[ { "id": "https://openalex.org/W7132546002", "doi": null, "title": "Otimização de áreas de pesquisa", "year": 2024, "publication_date": "2024-10-02", "type": "dissertation", "language": "pt", "authors": [ "João Pedro Figueiredo Gomes de Oliveira" ], "first_author": "João Pedro Figueiredo Gomes de Oliveira", "venue": "Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)", "publisher": null, "abstract": "The use of Unmanned Aerial Vehicles (UAVs) in military and police contexts has been drastically in creasing in recent years, both individually and in swarms. One of the tasks they can perform is area coverage, which can later be directed towards a Search and Rescue (SAR) operation or forest and urban surveillance. This dissertation aims to find the optimized solution with minimum time for a Vehicle Routing Problem (VRP). To achieve this, an optimization problem is formulated using a Mixed-Integer Linear Programming (MILP) approach to find the minimum time value. The solution is tested for convex areas separated by sweep segments parallel to each other, thus generating a back and forth sweep pattern. It is also tested for non-convex areas that are decomposed through Delaunay triangulation. The methods were validated in Python, and it was found that the MILP formulation is essential to achieve the minimum time. Area coverage for non-convex areas, in most cases, obtained lower time va lues, which is important to get the mission completed faster, when compared to convex polygon method. However, some weaknesses were observed regarding the division of flight time for each UAV.", "keywords": [ "Delaunay triangulation", "Linear programming", "Vehicle routing problem", "Polygon (computer graphics)", "Regular polygon", "Routing (electronic design automation)", "Convex optimization", "Greedy algorithm" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://hdl.handle.net/10400.26/59799" }, { "id": "https://openalex.org/W4414052181", "doi": "10.24200/j65.2024.64520.2405", "title": "توسعه و ارزیابی مدلهای جدید برای مسئله گشت آزاد وسایل نقلیه", "year": 2024, "publication_date": "2024-10-08", "type": "article", "language": "fa", "authors": [ "حامد منشادیان", "محسن صادق عمل نیک", "سیدعلی ترابی" ], "first_author": "حامد منشادیان", "venue": "مهندسی صنایع و مدیریت", "publisher": null, "abstract": "Vehicle routing problems are a category of complex mathematical problems in the field of transportation and supply chain management, and a wide variety of these problems have been introduced.One category within this field involves the collaboration of two different types of fleets in a network, where in many cases, one of the fleets must patrol the graph as a support fleet and is not subject to the same constraints as the other fleet, such as only visiting a vertex or an edge once.Examples include refueling fleets or trucks providing services to drones, where these fleets patrol the network so that the other fleet can use their services as needed.In this paper, the problem of the free movement of such fleets on a graph is examined as a free patrol problem, which can be used as a sub-problem in the modeling and solving of many routing problems.To this end, the problem is modeled as a mixed-integer linear programming (MILP) problem in both discrete and continuous time spaces.Generally, it is expected that the model in continuous time space will exhibit lower computational complexity, but since modeling in continuous time requires changes to the graph, leading to an increase in the number of vertices and edges, its behavior needs to be more thoroughly investigated.On the other hand, another challenge in analyzing this problem is that the objective of the free patrol sub-problem depends on the main problem, as without the main problem, the free patrol problem may lose its objective, with a fleet patrolling the graph within a designated time.Therefore, to decouple and make the free patrol independent of the main problem, five different objective functions have been introduced for each model, covering a wider range of comparisons, leading to ten models in total.Subsequently, all ten models were solved exactly using randomly generated data for ten different graphs.Finally, the behavior of the models was evaluated and compared from various perspectives.The numerical results demonstrate the superior performance of continuous time model over the discrete time model.", "keywords": [ "Process (computing)", "Identification (biology)", "Product (mathematics)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://sjie.journals.sharif.edu/article_23753_faf0427cc4125a74d48f5940004299a6.pdf" }, { "id": "https://openalex.org/W7115725860", "doi": "10.48676/unibo/amsdottorato/12266", "title": "Studies on optimization problems with dynamically arriving information", "year": 2025, "publication_date": "2025-01-01", "type": "dissertation", "language": "en", "authors": [ "Lorenz, Catherine" ], "first_author": "Lorenz, Catherine", "venue": "AMS Dottorato Institutional Doctoral Theses Repository (University of Bologna)", "publisher": "University of Bologna", "abstract": "In real-time environments like transportation planning, e-commerce, smart manufacturing, or emergency services, dynamically arriving information must be integrated on-the-fly into decision-making. This dissertation proposes effective online algorithms using classical and innovative analytical and computational evaluation methods, performance bounds, and comparisons to optimal solutions. It focuses on two dynamic optimization problems: the Online Order Batching, Sequencing, and picker Routing Problem(OOBSRP) in warehousing and the Traveling Salesman Problem with a Truck and a Drone under Incomplete Information(TSP-DI), for disaster relief. For the OOBSRP with manual and robotic carts, we prove that Reopt is asymptotically optimal with probability one under broad stochastic conditions. From a worst-case perspective, no policy can improve Reopt by more than 50%, as we show its asymptotical two-competitiveness. A computational study validates Reopt’s small gaps to the complete-information optimum (e.g. averaging at most 5% for a cost-minimization objective). A pattern analysis of Complete-Information Optimal Solutions (CIOSs), generated with dynamic programming algorithms, identifies simple algorithmic enhancements – like eliminating waiting, intervention, or strategic relocation – that further reduce costs and delivery times. These findings suggest limited benefits of anticipatory (including AI-based) algorithms in OOBSRP. Conversely, for TSP-DI with dynamically revealing road blockages, we prove that Reopt performs poorly in the worst case, with an exponentially growing competitive ratio. We propose policies delaying deliveries for drone surveillance that are significantly superior in competitive ratio and average results. Scheduling replenishment of battery-limited drones introduces a challenging static subproblem within these policies, which we address with a novel Very Large-Scale Neighborhood Search(VLNS) and an exact method. VLNS efficiently explores exponential-sized neighborhoods in polynomial runtime, making it an attractive subroutine for online policies and metaheuristics. This dissertation underscores the importance of analytical guarantees and comparisons to optima in online algorithm design, as policy effectiveness often diverges from intuition and varies significantly across problems.", "keywords": [ "Drone", "Travelling salesman problem", "Scheduling (production processes)", "Competitive analysis", "Online algorithm", "Dynamic programming", "Relocation", "Job shop scheduling", "Stochastic programming" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://amsdottorato.unibo.it/view/dottorati/DOT547/>," }, { "id": "https://openalex.org/W7113659816", "doi": null, "title": "Bi-Level Route Optimization and Path Planning With Hazard Exploration", "year": 2025, "publication_date": "2025-05-05", "type": "article", "language": null, "authors": [ "Stagg, Grant", "Peterson, Cameron K." ], "first_author": "Stagg, Grant", "venue": "Digital Commons - USU (Utah State University)", "publisher": "Utah State University", "abstract": "Effective risk monitoring in dynamic environments such as disaster zones requires an adaptive exploration strategy to detect hidden threats. We propose a bi-level unmanned aerial vehicle (UAV) monitoring strategy that efficiently integrates high-level route optimization with low-level path planning for known and unknown hazards. At the high level, we formulate the route optimization as a vehicle routing problem (VRP) to determine the optimal sequence for visiting known hazard locations. To strategically incorporate exploration efficiency, we introduce an edge-based centroidal Voronoi tessellation (CVT), which refines baseline routes using pseudo-nodes and allocates path budgets based on the UAV's battery capacity using a line segment Voronoi diagram. At the low level, path planning maximizes information gain within the allocated path budget by generating kinematically feasible B-spline trajectories. Bayesian interference is applied to dynamically update hazard probabilities, enabling the UAVs to prioritize unexplored regions. Simulation results demonstrate that edge-based CVT improves spatial coverage and route uniformity compared to the node-based method. Additionally, our optimized path planning consistently outperforms baselines in hazard discovery rates across a diverse set of scenarios.", "keywords": [ "Motion planning", "Voronoi diagram", "Path (computing)", "Hazard", "Routing (electronic design automation)", "Set (abstract data type)", "Baseline (sea)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://digitalcommons.usu.edu/spacegrant/2025/all2025/21" }, { "id": "https://openalex.org/W7165675322", "doi": "10.3390/drones10060472", "title": "U-Plan: An Integrated Framework for the Coordination and Real-Time Supervision of Heterogeneous Unmanned Aerial Systems", "year": 2026, "publication_date": "2026-06-20", "type": "article", "language": "en", "authors": [ "Ehsan Kouchaki", "M.Á. Frutos", "José Ramiro Martínez‐de Dios", "Anı́bal Ollero" ], "first_author": "Ehsan Kouchaki", "venue": "Drones", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Despite the large amount of successful existing methods and frameworks for planning sets of multiple unmanned aerial systems (UASs), there is still a lack of coordination frameworks that are capable of coping with real-world operational conditions. This paper presents U-Plan, an integrated management framework for the coordination of multi-UAS missions. U-Plan is designed to plan, schedule, monitor, and replan a heterogeneous set of UASs to complete point of interest (PoI) visiting missions while ensuring that all the generated trajectories are safe, feasible, and compliant with the required PoIs’ arrival times, UAS kinematics and energy constraints, and the existing 3D no-fly zones (NFZs). U-Plan is designed as a practical tool for strongly dynamic missions and is built upon three core components: (1) an NFZ-aware route computation method that explicitly accounts for NFZs prior to vehicle routing problem (VRP) optimization, resulting in shorter NFZ-safe routes; (2) a trajectory smoothing module that ensures the generation of kinematically feasible trajectories for fixed-wing UASs; and (3) a mission supervision module for real-time monitoring and replanning in case of changes in the UAS, mission, wind speed, or airspace restrictions. To validate the proposed architecture, we conducted rigorous experiments utilizing the VECTOR-SIL autopilot and Visionair Ground Control Station to realistically replicate the behavior of certified fixed-wing autopilots under various weather conditions using the exact same hardware and flight control software that runs onboard the physical drones. The validation shows U-Plan’s capacity to efficiently satisfy complex mission requirements with strong scalability. Due to its high computational efficiency, U-Plan enables online mission replanning, allowing UAS fleets to seamlessly adapt to changes that are typical of real-world operational scenarios.", "keywords": [ "Autopilot", "Trajectory", "Software", "Flight planning", "Drone", "Set (abstract data type)", "Automation", "Computation", "Smoothing" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.mdpi.com/2504-446X/10/6/472/pdf?version=1781945665" }, { "id": "https://openalex.org/W4403027416", "doi": "10.1111/itor.13526", "title": "Preface to the Special Issue on Logistics Optimization for Social Good", "year": 2024, "publication_date": "2024-10-01", "type": "editorial", "language": "en", "authors": [ "Jésica de Armas", "Helena Ramalhinho", "Stefan Voß" ], "first_author": "Jésica de Armas", "venue": "International Transactions in Operational Research", "publisher": "Wiley", "abstract": "Making this world a better place goes along with efforts and challenges. Many of the related issues make logistics a common basis to allow for efficiency in various dimensions, be it related to climate change, healthcare operations or social good in general. This special issue of the International Transactions in Operational Research is a compilation of pioneering research that underscores the pivotal role of logistics optimization in addressing various social challenges. The selected papers reflect a diverse array of applications, from humanitarian aid to healthcare and education, demonstrating the broad impact and potential of operational research in driving social good. The journey towards this special issue began with a simple yet profound idea: leveraging logistics and operational research to create solutions that benefit society at large. Each paper included in this issue contributes uniquely to this overarching theme, offering innovative approaches and insights that push the boundaries of conventional logistics and supply chain management. The first paper, “The Role of Drones in Disaster Response: A Literature Review of Operations Research Applications”, sets the stage by exploring the applications of drone technology in disaster management. This comprehensive review not only highlights the current state of research but also identifies future directions for leveraging drones in humanitarian logistics. Following this, “A Probabilistic Bi-objective Model for a Humanitarian Location-Routing Problem Under Uncertain