", "Artificial intelligence", "Robot" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/iaecst54258.2021.9695662" }, { "id": "https://openalex.org/W4388473693", "doi": "10.1016/j.trc.2023.104401", "title": "Reinforcement learning for humanitarian relief distribution with trucks and UAVs under travel time uncertainty", "year": 2023, "publication_date": "2023-11-07", "type": "article", "language": "en", "authors": [ "Robert van Steenbergen", "Martijn Mes", "Wouter van Heeswijk" ], "first_author": "Robert van Steenbergen", "venue": "Transportation Research Part C Emerging Technologies", "publisher": "Elsevier BV", "abstract": "Effective humanitarian relief operations are challenging in the aftermath of disasters, as trucks are often faced with considerable travel time uncertainties due to damaged transportation networks. Efficient deployment of Unmanned Aerial Vehicles (UAVs) potentially mitigates this problem, supplementing truck fleets in an impactful manner. To plan last-mile relief distribution in this setting, we introduce a multi-trip, split-delivery vehicle routing problem with trucks and UAVs, soft time windows, and stochastic travel times for last-mile relief distribution, formulated as a stochastic dynamic program. Within a finite time horizon, we aim to maximize a weighted objective function comprising the number of goods delivered, the number of different locations visited, and late arrival penalties. Our study offers insights into dealing with travel time uncertainty in humanitarian logistics by (i) deploying Unmanned Aerial Vehicles (UAVs) as partial substitutes for trucks, (ii) evaluating dynamic solutions generated by two deep reinforcement learning (RL) approaches – specifically value function approximation (VFA) and policy function approximation (PFA) – and (iii) comparing the RL solutions with solutions stemming from mathematical programming and dynamic heuristics. Experiments are performed on both Solomon-based instances and two real-world cases. The real-world cases – the 2015 Nepal earthquake and the 2018 Indonesia tsunami – are based on locally collected field data and real-world UAV specifications, and aim to provide practical insights. The experimental results show that dynamic decision-making improves both performance and robustness of humanitarian operations, achieving reductions in lateness penalties of around 85% compared to static solutions based on expected travel times. Furthermore, the results show that replacing half of the trucks with UAVs improves the weighted objective value by 11% to 56%, benefitting both reliability and location coverage. The results indicate that both the deployment of UAVs and the use of dynamic methods successfully mitigate travel time uncertainties in humanitarian operations.", "keywords": [ "Truck", "Heuristics", "Reinforcement learning", "Computer science", "Operations research", "Time horizon", "Dynamic programming", "Vehicle routing problem", "Robustness (evolution)", "Mathematical optimization", "Transport engineering", "Routing (electronic design automation)" ], "cited_by_count": 54, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.trc.2023.104401" }, { "id": "https://openalex.org/W4403108993", "doi": "10.1080/17441692.2024.2405987", "title": "Digital entanglements: Medical drones in African healthcare systems", "year": 2024, "publication_date": "2024-10-02", "type": "article", "language": "en", "authors": [ "Edwin Ambani Ameso" ], "first_author": "Edwin Ambani Ameso", "venue": "Global Public Health", "publisher": "Taylor & Francis", "abstract": "The intensified scramble for the digitalisation of healthcare across Africa, coupled with the general drive for digital economies, has ushered in digital health innovations that are reconfiguring national discourses on humanitarian and development contexts. Through these innovations, imaginaries of health have become entangled with aspirations for universal health coverage (UHC) and the actualisation of the health-related sustainable development goals (SDGs). Among these innovations, drones promise to leapfrog and transform conventional African healthcare systems, which have suffered from structural bottlenecks for years, offering citizens on the margins of care critical biomedical gazes. By using drones, African states hope to improve revenue collection, curb corruption, redress health insecurities and deliver life-saving medicines, vaccines and laboratory diagnostics through a last-mile distribution schedule. Ethnographic fieldwork from 2022 to 2023 in Ghana and Malawi on the use of drones found distortions to the health workforce, disruptions to health work, and a pervasive internal brain drain, all exacerbating health-worker shortages. This paper explores how drones are reconfiguring health work and its available labour force in practice amid persistent shortages of health-workers.", "keywords": [ "Drone", "Health care", "Healthcare system", "Internet privacy", "Computer science", "Economic growth", "Economics", "Biology" ], "cited_by_count": 12, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.tandfonline.com/doi/pdf/10.1080/17441692.2024.2405987?needAccess=true" }, { "id": "https://openalex.org/W4224951819", "doi": "10.3390/s22093321", "title": "Research Trends in Collaborative Drones", "year": 2022, "publication_date": "2022-04-26", "type": "article", "language": "en", "authors": [ "Michel Barbeau", "Joaquín García-Alfaro", "Evangelos Kranakis" ], "first_author": "Michel Barbeau", "venue": "Sensors", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "The last decade has seen an explosion of interest in drones-introducing new networking technologies, such as 5G wireless connectivity and cloud computing. The resulting advancements in communication capabilities are already expanding the ubiquitous role of drones as primary solution enablers, from search and rescue missions to information gathering and parcel delivery. Their numerous applications encompass all aspects of everyday life. Our focus is on networked and collaborative drones. The available research literature on this topic is vast. No single survey article could do justice to all critical issues. Our goal in this article is not to cover everything and include everybody but rather to offer a personal perspective on a few selected research topics that might lead to fruitful future investigations that could play an essential role in developing drone technologies. The topics we address include distributed computing with drones for the management of anonymity, countering threats posed by drones, target recognition, navigation under uncertainty, risk avoidance, and cellular technologies. Our approach is selective. Every topic includes an explanation of the problem, a discussion of a potential research methodology, and ideas for future research.", "keywords": [ "Drone", "Open research", "Computer science", "Anonymity", "Cloud computing", "Data science", "Perspective (graphical)", "Computer security", "World Wide Web", "Artificial intelligence" ], "cited_by_count": 9, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/1424-8220/22/9/3321/pdf?version=1651148913" }, { "id": "https://openalex.org/W4386157309", "doi": "10.36501/0197-9191/23-014", "title": "Analysis of Drone-based Last-mile Delivery Systems under Aerial Congestion: A Continuum Approximation Approach", "year": 2023, "publication_date": "2023-08-01", "type": "report", "language": "en", "authors": [ "Ruifeng She", "Yanfeng Ouyang" ], "first_author": "Ruifeng She", "venue": null, "publisher": null, "abstract": "This paper presents a systematic analysis and design framework for a spectrum of last-mile delivery systems that leverage unmanned aerial vehicles (UAVs). Four distinct modes are considered: (1) direct drone deliveries from a fixed depot; (2) drone deliveries from parked trucks that carry bulk parcels to customer neighborhoods; (3) drone deliveries from nonstopping trucks that tour customer neighborhoods; and (4) as a benchmark, traditional truck-based home deliveries. We present a new continuum-approximation approach that is used for analysis of both truck routing and aerial-UAV traffic. We compared the operational cost and efficiency of different delivery schemes to reveal how a certain scheme is the most efficient under various scenarios. We demonstrate the applicability of our model on expansive real-world roadway networks and further conduct analysis on grid networks, yielding key analytical insights. The drone-based delivery is demonstrated to be superior to conventional truck-only delivery, suggesting a significant potential for socioeconomic benefit. It is observed that when servicing a relatively low demand over a small area, dispatching drones directly from the distribution facility is the most efficient method. As the demand grows or spans a wider area, collaborative strategies are preferred, as they better cope with certain aspects, such as the long line-haul cost or the ramping aerial congestion, by striking a balance between efficiency and flexibility.", "keywords": [ "Drone", "Truck", "Last mile (transportation)", "Computer science", "Transport engineering", "Grid", "Operations research", "Computer network", "Mile", "Engineering", "Geography", "Automotive engineering" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.36501/0197-9191/23-014" }, { "id": "https://openalex.org/W4396763273", "doi": "10.1002/rob.22359", "title": "Energy‐consumption model for rotary‐wing drones", "year": 2024, "publication_date": "2024-05-08", "type": "article", "language": "en", "authors": [ "Hongqi Li", "Zhuopeng Zhan", "Zhiqi Wang" ], "first_author": "Hongqi Li", "venue": "Journal of Field Robotics", "publisher": "Wiley", "abstract": "Abstract With technological advancement, the use of drones in delivery systems has become increasingly feasible. Many companies have developed rotary‐wing drone (RWD) technologies for parcel delivery. At present, the limited endurance is the main disadvantage of RWD delivery. The energy consumption of RWDs must be carefully managed, and it is necessary to develop