Demand and Road Closure” delves into the complexities of humanitarian logistics. The paper presents a robust model to address location and routing problems amidst uncertainty, providing crucial insights for effective disaster response planning. “A Predictive Multistage Postdisaster Damage Assessment Framework for Drone Routing” continues the focus on humanitarian efforts, introducing a novel framework that enhances post-disaster assessments and optimizes drone routing for timely aid delivery. Shifting to social care, the issue includes “Home Visit Scheduling for Family Interventions: A Child Protection Case Study”, which addresses the critical need for efficient scheduling in child protection services. This study exemplifies how operational research can directly contribute to societal welfare by optimizing intervention schedules for vulnerable populations. Another significant contribution to social care is “The Rich Heterogeneous Dial-a-Ride Problem with Trip Time Prediction”. This paper presents an advanced approach to managing transportation for individuals requiring special assistance, improving both efficiency and service quality. Healthcare logistics is represented by “Variable Neighborhood Search Approach to Face-Shield Delivery During Pandemic Periods”, which showcases the application of operational research in managing healthcare supply chains during crises. This timely research highlights strategies to enhance the distribution of essential medical supplies. The issue also features “Optimizing Extracurricular Activities Assignment and Related Logistical Deployment Cost Between Collaborating Schools”, which tackles the logistical challenges in education. The proposed optimization model facilitates better resource allocation and cost management for schools, ultimately benefiting students' extracurricular engagement. From the perspective of farming and sustainability, “Sustainable Urban Farming Using a Two-Phase Multi-objective and Multicriteria Decision-Making Approach” offers a comprehensive model for optimizing urban farming logistics. This research underscores the importance of sustainable practices in food production and distribution. “Assessing Social Performance in Supply Chain Design and Planning Through a Monetization Approach” explores the monetization of social performance in supply chains. This paper bridges the gap between economic and social objectives, offering a holistic view of supply chain management. Lastly, in the realm of logistics performance evaluation, “An Alternative Assessment Approach to Global Logistics Performance Evaluation: Common Weight H-DEA Approach” introduces an innovative method for assessing logistics performance on a global scale, providing valuable metrics for benchmarking and improvement. We extend our heartfelt thanks to the authors for their exceptional contributions and to the reviewers for their insightful feedback and dedication. It is our hope that this special issue will inspire further research and practical applications in the field of logistics optimization for social good, ultimately leading to more resilient, efficient, and socially responsible systems worldwide. By bringing together these diverse studies, this special issue not only highlights the versatility and impact of Operational Research but also emphasizes the collective effort required to address complex social challenges. We invite lecturers, participants, authors, and referees to explore these papers and join us in advancing the field of logistics optimization for the betterment of society.", "keywords": [ "Computer science", "Operations research", "Management science", "Economics", "Engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1111/itor.13526" }, { "id": "https://openalex.org/W4416794684", "doi": "10.1007/s10100-025-01010-w", "title": "The two-echelon location-routing model with multi-commodity, automated delivery systems and integrating people and freight transportation", "year": 2025, "publication_date": "2025-11-28", "type": "article", "language": "en", "authors": [ "Onur Derse", "Tom Van Woensel" ], "first_author": "Onur Derse", "venue": "Central European Journal of Operations Research", "publisher": "Springer Science+Business Media", "abstract": null, "keywords": [ "Drone", "City logistics", "Quality (philosophy)", "Sample (material)", "Face (sociological concept)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1007/s10100-025-01010-w" }, { "id": "https://openalex.org/W4413096567", "doi": "10.14711/thesis-hdl151302", "title": "Optimizing On-Demand Delivery Services under Emerging Business Models", "year": 2025, "publication_date": "2025-01-01", "type": "dissertation", "language": "en", "authors": [ "Yan Liu" ], "first_author": "Yan Liu", "venue": null, "publisher": null, "abstract": "With the rapid development of the sharing economy and smart technology, on-demand delivery services have gained immense popularity. By leveraging app-based platforms, these services provide a convenient and efficient solution to rising consumer expectations regarding delivery speed, reliability, and overall service quality. This thesis examines strategic management approaches for on-demand delivery platforms by analyzing various business models and their impacts on market outcomes. Specifically, it includes two parts that propose distinct business models, focusing on the integration of diverse on-demand services, as well as the incorporation of emerging transportation mode, respectively. The first part of the thesis focuses on the integrated management of on-demand delivery services and ride-sourcing services, considering two typical delivery models: (1) on-demand food delivery services and (2) on-demand parcel delivery services. We begin with an economic analysis of on-demand food-delivery platforms, investigating management strategies that protect restaurant owners and for-hire gig workers considering the interactions with ride-sourcing services. This analysis compares the market outcomes of two business models: one where the on-demand food-delivery services and ride-sourcing services compete with each other, and another where they are integrated to a single platform. The findings indicate that the integrated strategy can make everyone in the market better-off. Furthermore, we investigate the management strategy of another business model, where an integrated platform provides both intracity parcel delivery services and ride-sourcing services. A special parcel delivery mode named “flexible delivery services”is proposed and assumed to leverage driver idle time only and have lower priority than on-demand services. The analysis reveals the complex interactions between ride-sourcing services and parcel delivery services, and evaluates their effects on each other. The second part shifts focus from service integration to the incorporation of novel transportation modes. Specifically, we investigate the impacts of integrating drone technology into the on-demand food delivery market, where an on-demand food-delivery platform serves orders with both human couriers and drones. The platform determines the optimal locations for launchpads and kiosks within a transportation network and devises an order assignment strategy that allocates food delivery orders between ground and air delivery. We formulate the platform’s problem as a mixed-integer nonlinear program and develop a novel neural network-assisted optimization method to obtain high-quality solutions. The results demonstrate that the introduction of drone delivery can lead to reduced operational costs for the platform, a smaller courier fleet size, and increased opportunities for order bundling. Overall, this thesis provides a comprehensive evaluation of management strategies for on-demand delivery services through various business models. It specifically addresses two key questions: (1) how can we enhance on-demand delivery services by leveraging the synergy between different services through integration, and (2) how can we improve these services by utilizing emerging transportation modes, such as air delivery? The findings of this thesis offer valuable managerial implications for on-demand delivery services within the contexts of the sharing economy and the low-altitude economy.", "keywords": [ "Business model", "On demand", "Business", "Marketing", "Commerce" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.14711/thesis-hdl151302" }, { "id": "https://openalex.org/W7135023145", "doi": "10.1080/24725854.2026.2639646", "title": "Optimizing inspection routes and schedules for infrastructure systems under stochastic decision-dependent failures", "year": 2026, "publication_date": "2026-03-11", "type": "article", "language": "en", "authors": [ "Juan-Alberto Estrada-Garcia", "Siqian Shen" ], "first_author": "Juan-Alberto Estrada-Garcia", "venue": "IISE Transactions", "publisher": "Taylor & Francis", "abstract": "Effective monitoring is vital for maintaining interconnected infrastructure systems, where components are prone to failure without proper servicing. However, designing inspection routes remains computationally difficult due to high complexity and inherent uncertainties of large-scale infrastructure systems. This paper investigates the deployment of multi-vehicle fleets, such as unmanned aerial vehicles (UAVs), to inspect spatially distributed components subject to uncertain travel times, inspection durations, and failure risks. Notably, the probability of component failure depends on inspection timing, creating decision-dependent (endogenous) uncertainty. We model this as a variant of a stochastic multi-vehicle routing problem and formulate a two-stage stochastic mixed-integer program based on finite samples. We propose a scenario decomposition framework that integrates column generation and random coloring techniques to accelerate subproblem resolution. We further provide theoretical analyses of the algorithm’s finite convergence and optimality guarantees under a user-specified probabilistic error tolerance. Numerical experiments on networks of varying topologies, including IEEE and EPANET systems, demonstrate the computational efficiency and effectiveness of our approaches. Across all large instances, our algorithm achieves an optimality gap below 4% and consistently outperforms state-of-the-art optimization solvers and the adaptive large neighborhood search as a heuristic benchmark.", "keywords": [ "Scheduling (production processes)", "Key (lock)", "Measure (data warehouse)", "Schedule" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1080/24725854.2026.2639646" }, { "id": "https://openalex.org/W3085475527", "doi": "10.1504/ijsnet.2020.10032031", "title": "Linear models for total coverage problem with connectivity constraints using multiple unmanned aerial vehicles", "year": 2020, "publication_date": "2020-01-01", "type": "article", "language": "en", "authors": [ "Khaled Ghédira", "Hichem Snoussi", "Fethi Mguis", "Amani Lamine" ], "first_author": "Khaled Ghédira", "venue": "International Journal of Sensor Networks", "publisher": "Inderscience Publishers", "abstract": "The use of unmanned aerial vehicles (UAVs) has recently increased both in civilian and military operations, and the planning of their routes is critical. This research investigates a routing problem in which a UAV network, equipped with sensors, covers a given area and maintains connectivity with its neighbouring UAVs and the base station, while respecting to the UAVs lifetime. To cover the area, two integer linear programming models are formulated to solve two problems optimally. In the first one, covering means that all positions should be visited. However, in the second one, covering means that every position should be covered at least by one UAV. Due to the limited communication radius of the UAVs, connectivity then has to find inter-UAVs routing paths to satisfy the communication between UAVs and the base. We verify by experiments that the models, using Cplex, can provide an optimal solution of different area dimensions.", "keywords": [ "Computer science", "Integer programming", "Cover (algebra)", "Routing (electronic design automation)", "Linear programming", "Base station", "Position (finance)", "Wireless sensor network", "Drone", "Real-time computing", "Computer network", "Mathematical optimization" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1504/ijsnet.2020.10032031" }, { "id": "https://openalex.org/W4413129017", "doi": "10.3233/pmst250095", "title": "Coordinated Swarm Operations of Unmanned Surface Vehicles for Coastal Surveillance and Search and Rescue Missions", "year": 2025, "publication_date": "2025-08-07", "type": "book-chapter", "language": "en", "authors": [ "Egidio D’Amato", "Silvia Pennino" ], "first_author": "Egidio D’Amato", "venue": "Progress in marine science and technology", "publisher": "IOS Press", "abstract": "Over the past decades, the use of marine autonomous systems has grown exponentially, thanks to their simplicity, versatility, and significant technological advancements. Unmanned Surface Vehicles (USVs) have emerged as indispensable tools for critical maritime applications, such as coastal surveillance, search and rescue (SAR) operations, and environmental monitoring, where speed, endurance, and precision are essential. These applications often involve challenges that are either too hazardous or too demanding for human operators, such as operating in adverse weather conditions or covering vast, inaccessible areas. Recent research has increasingly focused on cooperative formations or swarms of USVs and UAVs (Unmanned Aerial Vehicles). These formations not only enable the execution of complex missions more effectively than a single vehicle but also reduce the costs and individual payload requirements of each unit. Additionally, such formations improve the robustness and reliability of the system: in case of failure of one USV, the swarm can dynamically reconfigure itself, ensuring mission continuity and adaptability in real-time. Despite their potential, the effective coordination of USVs and UAVs in such swarms presents significant challenges. These include ensuring autonomous operation in dynamic environments, optimizing energy consumption given limited fuel or battery capacity, and enabling adaptive task allocation while maintaining real-time communication in potentially low-bandwidth