an effective energy‐consumption model to support RWD flight planning. Because the interaction between the forces on the RWD and its flying environment is very complex, it is challenging to estimate accurately the RWD energy consumption. This study summarizes several energy‐consumption models proposed in the literature, then we develop an RWD energy‐consumption model (called the integrated model) based on analyzing the dynamic equilibrium of forces and power consumption in flight phases (including climb, descent, hover, and horizontal flight). Computational experiments involving several commercial RWDs indicate that the integrated model is more effective than several models in the literature. In the case where an RWD completed one flight segment, on average, 87.63% of the battery capacity was consumed in the horizontal flight phase. We also analyzed the effects of the total mass and horizontal airspeed on the RWD endurance and found that a larger mass corresponded to shorter endurance, and in the experimental range of the horizontal airspeed, a higher horizontal airspeed corresponded to longer endurance. Moreover, the total mass affected the RWD endurance more significantly than the horizontal airspeed.", "keywords": [ "Airspeed", "Energy consumption", "Climb", "Range (aeronautics)", "Environmental science", "Computer science", "Automotive engineering", "Simulation", "Aerospace engineering", "Engineering" ], "cited_by_count": 12, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1002/rob.22359" }, { "id": "https://openalex.org/W3160211367", "doi": "10.21595/vp.2021.21988", "title": "Modelling and mitigation of drone noise", "year": 2021, "publication_date": "2021-05-07", "type": "article", "language": "en", "authors": [ "John F. Kennedy", "Simone Garruccio", "Kai Cussen" ], "first_author": "John F. Kennedy", "venue": "Vibroengineering PROCEDIA", "publisher": "JVE International", "abstract": "According to studies by Airbus, one of the biggest concerns among the public regarding drones is the noise they produce. ICAO have also stated that when drones are flown “low and in great numbers” they will cause a significant level of noise. To date there is literature around the public’s perception of drone usage and noise, however there is currently no recommended software package for modelling drone noise. There has also been little work done into the level of noise that is likely to be emitted by drones used in the commercial market for tasks such as parcel delivery. This research aimed to investigate the noise emitted by drones and determine suitable operating conditions and flight paths to mitigate this noise. The research also aimed to investigate the suitability of the software package ‘iNoise’ for modelling noise emission by drones. The results showed noise emission can be mitigated by maintaining high altitudes and high speeds, as well as vertical take offs and landings. Although many of the noise levels recorded in this research for different flight conditions and flow numbers were lower than the maximum recommended L DEN for road traffic noise, they did approach this value at low speeds. As the sound power level used in this project came from a small quadcopter, it is reasonable to assume larger, commercial drones will have a higher sound power level, thus increasing the need for noise mitigation measures. The research also demonstrated the suitability of iNoise for modelling noise emissions caused by drone activity.", "keywords": [ "Drone", "Noise (video)", "Quadcopter", "Noise control", "Roadway noise", "Computer science", "Aircraft noise", "Noise pollution", "Environmental science", "Noise reduction", "Aeronautics", "Engineering" ], "cited_by_count": 9, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.extrica.com/article/21988/pdf" }, { "id": "https://openalex.org/W4382651503", "doi": "10.1007/s10287-023-00453-3", "title": "Enabling same-day delivery using a drone resupply model with transshipment points", "year": 2023, "publication_date": "2023-05-08", "type": "article", "language": "en", "authors": [ "Mohammad Moshref‐Javadi", "Kristof P. Van Cauwenberghe", "Brent A. McCunney", "Ahmad Hemmati" ], "first_author": "Mohammad Moshref‐Javadi", "venue": "Computational Management Science", "publisher": "Springer Science+Business Media", "abstract": "Abstract We study a hybrid logistics delivery model which incorporates trucks and drones. In this model, named the Drone Resupply Model with Transshipment Points (DRMTP), drones are used to resupply transshipment points where trucks visit to pick up packages for delivery to customers. A simulation model is developed to simulate the delivery operations. The model is evaluated in two case studies in an urban area and a suburban area in Massachusetts, USA. Using this resupply model, the average delivery time is reduced compared to the conventional truck-only delivery model. The experimental results in the suburban region show that the DRMTP model with one transshipment point achieves 36% lower delivery time compared to the truck-only model. In the urban region, the model with four transshipment points obtains 66% delivery time reduction compared to the truck-only model. The results indicate that the proposed model can be used to enhance the efficiency of the last-mile delivery operations.", "keywords": [ "Transshipment (information security)", "Truck", "Drone", "Computer science", "Delivery Performance", "Operations research", "Point (geometry)", "Location model", "Transport engineering", "Simulation", "Engineering", "Automotive engineering" ], "cited_by_count": 16, "is_retracted": false, "is_paratext": false, "open_access_url": "https://link.springer.com/content/pdf/10.1007/s10287-023-00453-3.pdf" }, { "id": "https://openalex.org/W4412584916", "doi": "10.1016/j.ejor.2025.07.027", "title": "Exact solution approaches for the traveling salesman problem with a drone station", "year": 2025, "publication_date": "2025-07-23", "type": "article", "language": "en", "authors": [ "Zhiyuan Shi", "Shaozhi Hong", "Zeling Wang", "Ang Li" ], "first_author": "Zhiyuan Shi", "venue": "European Journal of Operational Research", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Travelling salesman problem", "Drone", "Traveling purchaser problem", "Computer science", "Mathematical optimization", "Bottleneck traveling salesman problem", "2-opt", "Operations research", "Mathematics", "Biology" ], "cited_by_count": 12, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.ejor.2025.07.027" }, { "id": "https://openalex.org/W2940605362", "doi": null, "title": "Optimization of Drone-Assisted Delivery System", "year": 2018, "publication_date": "2018-01-01", "type": "article", "language": "en", "authors": [ "Lanyi Liu" ], "first_author": "Lanyi Liu", "venue": "Open Scholarship Institutional Repository (Washington University in St. Louis)", "publisher": "Washington University in St. Louis", "abstract": "Recently, a new parcel delivery method has been emerged, which involves Unmanned Aerial Vehicles, also known as drones, assisting traditional trucks in last-mile delivery across logistic networks. This method generally combines a truck and one drone or more to handle the delivery processes to customers. And this paper tends to calculate the optimal route of this drone-assisted delivery system, the objective is to minimize the operational time and energy cost. I developed a heuristic solution approach which implements an effective Genetic Algorithm to solve and simulate this delivery system of practical size, this program can automatically generate the on-screen best route results and show the total operational time and energy cost.", "keywords": [ "Drone", "Computer science", "Delivery system", "Medicine", "Biology", "Biomedical engineering" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "https://openscholarship.wustl.edu/mems5001/3" }, { "id": "https://openalex.org/W3170658350", "doi": null, "title": "Optimizing Satellite Locations for a Multi-Echelon Last Mile Distribution Network to utilize Alternative Delivery Vehicles for Last Mile Delivery", "year": 2020, "publication_date": "2020-08-06", "type": "other", "language": "en", "authors": [ "Abhinav Goyal" ], "first_author": "Abhinav Goyal", "venue": "DSpace@MIT (Massachusetts Institute of Technology)", "publisher": "Massachusetts Institute of Technology", "abstract": "The growing urban population over the past few years has created many challenges for last mile distribution, such as traffic congestion, pollution, and lack of parking space availability. With the advent of e-commerce industry, the volumes for last mile delivery are growing consistently. As firms struggle to provide competitive delivery commitments to the end customers, they are exploring alternative delivery methods, such as drones and e-cargo bikes to navigate urban areas efficiently while addressing pollution and traffic concerns. However, range and capacity constraints associated with such alternative delivery modes restrict the operations that can be carried out with such vehicles. Hence, firms are re-designing their last mile distribution strategies to adapt to the constraints posed by these delivery modes. One such strategy is to deploy a multi-echelon distribution network, using satellite nodes near customer locations that allow for transshipment. The large conventional trucks deliver parcels to a satellite, from whereon the parcels are cross-docked into lighter vehicles (such as e-cargo bikes) which perform the final delivery. This project introduces a mixed integer linear programming model for a two-echelon delivery network, to determine the optimal count and locations of satellites for a large parcel company. The transactional data for deliveries and pickups associated with one parcel center has been used to develop and test the model. The first-tier transportation, that is from parcel center to satellites, has been designed as a location routing problem. The second-tier transportation, that is from satellites to customer delivery points, has been designed as an allocation problem. The model tries to minimize the associated costs with the satellite operation, optimizing the fixed cost of establishing a satellite against the cost of distance travelled and transit time. Traffic considerations and road network distances have been accounted for by using real road network data for distance calculations, and transit times adjusted for traffic conditions across multiple hours during the day. The model returns the count of satellites to be established, along with their respective locations and vehicle routes for first tier transportation. Finally, the model maps all the customers to their respective satellites to achieve an optimum distribution cost.", "keywords": [ "Mile", "Last mile (transportation)", "Computer science", "Satellite", "Telecommunications", "Engineering", "Geography", "Geodesy" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://hdl.handle.net/1721.1/126489" }, { "id": "https://openalex.org/W4402992264", "doi": "10.1016/j.ipm.2024.103888", "title": "Crowdsourced auction-based framework for time-critical and budget-constrained last mile delivery", "year": 2024, "publication_date": "2024-09-30", "type": "article", "language": "en", "authors": [ "Esraa Odeh", "Shakti Singh", "Rabeb Mizouni", "Hadi Otrok" ], "first_author": "Esraa Odeh", "venue": "Information Processing & Management", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Mile", "Last mile (transportation)", "Computer science", "Operations research", "Engineering", "Geography" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.ipm.2024.103888" }, { "id": "https://openalex.org/W4286376539", "doi": "10.2139/ssrn.4167190", "title": "Drone-Assisted Last-Mile Delivery Problem by Covering Salesman Problem with Nodes and Segments", "year": 2022, "publication_date": "2022-01-01", "type": "preprint", "language": "en", "authors": [ "Hosang Song", "Abdullahi Mohammed Jingi", "Xinan Yang" ], "first_author": "Hosang Song", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Travelling salesman problem", "Mile", "Drone", "Last mile (transportation)", "Computer science", "Mathematical optimization", "2-opt", "Mathematics", "Geography", "Biology", "Geodesy" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.4167190" }, { "id": "https://openalex.org/W4403516281", "doi": "10.3390/s24206698", "title": "Energy-Saving Multi-Agent Deep Reinforcement Learning Algorithm for Drone Routing Problem", "year": 2024, "publication_date": "2024-10-18", "type": "article", "language": "en", "authors": [ "Xiu Shu", "Anping Lin", "Xupeng Wen" ], "first_author": "Xiu Shu", "venue": "Sensors", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "With the rapid advancement of drone technology, the efficient distribution of drones has garnered significant attention. Central to this discourse is the energy consumption of drones, a critical metric for assessing energy-efficient distribution strategies. Accordingly, this study delves into the energy consumption factors affecting drone distribution. A primary challenge in drone distribution lies in devising optimal, energy-efficient routes for drones. However, traditional routing algorithms, predominantly heuristic-based, exhibit certain limitations. These algorithms often rely on heuristic rules and expert knowledge, which can constrain their ability to escape local optima. Motivated by these shortcomings, we propose a novel multi-agent deep reinforcement learning algorithm that integrates a drone energy consumption model, namely EMADRL. The EMADRL algorithm first formulates the drone routing problem within a multi-agent reinforcement learning framework. It subsequently designs a strategy network model comprising multiple agent networks, tailored to address the node adjacency and masking complexities typical of multi-depot vehicle routing problem. Training utilizes strategy gradient algorithms and attention mechanisms. Furthermore, local and sampling search strategies are introduced to enhance solution quality. Extensive experimentation demonstrates that EMADRL consistently achieves high-quality solutions swiftly. A comparative analysis against contemporary algorithms reveals EMADRL's superior energy efficiency, with average energy savings of 5.96% and maximum savings reaching 12.45%. Thus, this approach offers a promising new avenue for optimizing energy consumption in last-mile distribution scenarios.", "keywords": [ "Reinforcement learning", "Drone", "Computer science", "Routing (electronic design automation)", "Energy (signal processing)", "Reinforcement", "Artificial intelligence", "Mathematical optimization", "Distributed computing", "Engineering", "Computer network", "Mathematics" ], "cited_by_count": 18, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/1424-8220/24/20/6698/pdf?version=1729239110" }, { "id": "https://openalex.org/W4402158311", "doi": "10.1109/icscss60660.2024.10625617", "title": "Impact of Drone and Big Data Integration on Supply Chain Efficiency and Operations", "year": 2024, "publication_date": "2024-07-10", "type": "conference-paper", "language": "en", "authors": [ "Shyamsunder Chitta", "Dankan Gowda V", "Hariprasad Soni", "Ved Srinivas", "Santosh Aghav", "Ibrahim N. Abdullah" ], "first_author": "Shyamsunder Chitta", "venue": null, "publisher": null, "abstract": "This paper focuses on analyzing the effects that are characterized by the use of drones in supply chain management and the role played by big data integration. The analysis of the contemporary trends that take place in the supply chain industry has demonstrated that considering the use of drones and IoT the companies have attainable opportunities to become more effective and transparent. Depending on what type of sensors and cameras are mounted onto an unmanned aerial vehicle, it can collect tremendous amounts of over the airspace. When used with big data analytics, this information can be used for logistics, observing shifts in users' behavior and last-mile delivery. In this paper, the importance of the big data analytics in procurement and supply chain management decision making process will be evaluated, usage of drones in the last mile delivery will be discussed, and dependence of both the drone and big data technologies in supply chain management innovations will be established.", "keywords": [ "Drone", "Supply chain", "Big data", "Computer science", "Supply chain management", "Business", "Data mining", "Marketing" ], "cited_by_count": 10, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/icscss60660.2024.10625617" }, { "id": "https://openalex.org/W4411532680", "doi": "10.1186/s13690-025-01650-z", "title": "Adopting drone technology for blood delivery: a feasibility study to evaluate its efficiency and sustainability", "year": 2025, "publication_date": "2025-06-23", "type": "article", "language": "en", "authors": [ "Pammi Gauba", "Anita Nangia", "Shalini Bahadur", "Sangeeta Pahuja", "Shweta Dang", "Rambha Pathak", "Kiran Agarwal", "Sunita Sharma", "Deepti Ambalkar", "Manoj Jha", "Ranju Singh", "Priyanka Virdi", "Rakesh Gupta", "Shambhavee Singh", "Ravindra Pandey", "Bhuvan Adhlakha", "Pragati Agarwal", "Kuldeep Nigam", "Sumit Suresh Aggarwal" ], "first_author": "Pammi Gauba", "venue": "Archives of Public Health", "publisher": "BioMed Central", "abstract": "BACKGROUND: Understanding what drives effective implementation is the key to making more sustainable developments. Blood and its components are critical for life-saving transfusions, however, their availability at the point of care is often limited. Moreover, the logistical challenges associated with blood product transport are substantial and maintaining the cold chain throughout the supply route is crucial to sustain their functional integrity as the temperature fluctuations can compromise its oxygen-carrying capacity. Furthermore, factors such as unpredictable demand, remote geographical locations, and extreme climatic conditions can further compound these difficulties. With India's diverse and distinct topographical variations and geographical spread, last-mile service deliveries of healthcare supplies face multiple challenges. Unmanned Aerial Vehicles (UAVs) or drones are emerging technologies with the potential to leapfrog the last mile logistics solution for transporting medical supplies thus, strengthening the overall healthcare system. METHODS: This study investigates the impact of drone-based delivery on the quality and stability of blood components, relative to conventional transport methods along with the operational challenges associated with implementing a drone-based blood delivery system. To achieve these objectives, the EPIS (Exploration, Preparation, Implementation, Sustainment) framework was adopted, which facilitates a systematic approach to this complex implementation process. The drone-related technical and logistic challenges encountered during the study were also identified. RESULTS: The study documents the challenges experienced by the study team during the drone-based delivery of blood while maintaining the biochemical parameters of blood components. During the drone based transportation the temperature, integrity of blood cells, and other parameters were maintained, while slight changes in few parameters were observed via both transportation modes. In this study the drone travelled around 36 km in 8 min, while van took around 55 min to cover the same distance. CONCLUSIONS: The present study was done to assess the impact of drone-based delivery on the blood components after the transportation and their comparison to conventional modes of transportation, also, to lay foundation for the successful and sustainable drone-based blood delivery programs. This study suggests that the blood and its components can be transported safely by drone following standard guidelines, which could be helpful particularly in determining blood in emergency situation and difficult terrains.", "keywords": [ "Drone", "Sustainability", "Health services research", "Public health", "Health informatics", "Health administration", "Medicine", "Computer science", "Nursing" ], "cited_by_count": 