conditions. Addressing these challenges is crucial, especially in time-critical SAR scenarios where rapid response and coverage are vital. This paper introduces a novel swarm coordination algorithm tailored to USV and UAV formations, based on an adaptation of the Vehicle Routing Problem (VRP). The algorithm optimizes the trajectories of multiple USVs while ensuring seamless collaboration with a mothership, which serves as a central hub for refueling, communication, and coordination. Numerical simulations developed in Matlab/Simulink are presented to validate the effectiveness of the algorithm across various coastal surveillance and SAR scenarios. The results demonstrate the potential of the approach to improve mission success rates and operational robustness in complex maritime environments.", "keywords": [ "Swarm behaviour", "Search and rescue", "Payload (computing)", "Computer science", "Adaptability", "Engineering", "Real-time computing", "Systems engineering", "Robot", "Computer security", "Artificial intelligence" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3233/pmst250095" }, { "id": "https://openalex.org/W4388675460", "doi": "10.53866/jimi.v3i3.249", "title": "ANALISIS DAN SIMULASI KECEPATAN LALU-LINTAS PADA SIMPANG JALAN MENGGUNAKAN PERANGKAT LUNAK PTV VISSIM STUDENT VERSION (STUDI KASUS: SIMPANG BERGOTA, KOTA SEMARANG)", "year": 2023, "publication_date": "2023-08-31", "type": "article", "language": "en", "authors": [ "Ryan Hasnan Harahap", "Bambang Haryadi" ], "first_author": "Ryan Hasnan Harahap", "venue": "Citizen Jurnal Ilmiah Multidisiplin Indonesia", "publisher": null, "abstract": "One of the intersections that has a significant role in the city of Semarang is the Bergota Intersection. The level of traffic density and crowds at this point of the road is quite significant because it is a route from Jalan Pandanaran and towards the Central Java Regional Police, which passes Jalan Kyai Saleh—a transportation movement system of various types and characteristics of the traffic that occurs. The method used in this study is observation by collecting data as input at PTV Vissim, such as geometric data at intersections, vehicle routes, composition, vehicle volume, vehicle speed, and traffic time cycle using the Traffic Counting method at Bergota Intersections based on analysis of recorded results. The drone video then enumerates the traffic flow by counting the vehicles passing through the survey point. Modeling and simulation results using PTV Vissim in the existing conditions at 11:10-11:20 at the Bergota Intersection using vehicle volume data taken from the field show that the highest speed is 20.27 Km/hour on Jalan Minister Supeno. The lowest speed is 14.59 km/hour on Jalan Kyai Saleh Bergota. Modeling and simulation results using PTV Vissim in the existing conditions at 11.10-11.20 Simpang Bergota using vehicle volume data taken from the field show that the highest delay is 41.19 seconds on Jalan Kyai Saleh Bergota - Jalan Kyai Saleh Veteran. At the same time, the lowest delay is 5.59 seconds on Jalan Dr. Kariadi – Jalan Kyai Saleh Bergota.", "keywords": [ "VisSim", "Traffic volume", "Intersection (aeronautics)", "Transport engineering", "Traffic flow (computer networking)", "Travel time", "Computer science", "Geography", "Mathematics", "Engineering", "Computer security" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://journal.das-institute.com/index.php/citizen-journal/article/download/249/229" }, { "id": "https://openalex.org/W7163541705", "doi": "10.2514/6.2026-4291", "title": "Reliability-Oriented Optimization of a Command-and-Control System for Advanced Air Mobility", "year": 2026, "publication_date": "2026-06-04", "type": "conference-paper", "language": null, "authors": [ "Delonte Goodman", "Elaheh Sabziyan Varnousfaderani", "Syed Arbab Mohd Shihab", "Ruben Del Rosario" ], "first_author": "Delonte Goodman", "venue": null, "publisher": null, "abstract": "Reliability engineering plays a critical role in ensuring safe operations in Advanced Air Mobility (AAM). This study presents a system-level optimization framework for evaluating and improving the reliability of a generic Command-and-Control (C2) system architecture based on the Mobile Testing and Evaluation Center (MTEC) unit developed by Parallax Advanced Research. The C2 architecture is decomposed into several primary subsystems, including power, communication, sensing, control (hardware and software), and data storage, allowing reliability to be evaluated at both the subsystem and overall system levels. The optimization model aims to maximize total system reliability, sensor detection probability, and detection range by modifying the sensing and control subsystems. The sensing subsystem includes six sensor types: radar, radio frequency (RF), Automatic Dependent Surveillance–Broadcast (ADS-B), acoustic sensors, Remote Identification (Remote ID), and optical cameras. The baseline control subsystem consists of eight primary workstations capable of supporting up to 20 UAVs. Additional workstations can be incorporated to improve overall system reliability. The decision variables include the number of sensors of each type and the number of additional workstations added to the control subsystem. The objective function maximizes system reliability, detection probability, and detection range while satisfying budget constraints and a minimum reliability requirement of 95%. The model is evaluated across budgets ranging from $30,000 to $150,000 and UAV fleet sizes from 20 to 50 vehicles. For a representative scenario with 20 UAVs and a $60,000 budget, the optimal configuration includes four backup workstations, sixteen ADS-B sensors, one Remote ID sensor, and one optical camera. This configuration achieves approximately 97% system reliability at a total cost of $59,793.80. The results demonstrate that both system budget and fleet size significantly influence reliability and optimal resource allocation.", "keywords": [ "Work (physics)", "Control system", "Field (mathematics)", "Process (computing)", "Noise (video)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2514/6.2026-4291" }, { "id": "https://openalex.org/W4414117102", "doi": "10.1109/iccc65529.2025.11149011", "title": "Trusted Routing for Blockchain-Enabled Low-Altitude Intelligent Networks", "year": 2025, "publication_date": "2025-08-10", "type": "conference-paper", "language": "en", "authors": [ "Sijie He", "Ziye Jia", "Qiuming Zhu", "Fuhui Zhou", "Qihui Wu" ], "first_author": "Sijie He", "venue": null, "publisher": null, "abstract": "Due to the scalability and portability, the low-altitude intelligent networks (LAINs) are essential in various fields such as surveillance and disaster rescue. However, in LAINs, unmanned aerial vehicles (UAVs) are characterized by the distributed topology and high dynamic mobility, and vulnerable to security threats, which may degrade the routing performance for data transmission. Hence, how to ensure the routing stability and security of LAINs is a challenge. In this paper, we focus on the routing process in LAINs with multiple UAV clusters and propose the blockchain-enabled zero-trust architecture to manage the joining and exiting of UAVs. Furthermore, we formulate the routing problem to minimize the end-to-end (E2E) delay, which is an integer linear programming and intractable to solve. Therefore, considering the distribution of LAINs, we reformulate the routing problem into a decentralized partially observable Markov decision process. With the proposed soft hierarchical experience replay buffer, the multi-agent double deep Q-network based adaptive routing algorithm is designed. Finally, simulations are conducted and numerical results show that the total E2E delay of the proposed mechanism decreases by 22.38% than the benchmark on average.", "keywords": [ "Static routing", "Dynamic Source Routing", "Routing (electronic design automation)", "Policy-based routing", "Link-state routing protocol", "Hierarchical routing", "Multipath routing", "Scalability", "Benchmark (surveying)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/iccc65529.2025.11149011" }, { "id": "https://openalex.org/W4416506208", "doi": "10.48550/arxiv.2506.22745", "title": "Trusted Routing for Blockchain-Enabled Low-Altitude Intelligent Networks", "year": 2025, "publication_date": "2025-06-28", "type": "preprint", "language": "en", "authors": [ "Sijie He", "Ziye Jia", "Qiuming Zhu", "Fuhui Zhou", "Qihui Wu" ], "first_author": "Sijie He", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Due to the scalability and portability, the low-altitude intelligent networks (LAINs) are essential in various fields such as surveillance and disaster rescue. However, in LAINs, unmanned aerial vehicles (UAVs) are characterized by the distributed topology and high dynamic mobility, and vulnerable to security threats, which may degrade the routing performance for data transmission. Hence, how to ensure the routing stability and security of LAINs is a challenge. In this paper, we focus on the routing process in LAINs with multiple UAV clusters and propose the blockchain-enabled zero-trust architecture to manage the joining and exiting of UAVs. Furthermore, we formulate the routing problem to minimize the end-to-end (E2E) delay, which is an integer linear programming and intractable to solve. Therefore, considering the distribution of LAINs, we reformulate the routing problem into a decentralized partially observable Markov decision process. With the proposed soft hierarchical experience replay buffer, the multi-agent double deep Q-network based adaptive routing algorithm is designed. Finally, simulations are conducted and numerical results show that the total E2E delay of the proposed mechanism decreases by 22.38\\% than the benchmark on average.", "keywords": [ "Static routing", "Dynamic Source Routing", "Routing (electronic design automation)", "Policy-based routing", "Link-state routing protocol", "Hierarchical routing", "Multipath routing", "Scalability", "Benchmark (surveying)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2506.22745" }, { "id": "https://openalex.org/W4415181457", "doi": "10.48550/arxiv.2506.20804", "title": "Online Planning for Cooperative Air-Ground Robot Systems with Unknown Fuel Requirements", "year": 2025, "publication_date": "2025-06-25", "type": "preprint", "language": "en", "authors": [ "Agarwal, Ritvik", "Behnoushsadat Hatami", "Alvika Gautam", "Parikshit Maini" ], "first_author": "Agarwal, Ritvik", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "We consider an online variant of the fuel-constrained UAV routing problem with a ground-based mobile refueling station (FCURP-MRS), where targets incur unknown fuel costs. We develop a two-phase solution: an offline heuristic-based planner computes initial UAV and UGV paths, and a novel online planning algorithm that dynamically adjusts rendezvous points based on real-time fuel consumption during target processing. Preliminary Gazebo simulations demonstrate the feasibility of our approach in maintaining UAV-UGV path validity, ensuring mission completion. Link to video: https://youtu.be/EmpVj-fjqNY", "keywords": [ "Rendezvous", "Planner", "Fuel efficiency", "Online algorithm", "Motion planning", "Path (computing)", "Mobile robot", "Trajectory" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2506.20804" }, { "id": "https://openalex.org/W4417197159", "doi": "10.1080/21680566.2025.2586134", "title": "Collaborative routing optimization of ground delivery vehicles and unmanned aerial vehicles considering time window constraints", "year": 2025, "publication_date": "2025-12-10", "type": "article", "language": "en", "authors": [ "Weilan Mao", "Meng Xu", "Yupeng Jiang", "Xianyu Wu" ], "first_author": "Weilan Mao", "venue": "Transportmetrica B Transport Dynamics", "publisher": "Taylor & Francis", "abstract": "In order to improve the logistics delivery efficiency, this paper proposes a collaborative ground delivery vehicle (GDV) and unmanned aerial vehicle (UAV) routing problem with time window constraints, which takes into account the actual airspace and road conditions. A mixed-integer programming (MIP) model is established. The proposed model approaches the interaction between the GDV and the UAV through two binary variables, allowing flexible collaboration to improve delivery efficiency. Numerical experiments reveal that customer groups who are time-sensitive and geographically dispersed are more suitable for collaborative delivery of the GDV and the UAV. Sensitivity analysis reveals critical points where airspace and road conditions have a significant impact on the efficiency of collaborative delivery. This study provides insights for the practical application of collaborative delivery with the GDV and the UAV in logistics operations management.", "keywords": [ "Window (computing)", "Routing (electronic design automation)", "Key (lock)", "Vehicle routing problem", "Optimization algorithm", "Window of opportunity" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1080/21680566.2025.2586134" }, { "id": "https://openalex.org/W7110508507", "doi": null, "title": "Çok amaçlı rotalama için sezgisel ve kesin yaklaşımlar.", "year": 2013, "publication_date": "2013-01-01", "type": "dissertation", "language": "en", "authors": [ "Tezcaner Öztürk, Diclehan" ], "first_author": "Tezcaner Öztürk, Diclehan", "venue": "OpenMETU (Middle East Technical University)", "publisher": "Middle East Technical University", "abstract": "In this thesis, we consider the bi-objective routing problem. This problem is a combination of the bi-objective shortest path problem (finding the efficient arcs between nodes) and the bi-objective traveling salesperson problem (finding the efficient tours composed of efficient arcs). We develop solution procedures to find efficient tours. We consider two different terrain structures; discretized terrain and continuous terrain. In the discretized terrain, the terrain is approximated with grids and we allow the vehicle to move between adjacent grid points. In the continuous terrain, we consider a two dimensional plane and there are no restrictions on the movement of the vehicle. To find the most preferred solution, we first develop a general interactive algorithm for a decision maker whose preferences are consistent with an underlying quasiconvex function to be minimized