14, "is_retracted": false, "is_paratext": false, "open_access_url": "https://archpublichealth.biomedcentral.com/counter/pdf/10.1186/s13690-025-01650-z" }, { "id": "https://openalex.org/W4399365774", "doi": "10.3390/drones8060247", "title": "Drone-Based Instant Delivery Hub-and-Spoke Network Optimization", "year": 2024, "publication_date": "2024-06-05", "type": "article", "language": "en", "authors": [ "Zhi‐Hua Hu", "Yanling Huang", "Yao-Na Li", "Xiao-Qiong Bao" ], "first_author": "Zhi‐Hua Hu", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "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.", "keywords": [ "Instant", "Drone", "Computer science", "Spoke-hub distribution paradigm", "Engineering", "Transport engineering", "Physics" ], "cited_by_count": 12, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3390/drones8060247" }, { "id": "https://openalex.org/W4295837674", "doi": "10.1007/978-3-031-16579-5_14", "title": "Integrated Path Planning and Task Assignment Model for On-Demand Last-Mile UAV-Based Delivery", "year": 2022, "publication_date": "2022-01-01", "type": "conference-paper", "language": "en", "authors": [ "Jose Javier Escribano Macias", "Huan Chang", "Panagiotis Angeloudis" ], "first_author": "Jose Javier Escribano Macias", "venue": "Lecture notes in computer science", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Computer science", "Task (project management)", "Integer programming", "Heuristic", "Operations research", "Path (computing)", "Dynamic programming", "Assignment problem", "Reinforcement learning", "Stochastic programming", "Mathematical optimization", "Baseline (sea)" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-031-16579-5_14" }, { "id": "https://openalex.org/W2885320369", "doi": "10.1007/s10846-018-0894-5", "title": "A New Path Planning Algorithm Using a GNSS Localization Error Map for UAVs in an Urban Area", "year": 2018, "publication_date": "2018-08-03", "type": "article", "language": "en", "authors": [ "Guohao Zhang", "Li‐Ta Hsu" ], "first_author": "Guohao Zhang", "venue": "Journal of Intelligent & Robotic Systems", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Pseudorange", "GNSS applications", "Multipath propagation", "Computer science", "Non-line-of-sight propagation", "Multipath mitigation", "Algorithm", "Real-time computing", "Satellite", "Global Positioning System", "Engineering", "Telecommunications" ], "cited_by_count": 77, "is_retracted": false, "is_paratext": false, "open_access_url": "http://ira.lib.polyu.edu.hk/bitstream/10397/81261/3/Zhang_New_Path_GNSS.pdf" }, { "id": "https://openalex.org/W4381413955", "doi": "10.2139/ssrn.4485428", "title": "Crowdsourced Vehicles and Uavs for Last-Mile Delivery Application Using Blockchain-Hosted Matching Mechanism", "year": 2023, "publication_date": "2023-01-01", "type": "preprint", "language": "en", "authors": [ "Maha Kadadha", "Rabeb Mizouni", "Shakti Singh", "Hadi Otrok", "Azzam Mourad" ], "first_author": "Maha Kadadha", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Blockchain", "Crowdsourcing", "Last mile (transportation)", "Mile", "Computer science", "Matching (statistics)", "Mechanism (biology)", "Computer security", "World Wide Web", "Geography", "Physics" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.4485428" }, { "id": "https://openalex.org/W4399739177", "doi": "10.1007/978-3-031-62922-8_3", "title": "Optimization of a Last Mile Delivery Model with a Truck and a Drone Using Mathematical Formulation and a VNS Algorithm", "year": 2024, "publication_date": "2024-01-01", "type": "conference-paper", "language": "en", "authors": [ "Batool Madani", "Malick Ndiaye", "Saı̈d Salhi" ], "first_author": "Batool Madani", "venue": "Lecture notes in computer science", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Drone", "Computer science", "Truck", "Mile", "Last mile (transportation)", "Algorithm", "Simulation", "Aerospace engineering", "Engineering", "Geography", "Geodesy" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-031-62922-8_3" }, { "id": "https://openalex.org/W4411225498", "doi": "10.1016/j.eswa.2025.128503", "title": "Multi-objective optimization for time-dependent vehicle routing problem with drones", "year": 2025, "publication_date": "2025-06-12", "type": "article", "language": "en", "authors": [ "Yong Peng", "Chengxi Zhang", "Ajinkya N. Tanksale", "Dennis Z. Yu" ], "first_author": "Yong Peng", "venue": "Expert Systems with Applications", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Vehicle routing problem", "Computer science", "Mathematical optimization", "Routing (electronic design automation)", "Mathematics", "Computer network" ], "cited_by_count": 16, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.eswa.2025.128503" }, { "id": "https://openalex.org/W4407591470", "doi": "10.30574/ijsra.2025.14.2.0404", "title": "Lean in logistics through autonomous last-mile delivery", "year": 2025, "publication_date": "2025-02-15", "type": "article", "language": "en", "authors": [ "Olakunle Abimbola Kumuyi" ], "first_author": "Olakunle Abimbola Kumuyi", "venue": "International Journal of Science and Research Archive", "publisher": "GSC Online Press", "abstract": "Efficient last-mile delivery is essential in modern commerce, yet it poses challenges in timeliness, cost-effectiveness, and customer satisfaction. Between 41% and 53% of supply chain expenses, particularly in the United States, are attributed to this final leg. Autonomous technology, with its complex algorithms and driverless vehicles, is capable of revolutionizes last-mile delivery by optimizing routing and scheduling, reducing labor costs, and ensuring the fastest delivery routes. Real-time inventory management can further integrate lean principles into last-mile delivery, and therefore enhancing operational efficiency. This project explores the synergy between autonomous technology and lean concepts, aiming to eliminate non-value-added tasks, maximize resource utilization, and enhance overall efficiency, in terms of travel time reduction, with focuses specifically on develop countries with supporting technologies to support autonomous vehicles and robot technologies infrastructures in place. The research focused on leveraging available traffic data from autonomous technology can enable continuous improvement, enhancing productivity and customer satisfaction. Additionally, incorporating autonomous technology can enhance the reliability and safety of last-mile delivery operations through cost reduction, time reduction, and route flexibility. This research primarily examines the impact of traffic lights on traditional delivery vans, using New York City streets as a case study. A comprehensive model has been developed to quantify the cost implications of traffic light delays, providing a structured method to evaluate these inefficiencies. The model offers a practical approach that can be applied globally users simply need to input relevant parameters, and the system will compute the time, cost, and overall impact of such delays. Additionally, the study highlights the advantages of autonomous delivery systems, particularly drones, over conventional delivery methods. By utilizing this model, businesses and policymakers can make data-driven decisions to optimize last-mile logistics and enhance delivery efficiency. Recommendations include utilizing autonomous technology to meet environmental preservation requirements and offering eco-friendly delivery options. The convergence of lean principles and autonomous technologies offers transformative opportunities, enabling businesses to fulfill consumer demands, reduce costs, operate sustainably, and enhance efficiency.", "keywords": [ "Mile", "Last mile (transportation)", "Business", "Operations management", "Engineering", "Geography" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://journalijsra.com/sites/default/files/fulltext_pdf/IJSRA-2025-0404.pdf" }, { "id": "https://openalex.org/W2963421154", "doi": "10.1111/itor.12671", "title": "A variable neighborhood search for flying sidekick traveling salesman problem", "year": 2019, "publication_date": "2019-04-24", "type": "article", "language": "en", "authors": [ "Júlia Cária de Freitas", "Puca Huachi Vaz Penna" ], "first_author": "Júlia Cária de Freitas", "venue": "International Transactions in Operational Research", "publisher": "Wiley", "abstract": "Abstract The efficiency and dynamism of unmanned aerial vehicles, or drones, have presented substantial application opportunities in several industries in the last years. Notably, logistic companies have given close attention to these vehicles to reduce delivery time and operational cost. A variant of the traveling salesman problem (TSP), called the flying sidekick traveling salesman problem, was introduced involving drone‐assisted parcel delivery. The drone launches from the truck, proceeds to deliver parcels to a customer, and then is recovered by the truck at a third location. While the drone travels through a trip, the truck delivers parcels to other customers as long as the drone has enough battery to hover waiting for the truck. This work proposes a hybrid heuristic where the initial solution is created from the optimal TSP solution reached by a TSP solver. Next, an implementation of the general variable neighborhood search is employed to obtain the delivery routes of truck and drone. Computational experiments show the potential of the algorithm to improve significantly delivery time. Furthermore, we provide a new set of instances based on the well‐known traveling salesman problem library instances.", "keywords": [ "Travelling salesman problem", "Drone", "Truck", "Computer science", "Variable neighborhood search", "Solver", "2-opt", "Heuristic", "Traveling purchaser problem", "Set (abstract data type)", "Variable (mathematics)", "Operations research" ], "cited_by_count": 228, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/1804.03954.pdf" }, { "id": "https://openalex.org/W7131619759", "doi": "10.1016/j.trc.2026.105594", "title": "Slope-dependent energy optimization for cooperative truck-drone delivery and pickup routing with time windows", "year": 2026, "publication_date": "2026-02-26", "type": "article", "language": "en", "authors": [ "Ying Wang", "Taofeng Ye", "Jiajun Sun", "Jicong Duan" ], "first_author": "Ying Wang", "venue": "Transportation Research Part C Emerging Technologies", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Energy consumption", "Pickup", "Benchmark (surveying)", "Vehicle routing problem", "Scalability", "Schedule", "Terrain", "Routing (electronic design automation)", "Fuel efficiency" ], "cited_by_count": 4, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.trc.2026.105594" }, { "id": "https://openalex.org/W4403170628", "doi": "10.1007/s10668-024-05461-0", "title": "Evaluation of integrated sustainable last-mile delivery methods in an intuitionistic fuzzy environment", "year": 2024, "publication_date": "2024-10-05", "type": "article", "language": "en", "authors": [ "Faik Bilgili", "Ş. F. Yılmaz", "Fulya Zaralı", "Neslihan Demirel" ], "first_author": "Faik Bilgili", "venue": "Environment Development and Sustainability", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Sustainable development", "Mile", "Computer science", "Business", "Geography", "Political science" ], "cited_by_count": 3, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/s10668-024-05461-0" }, { "id": "https://openalex.org/W4283264932", "doi": "10.2514/6.2022-3766", "title": "Spatiotemporal Population Movement for Ground Risk of Unmanned Aerial Vehicles (UAVs) in Urbanized Environments using Public Transportation Data", "year": 2022, "publication_date": "2022-06-20", "type": "conference-paper", "language": "en", "authors": [ "Anush Kumar Sivakumar", "Mohd Hasrizam Che Man", "Kin Huat Low" ], "first_author": "Anush Kumar Sivakumar", "venue": "AIAA AVIATION 2022 Forum", "publisher": null, "abstract": "View Video Presentation: https://doi.org/10.2514/6.2022-3766.vid Unmanned aerial vehicles (UAV) or ’drones’ are expected to increase in the future and be employed for a multitude of applications like parcel delivery, industrial inspection, aerial photography, and safety surveillance. Consequently, increase in UAV traffic may lead to higher likelihood of UAV failure and crash to occur. For urbanized environments, operation of UAV poses high risk to the population on ground due to increased population movement and expectation of people to be outdoors. Diffusive model simulation using random walk was employed to estimate population movement outside buildings at different times especially near Mass Rapid Transit (MRT) train stations. In our preliminary study, the influence of different times to the population movement was highlighted. It was observed that the population was more concentrated around MRT stations at morning and afternoon during weekdays, while more constant throughout the day during weekends or holidays. Ground risk mapping due to UAV operation could also be studied and performed in future study to have better understanding on this risk. Study of population movement in urbanized environments for the ground risk assessment of UAV operations will provide better insights on the limitation of current risk assessment. At the same time, this could help provide information for national aviation authorities to formulate risk assessment and approve UAV operation in urban environments.", "keywords": [ "Population", "Drone", "Computer science", "Transport engineering", "Risk assessment", "Environmental science", "Aeronautics", "Geography", "Engineering", "Computer security" ], "cited_by_count": 6, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2514/6.2022-3766" }, { "id": "https://openalex.org/W4285253711", "doi": "10.1007/978-3-031-10047-5_57", "title": "Task Allocation Algorithms for Drone Parcel Delivery Systems", "year": 2022, "publication_date": "2022-01-01", "type": "conference-paper", "language": "en", "authors": [ "Tomás Bastos", "Bruno J. Guerreiro", "Rita Cunha" ], "first_author": "Tomás Bastos", "venue": "Lecture notes in electrical engineering", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Drone", "Task (project management)", "Computer science", "Algorithm", "Operations research", "Real-time computing", "Mathematics", "Engineering", "Systems engineering", "Biology" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-031-10047-5_57" }, { "id": "https://openalex.org/W4401407583", "doi": "10.3390/drones8080380", "title": "Wind Tunnel Investigation of the Icing of a Drone Rotor in Forward Flight", "year": 2024, "publication_date": "2024-08-07", "type": "article", "language": "en", "authors": [ "Derek Harvey", "Éric Villeneuve", "Mathieu Béland", "Maxime Lapalme" ], "first_author": "Derek Harvey", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "The Bell Textron APT70 is a UAV concept developed for last mile delivery and other usual applications. It performs vertical takeoff and transition into aircraft mode for forward flight. It includes four rotor each with four rotating blades. A test campaign has been performed to study the effects of ice accretion on rotor performance through a parametric study of different parameters, namely MVD, LWC, rotor speed, and pitch angle. This paper presents the last experimentations of this campaign for the drone rotor operating in forward flight under simulated icing conditions in a refrigerated, closed-loop wind tunnel. Results demonstrated that the different parameters studied greatly impacted the collection efficiency of the blades and thus, the resulting ice accretion. Smaller droplets were more easily influenced by the streamlines around the rotating blades, resulting in less droplets impacting the surface and thus slower ice accumulations. Higher rotation speeds and pitch angles generated more energetic streamlines, which again transported more droplets around the airfoils instead of them impacting on the surface, which also led to slower accumulation. Slower ice accumulation resulted in slower thrust losses, since the loss in performances can be directly linked to the amount of ice accreted. This research has not only allowed the obtainment of very insightful results on the effect of each test parameter on the ice accumulation, but it has also conducted the development of a unique test bench for UAV propellers. The new circular test sections along with the new instrumentation installed in and around the tunnel will allow the laboratory to be able to generate icing on various type of UAV in forward flight under representative atmospheric conditions.", "keywords": [ "Icing", "Drone", "Aerospace engineering", "Aeronautics", "Wind tunnel", "Rotor (electric)", "Aerodynamics", "Marine engineering", "Engineering", "Environmental science", "Meteorology", "Physics" ], "cited_by_count": 11, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2504-446X/8/8/380/pdf?version=1723000711" }, { "id": "https://openalex.org/W7160284882", "doi": "10.1007/s10479-026-07243-5", "title": "A Bayesian trust-based hybrid truck-drone coalition framework for resilient and sustainable last-mile delivery", "year": 2026, "publication_date": "2026-05-05", "type": "article", "language": "en", "authors": [ "Armin Mahmoodi", "Seyed Mojtaba Sajadi", "Said M. Easa" ], "first_author": "Armin Mahmoodi", "venue": "Annals of Operations Research", "publisher": "Springer Science+Business Media", "abstract": "Abstract This study proposes a novel hybrid Truck-Drone-Coalition (TDC) framework to enhance the resilience and sustainability of last-mile urban delivery systems. The model optimizes four conflicting objectives simultaneously: total delivery cost, service time, operational risk, and unmanned aerial vehicles (UAVs, also called drones) energy consumption. To address behavioral uncertainty among coalition partners, we integrate a Bayesian trust update mechanism with a Markov transition process, enabling dynamic adjustment of cooperation levels and automatic activation of fallback outsourcing when trust thresholds are breached. The solution combines an exact Mixed-Integer Linear Programming (MILP) core with a Non-dominated Sorting Genetic Algorithm II (NSGA-II) metaheuristic to ensure feasible routing, coalition stability, and robust Pareto-optimal trade-offs. Validated using real-world urban scenarios (Solomon benchmarks and commercial UAV specifications), the framework demonstrates: (a) 18% fewer delivery disruptions compared to static trust models, (b) 94% on-time delivery rate under high uncertainty (an 11% improvement over the base case), and (c) 9% lower costs and 12% higher energy efficiency through adaptive coalition reconfiguration. Simulation results demonstrate that the Bayesian trust mechanism effectively detects misbehavior, reallocates tasks via third-party outsourcing, and sustains service reliability under coalition instability or UAV breakdowns. Additionally, dynamic Shapley cost-sharing ensures fair cost allocation among coalition members. By integrating trust modeling, cooperative game theory, and risk-aware controls, this research bridges a critical gap in hybrid truck-drone logistics. The findings provide actionable insights for logistics managers seeking cost-effective, flexible, and resilient last-mile solutions. Future work will focus on real-time trust evaluation and dynamic coalition reconfiguration using live operational data.", "keywords": [ "Control reconfiguration", "Resilience (materials science)", "Sorting", "Cooperative game theory", "Service (business)", "Reliability (semiconductor)", "Markov decision process", "Pooling", "Service provider", "Game theory" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/s10479-026-07243-5" }, { "id": "https://openalex.org/W2990896168", "doi": "10.1016/j.trd.2019.11.024", "title": "Effects of a hovering unmanned aerial vehicle on urban soundscapes perception", "year": 2019, "publication_date": "2019-12-19", "type": "article", "language": "en", "authors": [ "Antonio J. Torija", "Zhengguang Li", "Rod Self" ], "first_author": "Antonio J. Torija", "venue": "Transportation Research Part D Transport and