for any bi-criteria integer program. We then apply our algorithm to the bi-objective routing problem. In each iteration of the algorithm, we find a number of efficient tours made up of the efficient arcs. For this, we initially find all efficient arcs between all pairs of nodes and introduce these arcs to the interactive algorithm. We establish some rules that decrease the number of efficient arcs required for finding an efficient tour that satisfies some constraints. We demonstrate the approach on the routing problem of unmanned air vehicles (UAVs) which are assumed to travel on a discretized terrain. We also study the routing problem in continuous terrain, specifically for the UAV routing problem. We develop methods to find the approximate efficient frontier of the shortest path problem between each node pair. We then find the efficient tours that use a subset of the efficient arcs using a mixed integer nonlinear program. We also discuss the implementation of the interactive algorithm for the routing problem in the continuous terrain.", "keywords": [ "Discretization", "Routing (electronic design automation)", "Shortest path problem", "Terrain", "Grid", "Path (computing)", "Cutting-plane method", "K shortest path routing" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://hdl.handle.net/11511/23189" }, { "id": "https://openalex.org/W4319959461", "doi": "10.17760/d20474697", "title": "Leveraging geometric approaches in data analytics and optimization", "year": 2022, "publication_date": "2022-01-01", "type": "dissertation", "language": "en", "authors": [ "Reyhaneh Mohammadi" ], "first_author": "Reyhaneh Mohammadi", "venue": null, "publisher": null, "abstract": "Geometric optimization is mainly focused on problems dealing with allocating a finite set ofresources to fulfill the demand that is dispersed over a geographical region. Exploiting the geometric characteristics of the problem such as shape, contiguity, and convexity, and integrating them into the optimization techniques, often helps to solve such problems more efficiently. This dissertation studies three different problems in this direction. The goal of the research is to find efficient solutions for some geometric optimization problems that are proved to be NP-hard. In the first project, we study a space partitioning problem and deal with finding an efficient partitioning scheme for generating treemaps that is a widely used tool in the data visualization field. A treemap takes a weighted tree structure and visualizes its leaves in a nested planar geometric shape such that it forms a partitioning of space with areas of sub-regions proportional to the weight of the nodes in the tree. We present an optimization model and five new algorithms including two divide and conquer approaches and three spiral treemap algorithms for this problem. Our optimization model generates superior treemaps that could serve as a benchmark for comparing the quality of computationally more efficient algorithms. We also presented an approximation algorithm with factor around 1.2 for this problem. In the second project, we study the k-medians problem, a well-known location optimization problem, in which the objective is to establish k facilities in a way to minimize the total (average) distance from each demand point to its nearest facility. Here, we assume that the demand points form a continuum in a polygonal region. This problem is called the continuous k-medians problem. We developed two approximation algorithms for this problem with approximation factors less than 1.81 and 1.64. We showed that the average performance is far better and within 1.2 factor of the optimal solution, which we further improved slightly by applying a modification in the algorithms. In the third project, we study a delivery routing problem where we combine trucks and drones to serve all demand points in a coordinated system. In this problem, the truck not only contributes to the actual delivery operation but also is used as a drone dispatcher and the battery charger. We developed an algorithm based on Variable Neighborhood Search for this problem.--Author's abstract", "keywords": [ "Computer science", "Optimization problem", "Mathematical optimization", "Benchmark (surveying)", "Convexity", "Tree (set theory)", "Set (abstract data type)", "Algorithm", "Theoretical computer science", "Mathematics" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://repository.library.northeastern.edu/files/neu:4f18k883k/fulltext.pdf" }, { "id": "https://openalex.org/W4293510427", "doi": "10.37675/jat.2022.00129", "title": "Finding Second Hierarchy Points for Cooperative Multi-Vehicles Delivery Route with P-median and Voronoi Diagram in Various Driving Area", "year": 2022, "publication_date": "2022-08-20", "type": "article", "language": "en", "authors": [ "Kyung-Hyun Kim", "Seong-Woo Kim" ], "first_author": "Kyung-Hyun Kim", "venue": "Academic Society for Appropriate Technology", "publisher": null, "abstract": "With the recent development of autonomous driving technology, various unmanned vehicles such as serving robots, mobile delivery robots, and drones have been developed. However, each robot has a different driving area, so it is necessary to find cooperative route for optimal unmanned delivery. In this paper, a cooperative scenario of an autonomous courier vehicle and five mobile delivery vehicles perform unmanned delivery tasks. The courier vehicle moves from a warehouse to the second hierarchy point, and drops off the five mobile delivery vehicles in order to transport goods to the doorstep of the destinations. We define the second hierarchy point by using the P-Median method and the Voronoi diagram method, and compare the results of each method using real map data. The points using P-Median are calculated based on the actual road distance and have shorter average moving distance than the Voronoi diagram method. The Voronoi diagram uses the Euclidian distance based on location information, which defines the second hierarchy point faster than the P-Median method. By comparing the results, we consider the Voronoi diagram method have capability in the cooperative multi-vehicle delivery route problem. The result of this paper have potential to be used in rough and remote areas where road information and infrastructure are lacking.", "keywords": [ "Voronoi diagram", "Computer science", "Point (geometry)", "Hierarchy", "Mobile robot", "Diagram", "Real-time computing", "Simulation", "Robot", "Artificial intelligence", "Mathematics" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.e-jat.org/upload/pdf/jat-2022-00129.pdf" }, { "id": "https://openalex.org/W3199158464", "doi": "10.23939/istcmtm2021.03.042", "title": "MODELING A NETWORK OF UNMANNED AERIAL VEHICLES", "year": 2021, "publication_date": "2021-01-01", "type": "article", "language": "en", "authors": [ "Mykola Mykyjchuk", "Nataliya Zihanshyn" ], "first_author": "Mykola Mykyjchuk", "venue": "Measuring Equipment and Metrology", "publisher": null, "abstract": "The research concerns the methods of UAV group control in networks with duplex communication between nodes built on the \"client-server\" architecture. Such systems belong to self-organized networks with variable topology. It is important to study the allowable parameters of deviation from the task in the management of a group of UAVs and analysis of the network topology for the group flighting. The network was optimized according to the Ant Colony algorithm ACO. The application of different types of algorithms prevents routing problems in networks, such as ANTMANET, AntNet, ACODV and others. Methods of coordination of group joint actions are considered. A method is proposed by which the optimal number of UAVs operated by one operator can be determined and the traffic intensity of the communication channel is calculated, as well as the bandwidth and packet transmission delay are estimated.", "keywords": [ "Computer science", "Computer network", "Network packet", "Ant colony optimization algorithms", "Network topology", "Telecommunications network", "Distributed computing", "Topology (electrical circuits)", "Real-time computing", "Artificial intelligence", "Engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://science.lpnu.ua/sites/default/files/journal-paper/2021/sep/24926/8236.pdf" }, { "id": "https://openalex.org/W3210590189", "doi": "10.13016/f2fx-1ov9", "title": "Adjustment Factors and Applications for Analytic Approximations of Tour Lengths", "year": 2021, "publication_date": "2021-01-01", "type": "dissertation", "language": "en", "authors": [ "Young‐Min Choi" ], "first_author": "Young‐Min Choi", "venue": "University Libraries (University of Maryland)", "publisher": "University of Maryland, College Park", "abstract": "The shortest tour distance for visiting all points exactly once and returning to the origin is computed by solving the well-known Traveling Salesman Problem (TSP). Due to the large computational effort needed for optimizing TSP tours, researchers have developed approximations that relate the average length of TSP tours to the number of points n visited per tour. The most widely used approximation formula has a square root form: √n multiplied by a coefficient β. Although the existing models can effectively approximate the distance for conventional vehicles with large capacities (e.g., delivery trucks) where n is large, approximations that seek to cover large ranges of n, possibly to infinity, tend to yield poorer results for the small n values. Thus, this dissertation focuses on approximation models for the small n values, which are needed for many practical applications, such as for some recent delivery alternatives (e.g., drones). The proposed models show promise in analyzing the real-world problems in which actual tours serve few customers due to limited vehicle capacity and incorporate realistic constraints, such as the effects of a starting point location, geographical restrictions on movements, demand patterns, and service area shapes. The dissertation may open new research avenues for analyzing the new transportation alternatives and provide guidelines to planners for choosing appropriate models in designing or evaluating transportation problems. Approximation models are estimated from the following experiments: 1) a total of 60 cases are developed by considering various factors, such as point distributions and shapes of service areas. 2) Solution methods for TSP instances are compared and chosen. 3) After the TSPs are optimized for each n, the TSP tour lengths are averaged. 4) Lastly, models for the averaged TSP tour lengths are fitted with ordinary least squares (OLS) regression. After the approximations are developed, some possible extensions are explored. First, adjustment factors are designed to integrate the 60 cases within one equation. With those factors, it can be understood how approximation varies with each classification. Next, the approximations considering stochastic customer presence (i.e., probabilistic TSP) are proposed. Third, the approximated tour lengths are compared with the optimal solutions of vehicle routing problem (VRP) in actual rural and urban delivery networks. Here, some additional factors, such as a circuity factor and service zone shape, are discussed. Lastly, the proposed methodology is applied to formulate and explore various types of existing and hypothetical delivery alternatives.", "keywords": [ "Mathematics", "Computer science", "Applied mathematics", "Econometrics", "Operations research" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://hdl.handle.net/1903/27698" }, { "id": "https://openalex.org/W7211960268", "doi": "10.3390/a19090772", "title": "A Mixed-Integer Programming and Branch-and-Cut Approach for Multi-Unmanned Aerial Vehicle Cooperative Scheduling in Mountain Forest Fire Surveillance", "year": 2026, "publication_date": "2026-09-08", "type": "article", "language": "en", "authors": [ "Jun Zhang", "素安 许", "Bo Liu" ], "first_author": "Jun Zhang", "venue": "Algorithms", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Mountainous forest-fire surveillance with multiple UAVs is constrained by rugged terrain, time-varying winds, and temperature-dependent battery derating, which jointly affect endurance and route feasibility. Most existing patrol models simplify these effects through planar routing and constant energy-consumption assumptions. This study develops an energy-aware mixed-integer linear programming model that integrates terrain-induced climbing, period-dependent wind conditions, and battery derating into multi-UAV mission scheduling. Two valid inequalities—a symmetry-breaking cut and a fleet-size lower-bound cut—are embedded in a branch-and-cut framework to improve exact solution efficiency. The proposed method is applicable to year-round patrol planning under season-dependent meteorological conditions. In the Jinyun Mountain, Chongqing, case study, it is evaluated under three representative seasonal scenarios (summer, autumn, and winter), with particular emphasis on the summer pre-fire period as the primary high-risk operating scenario. Within the same computational time limit, the proposed approach reduces total flight distance by approximately 36–49% and energy consumption by approximately 61–74% relative to large neighborhood search and ant colony optimization, while using fewer UAVs to complete the same patrol tasks. These results demonstrate that explicitly coupling environmental and battery constraints can improve the operational efficiency and energy feasibility of multi-UAV wildfire surveillance, and can provide practical decision support for daily wildfire-prevention patrol planning in mountainous environments.", "keywords": [ "Scheduling (production processes)", "Energy consumption", "Linear programming", "Route planning", "Routing (electronic design automation)", "Integer programming", "Battery (electricity)", "Derating" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3390/a19090772" }, { "id": "https://openalex.org/W7204846743", "doi": "10.1145/3817124.3817329", "title": "Analysis for the Impact of China's Low-Altitude Economy on Employment Based on Canonical Correlation Model and Its Algorithm Implementation", "year": 2026, "publication_date": "2026-03-27", "type": "conference-paper", "language": null, "authors": [ "Yongying Su" ], "first_author": "Yongying