Environment", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Drone", "Annoyance", "Soundscape", "Noise (video)", "Loudness", "Noise pollution", "Noise control", "Aeronautics", "Perception", "Computer science", "Transport engineering", "Environmental science" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/1912.00087" }, { "id": "https://openalex.org/W4414444959", "doi": "10.1016/j.tre.2025.104412", "title": "Urban food delivery service optimisation with coordinated delivery riders and drones", "year": 2025, "publication_date": "2025-09-23", "type": "article", "language": "en", "authors": [ "Mingzhi Lyu", "Wei Liu", "Qingying He", "Lingxiao Wu" ], "first_author": "Mingzhi Lyu", "venue": "Transportation Research Part E Logistics and Transportation Review", "publisher": "Elsevier BV", "abstract": "Online food delivery platforms like Meituan and Foodpanda are paying increasing attention to integrating drone technology into existing delivery service networks to enhance the efficiency and flexibility of their operations. However, existing drone-assisted delivery models are primarily designed for parcel delivery, which cannot be directly used for the food delivery market with coordinated delivery riders and drones. To address this challenge, we investigate a one-to-one Pickup and Delivery Problem with multi-riders and multi-drones (PDP-mR-mD) for the urban food delivery market. The studied problem involves coordinated riders and drones with package transhipment and synchronisation permitted at docking hubs. Pickup and delivery of a food package can be fulfilled by either a rider or a drone independently or in cooperation while considering constraints of time window, payload capacity, battery endurance, and location accessibility. In particular, an arc-based mixed integer linear programming model is developed for the studied problem. An exact branch-and-price algorithm is developed by decomposing the arc-based model into a path-based master problem with two resource-constrained pricing sub-problems, where synchronised package transhipment at docking hubs is established through the branching scheme. An Adaptive Large Neighbourhood Search (ALNS) algorithm has also been designed to solve large-scale instances. The efficiency and effectiveness of the proposed solution approaches and the potential benefits of coordinated delivery systems are verified and illustrated through small-scale instances and large-scale case studies.", "keywords": [ "Drone", "Food delivery", "Service (business)", "Service delivery framework", "Service quality", "Service provider" ], "cited_by_count": 15, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.tre.2025.104412" }, { "id": "https://openalex.org/W4283793386", "doi": "10.3390/app12136781", "title": "Multirotor Unmanned Aerial Vehicle Configuration Optimization Approach for Development of Actuator Fault-Tolerant Structure", "year": 2022, "publication_date": "2022-07-04", "type": "article", "language": "en", "authors": [ "Yisak Debele", "Ha-Young Shi", "Assefinew Wondosen", "Jin-Hee Kim", "Beom-Soo Kang" ], "first_author": "Yisak Debele", "venue": "Applied Sciences", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Presently, multirotor unmanned aerial vehicles (UAV) are utilized in numerous applications. Their design governs the system’s controllability and operation performance by influencing the achievable forces and moments produced. However, unexpected causalities, such as actuator failure, adversely affect their controllability, which raises safety concerns about their service. On the other hand, their design flexibility allows further design optimization for various performance requirements, including actuator failure tolerance. Thus, this study proposed an optimization framework that can be employed to design a novel actuator fault-tolerant multirotor UAV configuration. The approach used an attainable moment set (AMS) to evaluate the achievable moment from a multirotor configuration; similarly, standard deviation geometries (SDG) were employed to define performance requirements. Therefore, given a UAV configuration, actuator fault situation, and SDG derived from the designed mission requirement, the suggested optimization framework maximizes the scaling factor of SDG and fits it into the AMS by adjusting the design parameters up to a sufficient margin. The framework is implemented to optimize selected parameters of the Hexacopter-type of parcel delivery multirotor UAV developed by the PNU drone, and a simulation was conducted. The result showed that the optimized configuration of the UAV achieved actuator fault tolerance and operation-performing capability in the presence of a failed actuator.", "keywords": [ "Multirotor", "Actuator", "Controllability", "Fault tolerance", "Control theory (sociology)", "Control engineering", "Flexibility (engineering)", "Moment (physics)", "Computer science", "Engineering", "Aerospace engineering", "Reliability engineering" ], "cited_by_count": 8, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2076-3417/12/13/6781/pdf?version=1657016785" }, { "id": "https://openalex.org/W4405909329", "doi": "10.1109/wf-iot62078.2024.10811216", "title": "Green and Intelligent Planning of Drone Launch in Truck-Drone Collaborative Delivery", "year": 2024, "publication_date": "2024-11-10", "type": "conference-paper", "language": "en", "authors": [ "Didem Cicek", "Murat Şimşek", "Burak Kantarcı" ], "first_author": "Didem Cicek", "venue": null, "publisher": null, "abstract": "The Internet of Things (IoT) paradigm has enabled innovative applications across various domains, significantly enhancing efficiency in the transportation sector through intelligence-driven and sustainable solutions. In the field of parcel delivery, the integration of trucks and drones has attracted considerable attention from both academia and industry as a means to optimize logistics networks and reduce last-mile delivery costs. Traditionally, research on truck-drone collaborative delivery (TDCD) has focused on routing and scheduling problems within hypothetical scenarios. This study, however, seeks to address the problem using a more realistic approach by introducing a newly generated customer order dataset, which includes data from 191 customer locations over a span of 7 days. The goal is to evaluate the efficiency of drone deliveries assisted by trucks. Utilizing this dataset, we applied the Self-Organizing Feature Map (SOFM) algorithm, a type of artificial neural network, to the TDCD problem. This novel approach identifies the optimal location for truck-based drone launches to minimize overall travel distance. Thanks to its adaptive nature, the SOFM algorithm dynamically selects the launch location based on daily customer orders rather than relying on a static, predetermined site. This method has resulted in a 4.4% reduction in the total distance traveled by drones and a $\\mathbf{1. 1 \\%}$ reduction in the distance covered by trucks over the seven-day period. These efficiencies translate into savings of $30.28 \\mathrm{gCO2}$ in carbon emissions and 80.16 Wh of energy consumption, equivalent to 288.58 Kjoules.", "keywords": [ "Drone", "Aeronautics", "Truck", "Computer science", "Aerospace engineering", "Engineering" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/wf-iot62078.2024.10811216" }, { "id": "https://openalex.org/W4399970517", "doi": "10.4018/979-8-3693-2093-8.ch017", "title": "Drones for Healthcare Supplies and Last Mile Vital Aid Delivery in the Flood Affected Areas, Pakistan", "year": 2024, "publication_date": "2024-05-31", "type": "book-chapter", "language": "en", "authors": [ "Muhammad Asif Rabbani", "Saima Tasneem", "Ruhsan Onder" ], "first_author": "Muhammad Asif Rabbani", "venue": "Advances in computational intelligence and robotics book series", "publisher": "IGI Global", "abstract": "Pakistan's floods have devastated the country for decades. Climate change impacts many nations, especially South Asian countries bordered by glaciers and the Himalayas. Because of climate change, these reservoirs are melting quickly and creating massive floods that affect millions. The rise of water-based diseases, including diarrhea, dengue fever, and malaria, burdens healthcare systems. Flood-affected communities lack period-hygiene products and maternity care for many pregnant women. The study used past flood statistics to find a drone-based way to distribute health supplies to flood-affected communities quickly. This chapter outlines a knowledge-based architecture for flood-relief, an intelligent drone management system for large-scale flood relief and rescue operations. Drones cut first aid delivery time and might be used for flood assistance in the future.", "keywords": [ "Mile", "Drone", "Flood myth", "Last mile (transportation)", "Geography", "Medical emergency", "Medicine", "Archaeology", "Biology" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dx.doi.org/10.4018/979-8-3693-2093-8.ch017" }, { "id": "https://openalex.org/W4409712520", "doi": "10.1177/22779752251329395", "title": "A Systematic Review and Morphological Analysis of the Literature on Last Mile Delivery Using Drones: Quo Vadis?", "year": 2025, "publication_date": "2025-04-22", "type": "review", "language": "en", "authors": [ "G Rohit", "Ramesh Krishnan", "P. N. Ram Kumar" ], "first_author": "G Rohit", "venue": "IIM Kozhikode Society & Management Review", "publisher": "SAGE Publishing", "abstract": "With the evolution of technology, drones hold immense potential to address the last mile delivery challenges in e-commerce. However, a holistic and systematic literature review of all the studies and an understanding of the interrelationships between the different dimensions of drone-based last mile delivery, such as customer expectations, stakeholders and desired outcomes, is lacking in the literature. To address this gap, we conducted a systematic literature review and analysed the literature on drone-based last mile delivery using a morphological analysis approach to develop a conceptual framework with seven dimensions and 37 