Su", "venue": null, "publisher": null, "abstract": "To accurately reveal the mechanism linking the low-altitude economy and employment, this paper uses core indicators in the fields of general aviation and unmanned aerial vehicles (UAVs) as proxy variables for the low-altitude economy, and employs employment indicators across three industries and urban-rural areas as employment dimension metrics. Based on statistical data from the Civil Aviation Administration of China and the National Bureau of Statistics, it applies canonical correlation analysis to explore the impact of the low-altitude economy on employment, and presents corresponding algorithm steps and related computer programming languages. The research shows the following: First, in general aviation, the number of enterprises and fleet size have an impact on employment, while flight hours have a minimal effect. Moreover, general aviation mainly affects employment in the primary and tertiary industries, with little difference in its impact on urban and rural employment. Second, among the five UAV indicators, the number of valid pilot licenses and the number of corporate unit users have a significant impact on employment, with the latter having a greater impact than the former; the number of registered UAVs, the duration of commercial flight activities, and the number of individual users have no significant impact on employment. Third, UAVs have the most obvious impact on employment in the primary industry, followed by the tertiary industry, and finally the secondary industry. Fourth, UAVs have a greater impact on urban employment than on rural employment. The contribution of this paper lies in establishing a quantitative analysis framework for the correlation between the low-altitude economy and employment, providing empirical support for optimizing industrial-employment coordination policies, and offering decision-making references for the coordinated advancement of high-quality development of the low-altitude economy and the expansion and improvement of employment quality.", "keywords": [ "Aviation", "Economic impact analysis", "Dimension (graph theory)", "China", "Civil aviation", "Canonical correlation", "Proxy (statistics)", "Duration (music)", "General aviation" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1145/3817124.3817329" }, { "id": "https://openalex.org/W4404354180", "doi": "10.48550/arxiv.2410.20849", "title": "Optimal planning for heterogeneous autonomous teams with precedence and compatibility constraints and its application on power grid inspection with Unmanned Aerial Vehicles", "year": 2024, "publication_date": "2024-10-28", "type": "preprint", "language": "en", "authors": [ "Antonio Sojo", "Iván Maza", "Anı́bal Ollero" ], "first_author": "Antonio Sojo", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "In this paper we address the optimal planning of autonomous teams for general purpose tasks including a wide spectrum of situations: from project management of human teams to the coordination of an automated assembly lines, focusing in the automated inspection of power grids. There exist many methods for task planning. However, the vast majority of such methods are conceived for very specific problems or situations and are often based in certain assumptions and simplifications. Consider for example all the different algorithms developed to solve the Vehicle Routing Problem (VRP) for all the different vehicles and environment characteristics. This means that no robust general planning method exists and that a possible extension of any of them to a more general situation is often not a trivial task. To address this, we propose a new truly general method ultimately based on a generalization of the Traveling Salesman Problem (TSP). We call this new model the Heterogeneous Multi-worker Task Planning Problem (HMWTPP). It provides a natural framework to model many situations typical in task planning of all kinds. Task-Worker compatibility, precedence/order and time-windows constraints are already encoded into the HMWTPP while it can be easily extended to include weight capacity or battery per node constraints in an intuitive manner. Several classical TSP problems included in the TSPLIB library are solved for validation and performance analysis of HMWTPP showing a comparable numerical performance to that of existing models. In addition, a synthetic example modeling an automated assembly line is analyzed to prove the potential capabilities of the HMWTPP in real-life scenarios. Ultimately, we focus in the computation of the optimal plan of Unmanned Aerial Vehicles (UAVs) specifically in the context of automated inspection of electrical power grids.", "keywords": [ "Compatibility (geochemistry)", "Grid", "Power grid", "Computer science", "Environmental science", "Marine engineering", "Engineering", "Power (physics)", "Geology", "Geodesy" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2410.20849" }, { "id": "https://openalex.org/W4387634427", "doi": "10.48550/arxiv.2310.07729", "title": "Energy-Aware Routing Algorithm for Mobile Ground-to-Air Charging", "year": 2023, "publication_date": "2023-09-30", "type": "preprint", "language": "en", "authors": [ "Bill Cai", "Lu, Fei", "Lifeng Zhou" ], "first_author": "Bill Cai", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "We investigate the problem of energy-constrained planning for a cooperative system of an Unmanned Ground Vehicles (UGV) and an Unmanned Aerial Vehicle (UAV). In scenarios where the UGV serves as a mobile base to ferry the UAV and as a charging station to recharge the UAV, we formulate a novel energy-constrained routing problem. To tackle this problem, we design an energy-aware routing algorithm, aiming to minimize the overall mission duration under the energy limitations of both vehicles. The algorithm first solves a Traveling Salesman Problem (TSP) to generate a guided tour. Then, it employs the Monte-Carlo Tree Search (MCTS) algorithm to refine the tour and generate paths for the two vehicles. We evaluate the performance of our algorithm through extensive simulations and a proof-of-concept experiment. The results show that our algorithm consistently achieves near-optimal mission time and maintains fast running time across a wide range of problem instances.", "keywords": [ "Computer science", "Travelling salesman problem", "Routing (electronic design automation)", "Unmanned ground vehicle", "Range (aeronautics)", "Energy (signal processing)", "Real-time computing", "Monte Carlo tree search", "Algorithm", "Mobile robot", "Monte Carlo method", "Mathematical optimization" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2310.07729" }, { "id": "https://openalex.org/W2791126802", "doi": null, "title": "Routing Vehicles with Motion, Resource and Mission Constraints: Algorithms and Bounds", "year": 2015, "publication_date": "2015-04-20", "type": "dissertation", "language": "en", "authors": [ "Satyanarayana G. Manyam" ], "first_author": "Satyanarayana G. Manyam", "venue": "OakTrust (Texas A&M University Libraries)", "publisher": "Texas A&M University", "abstract": "Unmanned Aerial Vehicles (UAVs) are used for several military and civil applications such as reconnaissance, surveillance etc. The UAVs, due to their design and size limitations, have inherent kinematic constraints, communication constraints etc. This thesis considers the path planning problems for UAVs while satisfying a class of constraints.\\n\\nWe consider a multiple depot UAV routing problem, where the vehicles have motion constraints due to bound on their yaw-rate. For a given set of targets, it is required that each target should be on the path of at least one of the vehicles. This problem is hard to solve and currently there are no algorithm that could find an optimal solution. We aim to find tight lower bounds for this problem via Lagrangian relaxation. The complicating constraints of the problem are relaxed, and the cost function is penalized whenever those constraints are violated. This reduces the original problem to a known problem - a standard multiple traveling salesmen problem (MTSP). Simulation results are presented to show that this method significantly improved the existing lower bounds.\\n\\nThe second problem we consider is the routing of UAVs in GPS denied environments and with limited communication range. Two different architectures for navigation assisted by an array of Unattended Ground Sensors (UGSs) are considered. In the first case, when an UAV localizes itself by communicating with an UGS, the second UAV can orbit around the first UAV. Contact with UGS allows them to act as beacons for relative navigation eliminating the need for GPS. A randomized algorithm with approximation ratio of 9/2 and a transformation technique are developed to solve this problem. In the second architecture, when two UAVs are located at two different UGSs, the third UAV localizes by triangulation using range measurements from the first two UAVs. This three UAV case is solved using a graph transformation technique to pose it as an one-in-a-set TSP. The solutions produced by these algorithms were used to simulate the UAV routing on AMASE, a simulation tool for routing UAVs developed by the Air Force Research Laboratories.", "keywords": [ "Computer science", "Resource (disambiguation)", "Routing (electronic design automation)", "Motion (physics)", "Vehicle routing problem", "Algorithm", "Artificial intelligence", "Computer network" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://hdl.handle.net/1969.1/155175" }, { "id": "https://openalex.org/W3024661974", "doi": null, "title": "Coverage Path Planning for a Moving Vehicle", "year": 2020, "publication_date": "2020-04-21", "type": "dissertation", "language": "en", "authors": [ "Barry Gilhuly" ], "first_author": "Barry Gilhuly", "venue": "UWSpace (University of Waterloo)", "publisher": "University of Waterloo", "abstract": "A simple coverage plan called a Conformal Lawn Mower plan is demonstrated. This plan enables a UAV to fully cover the route ahead of a moving ground vehicle. The plan requires only limited knowledge of the ground vehicle's future path. For a class of curvature-constrained ground vehicle paths, the proposed plan requires a UAV velocity that is no more than twice the velocity required to cover the optimal plan. Necessary and sufficient UAV velocities, relative to the ground vehicle velocity, required to successfully cover any path in the curvature restricted set are established. In simulation, the proposed plan is validated, showing that the required velocity to provide coverage is strongly related to the curvature of the ground vehicle's path. The results also illustrate the relationship between mapping requirements and the relative velocities of the UAV and ground vehicle. Next, I investigate the challenges involved in providing timely mapping information to a moving ground vehicle where the path of that vehicle is not known in advance. I establish necessary and sufficient UAV velocities, relative to the ground vehicle velocity, required to successfully cover any path the ground vehicle may follow. Finally, I consider a reduced problem for sensor coverage ahead of a moving ground vehicle. Given the ground vehicle route, the UAV planner calculates the regions that must be covered and the time by which each must be covered. The UAV planning problem takes the form of an Orienteering Problem with Time Windows (OPTW). The problem is cast the problem as a Mixed Integer Linear Program (MILP) to find a UAV path that maximizes the area covered within the time constraints dictated by the moving ground vehicle. To improve scalability of the proposed solution, I prove that the optimization can be partitioned into a set of smaller problems, each of which may be solved independently without loss of overall solution optimality. This divide and conquer strategy allows faster solution times, and also provides higher-quality solutions when given a fixed time budget for solving the MILP. We also demonstrate a method of limited loss partitioning, which can perform a trade-off between improved solution time and a bounded objective loss.", "keywords": [ "Motion planning", "Path (computing)", "Computer science", "Aeronautics", "Transport engineering", "Operations research", "Engineering", "Artificial intelligence", "Computer network" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://hdl.handle.net/10012/15775" }, { "id": "https://openalex.org/W2950949451", "doi": "10.48550/arxiv.1711.04936", "title": "A Two-Stage Approach for Routing Multiple Unmanned Aerial Vehicles with Stochastic Fuel Consumption", "year": 2017, "publication_date": "2017-11-14", "type": "preprint", "language": "en", "authors": [ "Saravanan Venkatachalam", "Kaarthik Sundar", "Sivakumar Rathinam" ], "first_author": "Saravanan Venkatachalam", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "The past decade has seen a substantial increase in the use of small unmanned aerial vehicles (UAVs) in both civil and military applications. This article addresses an important aspect of refueling in the context of routing multiple small UAVs to complete a surveillance or data collection mission. Specifically, this article formulates a multiple-UAV routing problem with the refueling constraint of minimizing the overall fuel consumption for all of the vehicles as a two-stage stochastic optimization problem with uncertainty associated with the fuel consumption of each vehicle. The two-stage model allows for the application of sample average approximation (SAA). Although the SAA solution asymptotically converges to the optimal solution for the two-stage model, the SAA run time can be prohibitive for medium- and large-scale test instances. Hence, we develop a tabu-search-based heuristic that exploits the model structure while considering the uncertainty in fuel consumption. Extensive computational experiments corroborate the benefits of the two-stage model compared to a deterministic model and the effectiveness of the heuristic for obtaining high-quality solutions.", "keywords": [ "Fuel efficiency", "Vehicle routing problem", "Computer science", "Tabu search", "Mathematical optimization", "Context (archaeology)", "Routing (electronic design automation)", "Heuristic", "Constraint (computer-aided design)", "Exploit", "Operations