variants. The dimensions identified are customer expectations, distribution mode, inventory planning, returns handling, geographical scope, stakeholders and desired outcomes. The Variance Intersection Matrix revealed 176 research gaps that future researchers can explore. This study also helps managers and policymakers visualize the different dimensions of drone-based last mile delivery and their interrelationships, which will help accelerate the adoption of drones in delivery fleets.", "keywords": [ "Mile", "Drone", "Status quo", "Business", "Political science", "Computer science", "Geography", "Law" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1177/22779752251329395" }, { "id": "https://openalex.org/W4415649093", "doi": "10.56578/mits040402", "title": "Safety Risk Factors of Urban Logistics Drone Delivery in the Context of the Low-Altitude Economy", "year": 2025, "publication_date": "2025-10-28", "type": "article", "language": null, "authors": [ "Yuanyuan Zhang", "Xianglong Li", "Nuoning Zheng", "Yuxin Xue" ], "first_author": "Yuanyuan Zhang", "venue": "Mechatronics and intelligent transportation systems", "publisher": null, "abstract": "This study analyzes the safety risk transmission mechanism in urban logistics drone last-mile delivery within the policy-driven low-altitude economy.To address the limitations of traditional risk identification methods, which rely heavily on accident data, this research integrates the Fuzzy Decision Analysis Laboratory Method (Fuzzy-DEMATEL),Interpretive Structural Modeling (ISM), and the Matrix of Cross-Impact Multiplication (MICMAC) to construct a three-dimensional analytical framework based on causal relationships, structural hierarchy, and attribute classification.First, Fuzzy-DEMATEL is employed to quantify the strength of causal relationships among risk factors.Next, ISM is used to deconstruct the multi-level hierarchical network and identify fundamental causes within the risk system.Finally, MICMAC is applied to calculate the dependencies and driving forces of each influencing factor, helping prioritize risk governance measures.The research findings indicate that: (1) The safety risk system of urban logistics drones for last-mile delivery exhibits a \"dual-core driven -multi-loop coupled\" characteristic.Equipment failures act as the physical carriers of systemic failures, while the root-cause risks stem from institutional factors such as inadequate pre-service training and violations of laws and regulations.(2) The risk hierarchy follows a pyramid-shaped transmission path, with risks propagating from the root layer to the surface in successive layers.Open airspace serves as an accelerator, transforming environmental disturbances into institutional defects, which in turn lead to technical failures.(3) The dependency attributes of each factor indicate the priority order for risk prevention and control: management leverage points serve as the strategic control core, the environment-technology interaction network is central to joint prevention, standardized processes solidify basic operations, and systemic risk levels are reduced.", "keywords": [ "Context (archaeology)", "Drone", "Risk management", "Key (lock)" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://library.acadlore.com/MITS/2025/4/4/MITS_04.04_02.pdf" }, { "id": "https://openalex.org/W2981914048", "doi": "10.1109/dsd.2019.00067", "title": "Framework of Key Enabling Technologies for Safe and Autonomous Drones' Applications", "year": 2019, "publication_date": "2019-08-01", "type": "conference-paper", "language": "en", "authors": [ "Réda Nouacer", "Huascar Espinoza Ortiz", "Yassine Ouhammou", "Rodrigo Castineira Gonzalez" ], "first_author": "Réda Nouacer", "venue": null, "publisher": null, "abstract": "The potential applications for drones, especially those in manned areas or into non-segregated airspace, are currently not possible without the development and validation of certain key enabling technologies: \"detect and avoid\", \"air traffic management\" and \"command and control (C2) link\". SESAR JU identified that issue has a high impact on European innovation, which demands R&D investments and incentives for the convergence of shared technologies and markets. The COMP4DRONES project complements SESAR JU efforts with a particular focus on safe software and hardware drone architectures. COMP4DRONES will bear a holistically designed ecosystem ranging from application to electronic components. The ecosystem aims at supporting (1) efficient customization and incremental assurance of drone-embedded platforms, (2) safe autonomous decision making concerning individual or cooperative missions, (3) trustworthy drone-to-drone and drone-to-ground communications even in presence of malicious attackers and under the intrinsic platform constraints, and (4) agile and cost-effective design and assurance of drone modules and systems. Lead applications driving ecosystem development and benchmarking on the fields of transport, inspection, logistic, precision agriculture, parcel delivery, among others, will be produced.", "keywords": [ "Drone", "Agile software development", "Key (lock)", "Computer science", "Systems engineering", "Command and control", "Benchmarking", "Computer security", "Engineering", "Business", "Telecommunications", "Software engineering" ], "cited_by_count": 13, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/dsd.2019.00067" }, { "id": "https://openalex.org/W4400189255", "doi": "10.1109/iccps61052.2024.00036", "title": "Poster Abstract: Landing-Type Aware Multi-Drone Route Generation for Last-Mile Delivery Service", "year": 2024, "publication_date": "2024-05-13", "type": "conference-paper", "language": "en", "authors": [ "JiHyun Kwon", "BaekGyu Kim", "Yi‐Ying Chen", "Chung‐Wei Lin" ], "first_author": "JiHyun Kwon", "venue": null, "publisher": null, "abstract": "We consider the problem of generating delivery routes for multiple drones in the last-mile delivery service. In particular, the landing type—how a parcel is to be dropped off from a drone—is explicitly modeled in terms of the landing area and the landing time, which was not considered in other drone delivery works. A Mixed Integer Linear Programming (MILP) problem is formulated to optimize the delivery route for each drone by minimizing the total delivery completion time. Our preliminary result shows that landing types affect the total delivery completion time significantly, even with a small number of drones. Therefore, it is necessary to explicitly consider the characteristics of landing types for more realistic delivery route generation of a large number of drones.", "keywords": [ "Drone", "Mile", "Computer science", "Service (business)", "Last mile (transportation)", "Aeronautics", "Engineering", "Physics", "Astronomy", "Business" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/iccps61052.2024.00036" }, { "id": "https://openalex.org/W3107919152", "doi": "10.1007/978-3-030-45308-4_7", "title": "When Green Technology Meets Optimization Modeling: The Case of Routing Drones in Logistics, Agriculture, and Healthcare", "year": 2020, "publication_date": "2020-01-01", "type": "book-chapter", "language": "en", "authors": [ "Malick Ndiaye", "Saı̈d Salhi", "Batool Madani" ], "first_author": "Malick Ndiaye", "venue": null, "publisher": null, "abstract": null, "keywords": [ "Drone", "Agriculture", "Emerging technologies", "Work (physics)", "Last mile (transportation)", "Business", "Engineering", "Computer science", "Mile", "Geography" ], "cited_by_count": 17, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/978-3-030-45308-4_7" }, { "id": "https://openalex.org/W4408619602", "doi": "10.1016/j.trpro.2025.03.095", "title": "Business Applications of Cargo Drones in the EU", "year": 2025, "publication_date": "2025-01-01", "type": "article", "language": "en", "authors": [ "Mollalign Yizengaw Mebrat", "Muhammad Waqas Ahmed", "Wim Ectors" ], "first_author": "Mollalign Yizengaw Mebrat", "venue": "Transportation research procedia", "publisher": "Elsevier BV", "abstract": "Drones have become ubiquitous in various industries due to their versatility and efficiency in performing various tasks, from agricultural operations to search and rescue missions. This paper explores the use of drones, particularly cargo drones, in revolutionizing logistics and transportation systems. Medium-range cargo drones offer the potential to transform freight transportation by offering independence from traditional infrastructure and potentially reducing environmental impact. However, the integration of UAVs into existing logistical operations faces several challenges, including regulatory hurdles, technological limitations, and public perception issues. Drones can become a viable form of cargo transportation given that the regulatory challenges are addressed and can be efficiently integrated into the existing logistic operations. It would ultimately result in an efficient last-mile delivery option and will revolutionize the logistics industry.", "keywords": [ "Drone", "Business", "Transport engineering", "Aeronautics", "Computer security", "Engineering", "Computer science" ], "cited_by_count": 7, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.trpro.2025.03.095" }, { "id": "https://openalex.org/W3163209967", "doi": "10.1596/978-1-4648-1696-3", "title": "Unlocking the Lower Skies: The Costs and Benefits of Deploying Drones across Use Cases in East Africa", "year": 2021, "publication_date": "2021-06-14", "type": "book", "language": "en", "authors": [ "Aiga Stokenberga", "María Catalina Ochoa" ], "first_author": "Aiga Stokenberga", "venue": null, "publisher": null, "abstract": "This book explores the economic and broader societal rationale for using unmanned aerial vehicle (UAV) or \"drone\" technologies as acomplement to the current transport and logistics systems in several use cases in East Africa. The specific use cases examined include medical goods deliveries, food aid delivery, land mapping and risk assessment, agriculture, and transport and energy infrastructure inspection. Across these applications, the case for using UAVs is examined within the context of logistics objectives - total operating costs, speed, availability, andflexibility - as well as human, or societal, objectives. In the public healthuse case, as more low- and middle-income countries explore opportunities to improve efficiency and performance in their health supply chains and diagnostics networks, they face myriad choices about how best to use UAVs to improve product availability and public health outcomes and to reach the last mile.", "keywords": [ "Drone", "Context (archaeology)", "Flexibility (engineering)", "Business", "Commodity", "Environmental economics", "Economics", "Geography" ], "cited_by_count": 11, "is_retracted": false, "is_paratext": false, "open_access_url": "https://documents.worldbank.org/en/publication/documents-reports/documentdetail/800891621396276151/unlocking-the-lower-skies-the-costs-and-benefits-of-deploying-drones-across-use-cases-in-east-africa" }, { "id": "https://openalex.org/W3093524177", "doi": "10.1016/j.comnet.2020.107644", "title": "Scheduling of emergency tasks for multiservice UAVs in post-disaster scenarios", "year": 2020, "publication_date": "2020-10-23", "type": "article", "language": "en", "authors": [ "Cristina Rottondi", "Francesco Malandrino", "Andrea G. Bianco", "Carla Fabiana Chiasserini", "Ioannis Stavrakakis" ], "first_author": "Cristina Rottondi", "venue": "Computer Networks", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Computer science", "Scheduling (production processes)", "Heuristic", "Task (project management)", "Plan (archaeology)", "Disaster area", "Flood myth", "Real-time computing", "Operations research", "Distributed computing", "Systems engineering", "Artificial intelligence" ], "cited_by_count": 71, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.comnet.2020.107644" }, { "id": "https://openalex.org/W4313549106", "doi": "10.1109/scisisis55246.2022.10002027", "title": "Drone Scheduling for Parcel Delivery with a Given Truck Route and Setup Times", "year": 2022, "publication_date": "2022-11-29", "type": "conference-paper", "language": "en", "authors": [ "Riki Kataoka", "Yoshiyuki Karuno", "Hitoshi Iima" ], "first_author": "Riki Kataoka", "venue": "2022 Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems (SCIS&ISIS)", "publisher": null, "abstract": "We consider a parcel delivery scheduling problem for a truck-drone distribution system. The single carrier truck plays the role of a mobile depot, and traverses in one direction a given route with some points to stop for launching/retrieving drones. The multiple identical drones are dispatched for the last-mile delivery of a parcel from the carrier truck to each of customers. In this paper, we introduce two kinds of setup times at truck stops on given routes to the underlying model, and propose a genetic search based heuristic algorithm with a modified version of a well-known list scheduling procedure, called LPT (Longest Processing Time first) rule. We also conduct numerical experiments to demonstrate the performance of the proposed heuristic, and report the results.", "keywords": [ "Drone", "Truck", "Computer science", "Scheduling (production processes)", "Heuristic", "Job shop scheduling", "Real-time computing", "Simulation", "Computer network", "Mathematical optimization", "Engineering", "Automotive engineering" ], "cited_by_count": 2, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dx.doi.org/10.1109/scisisis55246.2022.10002027" }, { "id": "https://openalex.org/W4388726784", "doi": "10.1007/s13272-023-00693-8", "title": "A mixed-method approach to investigate the public acceptance of drones", "year": 2023, "publication_date": "2023-10-01", "type": "article", "language": "en", "authors": [ "Maria Stolz", "Anne Papenfuß", "Georgia Cesar de Albuquerque Richers", "Anna Bahnmüller", "Azeem Muhammad Syed", "Andreas Gerndt", "Martin Fischer", "Jan Wegener", "Teemu Joonas Lieb", "Marcus Biella" ], "first_author": "Maria Stolz", "venue": "CEAS Aeronautical Journal", "publisher": "Springer Science+Business Media", "abstract": "Abstract Drones may play an essential role in future traffic. As with every innovation, not only is the technical maturity decisive for its success, but also whether the general public will accept it. This paper uses a mixed-method approach combining quantitative and qualitative methods to investigate social acceptance concerning drones. The study included 20 participants and comprised a virtual simulation, a team task, and a group discussion. This way, different aspects of drone acceptance were investigated. On the one hand, a simulative approach was tested to explore the visual perception of drone flights in an urban setting. On the other hand, the acceptance of various drone applications such as civil protection, parcel delivery, and air taxis was studied. Furthermore, this research identified requirements for coordinating and managing future drone traffic. This paper combines two acceptance models from the literature as a theoretical framework: an adoption of Chamata’s and Winterton’s Technical Acceptance Model by Krempel and the Unacceptability–Acceptance Scale by Hofinger.", "keywords": [ "Drone", "Perception", "Scale (ratio)", "Computer science", "Management science", "Psychology", "Engineering", "Geography" ], "cited_by_count": 11, "is_retracted": false, "is_paratext": false, "open_access_url": "https://link.springer.com/content/pdf/10.1007/s13272-023-00693-8.pdf" }, { "id": "https://openalex.org/W7128486010", "doi": "10.1016/j.jairtraman.2026.102978", "title": "Environmental sustainability and risk-aware optimization in hybrid truck-drone logistics: A holistic multi-objective framework", "year": 2026, "publication_date": "2026-02-10", "type": "article", "language": "en", "authors": [ "Armin Mahmoodi", "Seyed Mojtaba Sajadi", "Jeremy F. Laliberté", "Said M. Easa" ], "first_author": "Armin Mahmoodi", "venue": "Journal of Air Transport Management", "publisher": "Elsevier BV", "abstract": "Compared to conventional truck-only systems, hybrid truck–drone delivery systems offer transformative potential for last-mile logistics by addressing operational inefficiencies, minimizing environmental impact, and enhancing safety through risk-aware optimization. This study develops a stochastic multi-objective optimization framework grounded in the Specific Operations Risk Assessment (SORA) methodology. By extending the vehicle routing problem with pickup and delivery (VRPPD) and the flying sidekick traveling salesman problem (FSTSP), the model incorporates battery optimization, CO 2 emissions reduction, and energy-efficient routing strategies. Delivery cost, time, energy consumption, operational risk, and battery performance are all optimized using a mixed-integer linear programming (MILP) and non-dominated sorting genetic algorithm II (NSGA-II) technique. Sensitivity analysis show that increasing drone fleet size and efficiency results in significant cost, time, and energy savings while improving safety. The model's flexibility in both urban and remote delivery contexts is confirmed by numerical trials. In line with life cycle analysis (LCA), this study offers practical advice for environmentally responsible and risk-aware logistics, assisting decision-makers and industry participants in the development of scalable and sustainable solutions. • Optimizes cost, time, risk, and energy in hybrid truck–drone logistics. • Uses MILP and NSGA-II for multi-objective routing and energy savings. • Incorporates SORA for risk-aware UAV operations in urban logistics. • Sensitivity shows drone efficiency cuts cost, risk, and CO 2 emissions. • Achieves energy-efficient delivery via battery-aware UAV coordination.", "keywords": [ "Drone", "Flexibility (engineering)", "Sustainability", "Vehicle routing problem", "Travelling salesman problem", "Scalability", "Sensitivity (control systems)", "Sorting", "Routing (electronic design automation)" ], "cited_by_count": 13, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.jairtraman.2026.102978" }, { "id": "https://openalex.org/W2994169124", "doi": null, "title": "A review on object detection from Unmanned Aerial Vehicle using CNN", "year": 2019, "publication_date": "2019-07-25", "type": "review", "language": "en", "authors": [ "Amanuel Ayalew", "Pooja Pooja" ], "first_author": "Amanuel Ayalew", "venue": "International journal of advance research, ideas and innovations in technology", "publisher": "IJARIT Research Foundation", "abstract": "UAV stands for unmanned Aerial Vehicle, which can be as small as birds, regular drones or as big as private aircraft with no pilot on board. Since there is no one onboard UAV is remotely controlled. UAV is currently being used for a different purpose, examples are spy footages, sky view footage, reconnaissance, attacking roles, aerial surveillance, motion picture filmmaking, disaster rescue, parcel delivery, warehouse management, and other uses. Due to UAV multi-functionality and portability especially drones demand is growing faster, therefore, people need systems that work with the UAV (drones) to detect objects in real-time for military, safety reconnaissance, and surveillance. This paper review approaches to detect objects from camera view and from UAV scenes using machine learning algorithms. The growth of computer vision systems initiated better algorithms using huge training and testing datasets, faster GPU and CPU so that systems can achieve state of the art object detection system by training and classifying the data using machine learning approach. Since there are different orientation, background, occlusion in an image, object detection is not an easy task. The goal of object detection is to categorize images and video feeds from UAV into common categories.", "keywords": [ "Drone", "Computer science", "Artificial intelligence", "Object detection", "Computer vision", "Software portability", "Segmentation" ], "cited_by_count": 5, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.ijariit.com/manuscripts/v5i4/V5I4-1250.pdf" } ]