research", "Engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/1711.04936" }, { "id": "https://openalex.org/W1580144855", "doi": "10.23919/ecc.2009.7074582", "title": "Event-based energy optimum route planning in the context of Unmanned Aerial Vehicles for multi-objective exploration missions", "year": 2009, "publication_date": "2009-08-01", "type": "conference-paper", "language": "en", "authors": [ "Georgios P. Kladis", "J.T. Economou", "Antonios Tsourdos", "Brian A. White", "K. Knowles" ], "first_author": "Georgios P. Kladis", "venue": null, "publisher": null, "abstract": "The shortest routing problem is revisited for the case of a dynamically changing environment in an Unmanned Aerial Vehicle (UAV) exploration mission. The latter is represented by energy graphs motivated by [1] and [2]. It is assumed that information about the map is received while on flight due to environmental events. In addition, UAVs are required, while on mission, to “explore” the map which involves extracting as much intelligence as possible and traversing it in the most safe flyable manner. Hence, the UAV should be capable of integrating knowledge from a variety of sources and re-plan its mission, in order to fulfil objectives. Furthermore, prior knowledge is considered to reduce computational costs. Depending on the decision making process, the notion of a “temporary” optimum path can be of physical and functional sense. The problem is modeled as a multistage decision making process, where each stage is triggered by an event. Given the current availability between paths, the objective is to devise a policy that leads from an origin or current known location to a destination node while traversing the unknown region of interest with the minimal energy requirements.", "keywords": [ "Traverse", "Computer science", "Context (archaeology)", "Event (particle physics)", "Process (computing)", "Node (physics)", "Motion planning", "Routing (electronic design automation)", "Plan (archaeology)", "Variety (cybernetics)", "Energy (signal processing)", "Real-time computing" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.23919/ecc.2009.7074582" }, { "id": "https://openalex.org/W7152360442", "doi": null, "title": "МОДЕЛЮВАННЯ Й АНАЛІЗ ЛОГІСТИЧНИХ МАРШРУТІВ ПОСТАЧАННЯ ТА ЗБУТУ ВИСОКОТЕХНОЛОГІЧНОЇ ПРОДУКЦІЇ З ОГЛЯДУ НА РІЗНІ ВИДИ ТРАНСПОРТУ", "year": 2026, "publication_date": "2026-03-12", "type": "article", "language": "en", "authors": [ "Юрій Полупан", "Ольга Володимирівна Малєєва", "Максим Кікоть" ], "first_author": "Юрій Полупан", "venue": "The Scientific Issues of Ternopil Volodymyr Hnatiuk National Pedagogical University Series pedagogy", "publisher": "Ternopil Volodymyr Hnatiuk National Pedagogical University", "abstract": "The subject of the article is the processes of organizing and optimizing the logistics of supply and distribution of high-tech defense products under wartime conditions, in particular, the development of a network-based logistics structure taking into account reverse flows, risks, and limited resources. The purpose of the study is to develop a model of supply and distribution routes that optimizes delivery time and costs while simultaneously reducing the risks of cargo loss and increasing the transport utilization rate. Tasks to be solved: to form a generalized combined structure of the logistics chain; to identify and analyze the main parameters of supply and distribution routes across different modes of transport; to develop a computer experiment plan; to conduct simulation modeling of supply and distribution routes; and to develop practical recommendations for implementing logistics projects under wartime conditions. Research methods and tools: a systems approach, simulation modeling, graphical and comparative analysis, and the AnyLogic platform. Results obtained: A combined structure of the logistics chain for a military drone manufacturing enterprise with multiple route options is schematically presented. A model is proposed that formalizes the logistics chain as a network of nodes and arcs within a transport system and includes a set of input parameters describing vehicles, routes, and order characteristics. The essence of the computer experiment lies in reproducing the properties of infrastructure and logistics projects using a discrete-event simulation model, which makes it possible to analyze critical parameters separately for supply and distribution, as well as to model cases of their interdependence. Particular attention is paid to the separation of routes into supply and distribution, as well as to the use of different modes of transport. Conclusions: The obtained results confirm that a decisive factor in logistics decision-making is a balanced ratio between economic costs, risk level, and expected effect. The proposed approach enables a quantitative assessment of management decision options within supply and distribution logistics chains of manufacturing enterprises, thereby increasing the validity of choosing an optimal development strategy.", "keywords": [ "Supply chain", "Set (abstract data type)", "Order (exchange)", "Drone", "Supply chain management", "Integrated logistics support", "Production (economics)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://asu-pa.nure.ua/article/view/356671" }, { "id": "https://openalex.org/W7150995501", "doi": "10.30837/0135-1710.2026.188.191", "title": "MODELING AND ANALYSIS OF LOGISTICS SUPPLY AND DISTRIBUTION ROUTES FOR HIGH-TECH PRODUCTS CONSIDERING DIFFERENT MODES OF TRANSPORT", "year": 2026, "publication_date": "2026-03-12", "type": "article", "language": null, "authors": [ "Yuriy Polupan", "Olga Malyeyeva", "Maksym Kikot" ], "first_author": "Yuriy Polupan", "venue": "Management Information System and Devises", "publisher": null, "abstract": "The subject of the article is the processes of organizing and optimizing the logistics of supply and distribution of high-tech defense products under wartime conditions, in particular, the development of a network-based logistics structure taking into account reverse flows, risks, and limited resources. The purpose of the study is to develop a model of supply and distribution routes that optimizes delivery time and costs while simultaneously reducing the risks of cargo loss and increasing the transport utilization rate. Tasks to be solved: to form a generalized combined structure of the logistics chain; to identify and analyze the main parameters of supply and distribution routes across different modes of transport; to develop a computer experiment plan; to conduct simulation modeling of supply and distribution routes; and to develop practical recommendations for implementing logistics projects under wartime conditions. Research methods and tools: a systems approach, simulation modeling, graphical and comparative analysis, and the AnyLogic platform. Results obtained: A combined structure of the logistics chain for a military drone manufacturing enterprise with multiple route options is schematically presented. A model is proposed that formalizes the logistics chain as a network of nodes and arcs within a transport system and includes a set of input parameters describing vehicles, routes, and order characteristics. The essence of the computer experiment lies in reproducing the properties of infrastructure and logistics projects using a discrete-event simulation model, which makes it possible to analyze critical parameters separately for supply and distribution, as well as to model cases of their interdependence. Particular attention is paid to the separation of routes into supply and distribution, as well as to the use of different modes of transport. Conclusions: The obtained results confirm that a decisive factor in logistics decision-making is a balanced ratio between economic costs, risk level, and expected effect. The proposed approach enables a quantitative assessment of management decision options within supply and distribution logistics chains of manufacturing enterprises, thereby increasing the validity of choosing an optimal development strategy.", "keywords": [ "Supply chain", "Distribution (mathematics)", "Set (abstract data type)", "Order (exchange)", "Drone", "Production (economics)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://asu-pa.nure.ua/article/download/356671/342620" }, { "id": "https://openalex.org/W7213450599", "doi": "10.51483/ijaiml.6.10s.2026.1418-1431", "title": "AI-Based Dynamic Waste Collection Route Optimization for Smart Cities", "year": 2026, "publication_date": "2026-01-01", "type": "article", "language": null, "authors": [ "Sugali Viswanatha Naik", "Aravind Kumar Madam" ], "first_author": "Sugali Viswanatha Naik", "venue": "International Journal of Artificial Intelligence and Machine Learning", "publisher": null, "abstract": "The waste management of a Smart City must follow intelligent routing systems that multi-objective, unlike purely distance minimization, and incorporates urgency signal from IoT monitored bins, and UAV derived allocation of waste prioritization. This paper proposes the PHGA-ACO framework that focuses on dynamic waste collection route optimization. Subsequently, the proposed framework efficiently integrates the smart bin fill-level data (Paper 1). In addition, the work integrates the WPI-HES outputs (Paper 2). The model works with a Capacitated Vehicle Routing Problem (CVRP) on a real OpenStreetMap road network of Nashik Municipal Corporation, India, having 84 active collection nodes with total demand 24,498.9 kg. A systematic comparison of five baseline algorithms First Come First Serve (FCFS), Nearest Neighbor (NN), Dijkstra-greedy, Ant Colony Optimization (ACO), and Genetic Algorithm (GA) with PHGA-ACO under same operating conditions. S(m) is a composite metric that combines the normalized route distance and the Urgency Position Index (UPI) that measures the joint efficiency–priority-compliance performance of each method. PHGA-ACO obtains the best overall score (S=0.2556, the best among all policies) as it cuts down total collection distance by 35.6% compared to that of FCFS. Compared to ACO, it improves the priority servicing index by 13.4% at the expense of a mean distance increase of 68.7% compared to ACO. The improvement happens specifically due to the urgency penalty component, as seen from the ablation analysis. Sensitivity analysis indicates optimal urgency weight range λ ∈ [54.3, 162.9]. Statistical analysis of 30 independent runs with the Wilcoxon and Friedman test indicates that all inter-algorithm differences are significant (p < 0.001).", "keywords": [ "Data collection", "Work (physics)", "Product (mathematics)", "Key (lock)", "Smart city" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.svedbergopen.com/index.php/ijaiml/article/download/1956/1248" }, { "id": "https://openalex.org/W7204772297", "doi": "10.1016/j.engappai.2026.116068", "title": "Reinforcement learning-enhanced routing for multiple unmanned aerial vehicles in satellite-assisted geohazard monitoring with emergency prioritization", "year": 2026, "publication_date": "2026-08-31", "type": "article", "language": "en", "authors": [ "Wei Wang", "Siyi Bai", "Wei Zhang", "Chao Fang", "Hongzhang Fu", "Jianyu Xu" ], "first_author": "Wei Wang", "venue": "Engineering Applications of Artificial Intelligence", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Geohazard", "Routing (electronic design automation)", "Prioritization", "Heuristics", "Reinforcement learning", "Process (computing)", "Wireless sensor network", "Convergence (economics)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1016/j.engappai.2026.116068" }, { "id": "https://openalex.org/W4302081600", "doi": "10.48550/arxiv.1711.04936", "title": "A Two-Stage Approach for Routing Multiple Unmanned Aerial Vehicles with\\n Stochastic Fuel Consumption", "year": 2017, "publication_date": "2017-11-13", "type": "preprint", "language": "en", "authors": [ "Saravanan Venkatachalam", "Kaarthik Sundar", "Sivakumar Rathinam" ], "first_author": "Saravanan Venkatachalam", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "The past decade has seen a substantial increase in the use of small unmanned\\naerial vehicles (UAVs) in both civil and military applications. This article\\naddresses an important aspect of refueling in the context of routing multiple\\nsmall UAVs to complete a surveillance or data collection mission. Specifically,\\nthis article formulates a multiple-UAV routing problem with the refueling\\nconstraint of minimizing the overall fuel consumption for all of the vehicles\\nas a two-stage stochastic optimization problem with uncertainty associated with\\nthe fuel consumption of each vehicle. The two-stage model allows for the\\napplication of sample average approximation (SAA). Although the SAA solution\\nasymptotically converges to the optimal solution for the two-stage model, the\\nSAA run time can be prohibitive for medium- and large-scale test instances.\\nHence, we develop a tabu-search-based heuristic that exploits the model\\nstructure while considering the uncertainty in fuel consumption. Extensive\\ncomputational experiments corroborate the benefits of the two-stage model\\ncompared to a deterministic model and the effectiveness of the heuristic for\\nobtaining high-quality solutions.\\n", "keywords": [ "Fuel efficiency", "Vehicle routing problem", "Computer science", "Mathematical optimization", "Context (archaeology)", "Tabu search", "Routing (electronic design automation)", "Heuristic", "Constraint (computer-aided design)", "Exploit", "Operations research", "Engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/1711.04936" }, { "id": "https://openalex.org/W2908413415", "doi": "10.1109/iaeac.2018.8577825", "title": "Efficient path planning for unmanned aerial vehicle with refuelling stations", "year": 2018, "publication_date": "2018-10-01", "type": "conference-paper", "language": "en", "authors": [ "Mbyayingabo Jean Marie Vianney", "Longjiang Li", "Uwizeye Erneste", "Kagaju Clarisse", "Haoyang Liang" ], "first_author": "Mbyayingabo Jean Marie Vianney", "venue": null, "publisher": null, "abstract": "Unmanned Air vehicle (UAV) is a preferred option for the country of Rwanda to distribute blood supply to different remote hospitals in the country. However, the execution of such a persistent mission demands for an extended path planning with refuelling stations (RS) to extent flight time, which is proven a NP-hard problem. This paper proposes a novel UAV path planning (UPP) algorithm for the routing problem, which borrows the idea of the classic Greedy Perimeter Stateless Routing protocol (GPSR) to make local decisions of RS selection. Since most routing operations are made locally and do not depend on the knowledge of whole topology, the UPP algorithm can achieve a much less computation complexity than previous algorithms. The simulation results show that the proposed scheme also achieves a less end-to-end trip delay than existing methods, when the refuelling time becomes a substantial factor.", "keywords": [ "Stateless protocol", "Motion planning", "Computer science", "Routing (electronic design automation)", "Path (computing)", "Greedy algorithm", "Scheme (mathematics)", "Computer network", "Computation", "Selection (genetic algorithm)", "Distributed computing", "Routing protocol" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/iaeac.2018.8577825" }, { "id": "https://openalex.org/W7166870210", "doi": "10.13016/jgbp-fhli", "title": "RESILIENT AND PREDICTIVE MULTI-DEPOT ARC ROUTING FOR RECHARGEABLE AND CAPACITY-CONSTRAINED VEHICLES WITH UNCERTAIN FAILURES", "year": 2026, "publication_date": "2026-07-01", "type": "other", "language": "en", "authors": [ "Eashwar Sathyamurthy" ], "first_author": "Eashwar Sathyamurthy", "venue": "Digital Repository at the University of Maryland (University of Maryland College Park)", "publisher": "University of Maryland, College Park", "abstract": "The deployment of battery-operated Unmanned Aerial Vehicles (UAVs) has steadily expanded for the surveillance and inspection of critical infrastructure, driven by their flexibility, cost-effectiveness, and capability for repeated missions. One vital application is the continuous monitoring of hazardous winter road conditions, such as black ice formation, which poses severe risks to public safety. However, utilizing autonomous UAVs for large-scale, time-sensitive monitoring introduces substantial operational and logistical complexities. This dissertation formally models these challenges as the Multi-Depot Rural Postman Problem with Rechargeable and Reusable Vehicles (MD-RPP-RRV), wherein UAVs with finite battery capacities must be dispatched from multiple bases to inspect spatially distributed road segments selected based on expert-recommendations, undergo frequent recharging, and ensure timely data collection. Beyond the core deterministic routing problem, real-world operational conditions introduce critical layers of disruption and uncertainty. In practice, UAVs operate in harsh environments where prolonged exposure leads to stochastic in-mission vehicle failures. Such disruptions sever planned routes and leave critical road segments uninspected, severely compromising system reliability. To address this, this research develops robust reactive rescheduling strategies, specifically introducing centralized and peer-to-peer auction algorithms. By enabling the surviving UAVs to dynamically bid on and autonomously reallocate abandoned tasks, these auction mechanisms facilitate efficient, real-time schedule recovery and maintain high coverage levels despite unexpected vehicle losses. Furthermore, while traditional routing models rely on static inputs, real-world hazard formation is inherently dynamic and governed by significant meteorological uncertainty. Relying on static historical data often leads to inefficient inspections that prioritize safe roads while active hazards remain undetected. To effectively manage this spatio-temporal uncertainty, this dissertation proposes a comprehensive closed-loop predictive routing framework that tightly integrates machine learning with combinatorial optimization. A Temporal Graph Neural Network (TGNN) processes sequential weather data to estimate time-varying hazard probabilities for specific road segments. These updated probabilities subsequently drive a memory-augmented Simulated Annealing metaheuristic, which dynamically generates inspection routes to maximize expected hazard coverage while adhering to strict operational vehicle constraints. The primary contributions of this research advance the field of autonomous monitoring by bridging the critical gap between static routing models and dynamic operational uncertainties. First, it expands the theoretical boundaries of arc routing by formulating the MD-RPP-RRV to explicitly integrate multiple depots, finite battery capacities, and periodic recharging constraints, simultaneously providing scalable metaheuristics that overcome the computational limitations of exact solvers on large networks. Second, it contributes novel reactive rescheduling frameworks utilizing centralized and peer-to-peer auction mechanisms. Unlike traditional methods that require complete global rescheduling, these algorithms guarantee real-time mission recovery and establish theoretical performance bounds during stochastic mid-flight vehicle failures. Finally, it introduces a closed-loop predictive routing framework that integrates machine learning forecasts directly with combinatorial optimization. Unlike traditional routing frameworks restricted by static networks, this framework continuously predicts dynamic environmental risks and adapts vehicle routes in response to changing weather forecasts. By developing and integrating memory-based routing techniques to bypass redundant calculations, the framework ensures computational tractability while managing rapidly evolving and uncertain environments.", "keywords": [ "Software deployment", "Schedule", "Routing (electronic design automation)", "Hazard", "Taxis", "Truck", "Downtime" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.13016/jgbp-fhli" }, { "id": "https://openalex.org/W4383988546", "doi": "10.48550/arxiv.2307.04328", "title": "Where to Drop Sensors from Aerial Robots to Monitor a Surface-Level Phenomenon?", "year": 2023, "publication_date": "2023-07-10", "type": "preprint", "language": "en", "authors": [ "Chak Lam Shek", "Guangyao Shi", "Asghar, Ahmad Bilal", "Pratap Tokekar" ], "first_author": "Chak Lam Shek", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "We consider the problem of routing a team of energy-constrained Unmanned Aerial Vehicles (UAVs) to drop unmovable sensors for monitoring a task area in the presence of stochastic wind disturbances. In prior work on mobile sensor routing problems, sensors and their carrier are one integrated platform, and sensors are assumed to be able to take measurements at exactly desired locations. By contrast, airdropping the sensors onto the ground can introduce stochasticity in the landing locations of the sensors. We focus on addressing this stochasticity in sensor locations from the path-planning perspective. Specifically, we formulate the problem (Multi-UAV Sensor Drop) as a variant of the Submodular Team Orienteering Problem with one additional constraint on the number of sensors on each UAV. The objective is to maximize the Mutual Information between the phenomenon at Points of Interest (PoIs) and the measurements that sensors will take at stochastic locations. We show that such an objective is computationally expensive to evaluate. To tackle this challenge, we propose a surrogate objective with a closed-form expression based on the expected mean and expected covariance of the Gaussian Process. We propose a heuristic algorithm to solve the optimization problem with the surrogate objective. The formulation and the algorithms are validated through extensive simulations.", "keywords": [ "Computer science", "Heuristic", "Submodular set function", "Mathematical optimization", "Gaussian process", "Real-time computing", "Focus (optics)", "Gaussian", "Artificial intelligence", "Mathematics" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2307.04328" }, { "id": "https://openalex.org/W4413166678", "doi": "10.25140/2411-5363-2025-2(40)-432-444", "title": "Selection of solar panels for autonomous power systems of unmanned aerial vehicles", "year": 2025, "publication_date": "2025-08-12", "type": "article", "language": "en", "authors": [ "Dmytrii Shokodko", "Maksym Khomenko" ], "first_author": "Dmytrii Shokodko", "venue": "TECHNICAL SCIENCES AND TECHNOLOGIES", "publisher": null, "abstract": "Growing military and civilian reliance on multirotor unmanned aerial vehicles (UAVs) accentuates the need for resilient, field-deployable power supplies capable of continuous operation whenever conventional grids are inaccessible or hazardous. The paper addresses this challenge by proposing the comprehensive methodology for choosing photovoltaic (PV) converters and complementary storage technologies that can sustain round-the-clock wireless charging of UAV batteries under Northern-Ukrainian insolation conditions. The problem is formulated as joint optimisation of the daily number of battery charging cycles, overall-energy demand and PV-array configuration while accounting for climatic variability and flight-mission logistics.The research objective is to substantiate the autonomous solar power plant sized to support three DJI Mavic 3 class UAVs performing uninterrupted aerial reconnaissance. Methods include energy-balance modelling, scenario scheduling of flight/charge cycles, comparative efficiency analysis of mono-, poly- and thin-film PV modules, and evaluation of two battery chemistries—LiFePO₄ and AGM/GEL—using manufacturer data and recent literature.The key results show that, for Chernihiv’s mean global tilted irradiation of 3.61 kWh m⁻² day⁻¹, a 3.5 kWh daily yield suffices to cover 35 full 100 W charges. LiFePO₄ storage (round-trip η ≈ 95 %) cuts required array area by 5–15 % versus AGM/GEL (η ≈ 85 %), enabling a lighter, cheaper and more mobile installation. Among thirteen commercially available panels analysed, high-efficiency monocrystalline modules (η > 20 %) teamed with LiFePO₄ batteries minimise total mass (≈ 55 kg) and capital cost (< 400 USD) while meeting mission autonomy. The proposed selection algorithm may be adapted to other regions and fleet sizes, and the findings facilitate rapid deployment of reliable solar-powered UAV charging outposts for de-fence, rescue and environmental monitoring tasks.", "keywords": [ "Photovoltaic system", "Automotive engineering", "Computer science", "Charge controller", "Battery (electricity)", "Converters", "Multirotor", "Energy storage", "Environmental science", "Power (physics)", "Electrical engineering", "Engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://tst.stu.cn.ua/article/download/337209/325762" }, { "id": "https://openalex.org/W3157855478", "doi": null, "title": "Routing and Control of Unmanned Aerial Vehicles for Performing Contact-Based Tasks", "year": 2021, "publication_date": "2021-05-05", "type": "dissertation", "language": "en", "authors": [ "Robert Blake Anderson" ], "first_author": "Robert Blake Anderson", "venue": "VTechWorks (Virginia Tech)", "publisher": "Virginia Tech", "abstract": "In this dissertation, two main topics are explored, the vehicle routing problem (VRP) and model reference adaptive control (MRAC) for unknown nonlinear systems. The VRP and its extension, the split delivery VRP (SVRP), are analyzed to determine the effects of using two different objective functions, the total cost objective, and the last delivery objective. A worst-case analysis suggests that using the SVRP can improve total costs by as much as a factor of 2 and the last delivery by a factor that scales with the number of vehicles over the classical VRP. To test the theoretical worst-cases against the solutions of benchmark datasets, a heuristic is developed based on embedding a random variable neighborhood search within an iterative local search heuristic. Results suggest that the split deliveries do in fact improve total cost and last delivery times over the classical formulation. The SVRP has been developed classically for use with vehicles such as trucks which have large payload capacities and typically long ranges for deliveries, but are limited to traversing on roads. Unmanned aerial vehicles (UAVs) are useful for their high maneuverability, but suffer from limited capacity for payloads and short ranges. The classical SVRP formulation is extended to one more suitable for UAVs by accounting for limited range, limited payloads, and the ability to swap batteries at known locations. Instead of Euclidean distances, path plans which are adjusted for a known, constant wind underlie the cost matrix of the optimization problem. The effects of payload on the vehicle's range are developed using propeller momentum theory, and simulations verify that the proposed approach could be used in a realistic scenario. Two novel MRAC laws are then developed. The first, MRAC laws for prescribed performance, exploits barrier Lyapunov functions and a 2-Layer approach to guarantee user-defined performance. This control law allows unknown nonlinear systems to verify a user-defined rate of convergence of the tracking error while verifying apriori control and tracking error constraints. Numerical simulations are performed on the roll dynamics of a delta-wing aircraft. The second novel MRAC law is MRAC for switched dynamical systems which is proven in two different mathematical frameworks. Applying the Caratheodory framework, it is proven that if the switching signal has an arbitrarily small, but non-zero, dwell-time, then solutions of both the trajectory tracking error's and the adaptive gains' dynamics exist, are unique, and are defined almost everywhere, and the trajectory tracking error converges asymptotically to zero. Employing the Filippov framework, it is proven that if the switching signal is Lebesgue integrable and has countably many points of discontinuity, then maximal solutions of both the trajectory tracking error and the adaptive gains dynamics exist and are defined almost everywhere, and the trajectory tracking error converges to zero asymptotically. The proposed MRAC law is experimentally verified in the case where a UAV with tilting propellers is tasked with mounting an unknown camera onto a wall. The previous results are then combined into a novel application in construction. A method for using a UAV to measure autonomously the moisture of an exterior precast concrete envelope is developed which can provide data feedback through contact-based measurements to improve safety and real-time data acquisition through the integration with the Building Information Model (BIM). To plan the path of the vehicle, the path planning and SVRP for UAV approaches developed in previous chapters are utilized. To enable the UAS to contact surfaces, a switched MRAC law is employed to control the vehicle throughout and guarantee successful measurements. A full physics-based simulation environment is developed, and the proposed framework is used to simulate taking multiple measurements.", "keywords": [ "Computer science", "Routing (electronic design automation)", "Control (management)", "Aeronautics", "Engineering", "Real-time computing", "Embedded system", "Artificial intelligence" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://hdl.handle.net/10919/103201" }, { "id": "https://openalex.org/W3178578225", "doi": null, "title": "Heurıstıc Approaches for The Multı-Objectıve Routıng Problem for A Fleet of Unmanned Aerıal Vehıcles", "year": 2019, "publication_date": "2019-12-01", "type": "dissertation", "language": "tr", "authors": [ "Büşra BİŞKİN" ], "first_author": "Büşra BİŞKİN", "venue": "Hacettepe University Institutional Repository (hacettepe.edu.tr)", "publisher": "Hacettepe University", "abstract": "Nowadays, Unmanned Aerial Vehicles (UAVs) are extensively employed for various missions with different purposes. In every mission, different goals and problem structures are considered. In this thesis, we study the routing problem of a fleet of identical UAVs under multiple objectives. UA Vs in the fleet, which have limited flight durations, take off from a base, visit a number of targets in a two-dimensional mission area, and return to the base. We assume that the targets have different priorities, and the UAVs try to visit as many targets as possible to collect maximum reward within flight limits. We consider the following three objectives: minimizing the total distance traveled by the fleet, maximizing the total reward collected from the targets, and minimizing the total radar threat. We address two versions of the problem: routing in a radar-free terrain (with distance and reward as objectives) and routing in a radar-monitored terrain (with all three objectives). We aim to find efficient routes for each UAV in the fleet and the trajectory between pairs of targets in each route. \\nWe employ two solution approaches for each version of our problem. First, we model the problem as a Multi-Objective Team Orienteering Problem (MOTOP) and find exact solutions. In our second approach, we utilize an Evolutionary Algorithm, EA-fUAV (Evolutionary Algorithm for routing a fleet of UAVs), to approximate efficient solutions in reasonable time. We test both approaches on three different problem cases. The results show that EA-fUAV approximates the efficient set well in reasonable time.", "keywords": [ "Humanities", "Mathematics", "Combinatorics", "Art" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://hdl.handle.net/11655/22747" }, { "id": "https://openalex.org/W7133245078", "doi": "10.48550/arxiv.2602.23667", "title": "Blockchain-Enabled Routing for Zero-Trust Low-Altitude Intelligent Networks", "year": 2026, "publication_date": "2026-02-27", "type": "preprint", "language": null, "authors": [ "Ziye Jia", "Sijie He", "Ligang Yuan", "Fuhui Zhou", "Qihui Wu", "Zhu Han", "Dusit Niyato" ], "first_author": "Ziye Jia", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Due to the scalability and portability, low-altitude intelligent networks (LAINs) are essential in various fields such as surveillance and disaster rescue. However, in LAINs, unmanned aerial vehicles (UAVs) are characterized by the distributed topology and high mobility, thus vulnerable to security threats, which may degrade routing performances for data transmissions. Hence, how to ensure the routing stability and security of LAINs is challenging. In this paper, we focus on the routing with multiple UAV clusters in LAINs. To minimize the damage caused by potential threats, we present the zero-trust architecture with the software-defined perimeter and blockchain techniques to manage the identify and mobility of UAVs. Besides, we formulate the routing problem to optimize the end-to-end (E2E) delay and transmission success ratio (TSR) simultaneously, which is an integer nonlinear programming problem and intractable to solve. Therefore, we reformulate the problem into a decentralized partially observable Markov decision process. We design the multi-agent double deep Q-network-based routing algorithms to solve the problem, empowered by the soft-hierarchical experience replay buffer and prioritized experience replay mechanisms. Finally, extensive simulations are conducted and the numerical results demonstrate that the proposed framework reduces the average E2E delay by 59\\% and improves the TSR by 29\\% on average compared to benchmarks, while simultaneously enabling faster and more robust identification of low-trust UAVs.", "keywords": [ "Routing (electronic design automation)", "Scalability", "Static routing", "Stability (learning theory)", "Link-state routing protocol", "Dynamic Source Routing", "Policy-based routing", "Markov decision process", "Hierarchical routing" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.48550/arxiv.2602.23667" }, { "id": "https://openalex.org/W7207543424", "doi": "10.1016/j.tre.2026.105178", "title": "A tractable model and heuristic for vessel routing with multiple unmanned aerial vehicles", "year": 2026, "publication_date": "2026-09-03", "type": "article", "language": "en", "authors": [ "Bo Ren", "Yantong Li", "Hairui Sun", "Danyu Bai", "Kang Chen" ], "first_author": "Bo Ren", "venue": "Transportation Research Part E Logistics and Transportation Review", "publisher": "Elsevier BV", "abstract": null, "keywords": [ "Simulated annealing", "Spare part", "Heuristic", "Travelling salesman problem", "Routing (electronic design automation)", "Key (lock)", "Baseline (sea)", "Heuristics" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.sciencedirect.com/science/article/pii/S1366554526005168/pdf" }, { "id": "https://openalex.org/W4414386704", "doi": "10.47941/ijscl.3189", "title": "Development and Application of Algorithms in Logistics", "year": 2025, "publication_date": "2025-09-22", "type": "article", "language": "en", "authors": [ "Valentyn Marcenko" ], "first_author": "Valentyn Marcenko", "venue": "International Journal of Supply Chain and Logistics", "publisher": null, "abstract": "Purpose: To examine how algorithmic thinking reshapes freight operations beyond dispatcher intuition by synthesizing evidence from twelve peer-reviewed empirical studies (2019–2025) alongside design logics from the OnLogix and Excel Logistics platforms. The focus is on routing, inventory, and risk workflows where efficiency, compliance, and organizational trust intersect. Methodology: A PRISMA-inspired, managerially focused search protocol identified studies on time-dependent vehicle routing, truck–drone collaboration, adaptive inventory control, and risk-aware accounts-receivable scoring. Each paper was coded against nine performance dimensions to enable structured cross-study comparison while avoiding premature meta-analysis given divergent samples and settings. Findings: Hybrid metaheuristics consistently outperform classic tabu or GRASP once stochastic travel times and regulatory constraints are modeled. Machine-learning layers raise prediction accuracy but introduce opacity. Interpreted through a mid-size 3PL lens, three actionable themes emerge: (1) embed compliance and financial-risk logic natively in routing engines; (2) prioritize deployment velocity over marginal optimality where data-science capacity is limited; and (3) treat explainability as essential for shared dashboards used by drivers, dispatchers, and auditors. Unique Contribution to Theory, Policy and Practice: The study reframes “algorithmic logistics” as a socio-technical system, integrating efficiency with governance, explainability, and data stewardship (theory). It signals to policymakers that auditability and transparency should complement classic efficiency metrics in regulatory guidance (policy). It offers practitioners an adoption playbook: build compliance-aware routing from the outset, value speed-to-production, and require interpretable ML in operator-facing tools (practice). It also maps a mixed-methods research agenda coupling large-scale simulation with ethnographic observation and points to future work on reinforcement-learning price engines as carbon markets tighten around fleets.", "keywords": [ "GRASP", "Workflow", "Transparency (behavior)", "Tabu search", "Supply chain", "Heuristics", "Metaheuristic", "Outsourcing", "Flexibility (engineering)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://carijournals.org/journals/IJSCL/article/download/3189/3799" }, { "id": "https://openalex.org/W7133267397", "doi": null, "title": "Blockchain-Enabled Routing for Zero-Trust Low-Altitude Intelligent Networks", "year": 2026, "publication_date": "2026-02-27", "type": "preprint", "language": null, "authors": [ "Ziye Jia", "Sijie He", "Ligang Yuan", "Fuhui Zhou", "Qihui Wu", "Zhu Han", "Dusit Niyato" ], "first_author": "Ziye Jia", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Due to the scalability and portability, low-altitude intelligent networks (LAINs) are essential in various fields such as surveillance and disaster rescue. However, in LAINs, unmanned aerial vehicles (UAVs) are characterized by the distributed topology and high mobility, thus vulnerable to security threats, which may degrade routing performances for data transmissions. Hence, how to ensure the routing stability and security of LAINs is challenging. In this paper, we focus on the routing with multiple UAV clusters in LAINs. To minimize the damage caused by potential threats, we present the zero-trust architecture with the software-defined perimeter and blockchain techniques to manage the identify and mobility of UAVs. Besides, we formulate the routing problem to optimize the end-to-end (E2E) delay and transmission success ratio (TSR) simultaneously, which is an integer nonlinear programming problem and intractable to solve. Therefore, we reformulate the problem into a decentralized partially observable Markov decision process. We design the multi-agent double deep Q-network-based routing algorithms to solve the problem, empowered by the soft-hierarchical experience replay buffer and prioritized experience replay mechanisms. Finally, extensive simulations are conducted and the numerical results demonstrate that the proposed framework reduces the average E2E delay by 59\\% and improves the TSR by 29\\% on average compared to benchmarks, while simultaneously enabling faster and more robust identification of low-trust UAVs.", "keywords": [ "Routing (electronic design automation)", "Scalability", "Static routing", "Stability (learning theory)", "Link-state routing protocol", "Dynamic Source Routing", "Policy-based routing", "Markov decision process", "Hierarchical routing" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2602.23667" }, { "id": "https://openalex.org/W3119707011", "doi": "10.30932/1992-3252-2020-18-84-96", "title": "Aspects of Equipping Transport Facilities with Systems for Controlling Routes of Unmanned Aerial Vehicles", "year": 2021, "publication_date": "2021-01-05", "type": "article", "language": "en", "authors": [ "Alexey V. Shvetsov" ], "first_author": "Alexey V. Shvetsov", "venue": "World of Transport and Transportation", "publisher": null, "abstract": "Currently, the problem of ensuring safety of flights of unmanned aerial vehicles (UAVs) over the territory of transport infrastructure facilities (TIF), primarily airports, remains topical. In one of the previous works, the author together with the co-author proposed a method for increasing safety of movement of unmanned aerial vehicles and the system for controlling the routes of unmanned aerial vehicles (hereinafter – route control system, RCS) that implements it, which makes it possible to improve safety and security of UAV traffic at transport infrastructure facilities by limiting UAV traffic area strictly to a dedicated air corridor (DAC). The development of this system creates the prerequisites for removing the existing restrictions on the use of unmanned aerial vehicles at transport infrastructure facilities. For practical implementation of the proposed system, it is relevant to develop a method of placing RCS at transport infrastructure facilities. This condition can be justified by the fact that RCS, as a rule, will be located under conditions of dense infrastructural development, including dangerous technical elements of technical equipment, a collision of a UAV with which can lead to an emergency situation (ES); besides, the movement of air/ground vehicles will be carried out in the immediate vicinity of RCS along transport routes/corridors, and employees, passengers and visitors of TIF will move along the pedestrian paths. The objective of this study – to develop a methodology for placing systems controlling routes of unmanned aerial vehicles at transport infrastructure facilities. The study conducted with well-known scientific methods, including the basic routing problem posed by Dantzig and Ramser, modelling, analysis, and synthesis, made it possible to develop a method for placing RCS for unmanned aerial vehicles at transport infrastructure facilities. The practical application of the proposed methodology makes it possible to build routes for movement of UAV at TIF, to form a network of dedicated air corridors for UAV at TIF operated in relationship with the system, determine the optimal location of the RCS elements at TIF.", "keywords": [ "Limiting", "Transport engineering", "Pedestrian", "Drone", "Control (management)", "Computer science", "Air transport", "Engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://mirtr.elpub.ru/jour/article/download/1924/2372" }, { "id": "https://openalex.org/W7167812640", "doi": "10.48550/arxiv.2607.07350", "title": "Towards Reliable Aerial Ground Vehicle Collaboration: An Integrated Planning and Autonomy Framework for Field Deployment", "year": 2026, "publication_date": "2026-07-07", "type": "preprint", "language": null, "authors": [ "Md Safwan Mondal", "Russo, Luca", "James D. Humann", "James M. Dotterweich", "Pranav Bhounsule" ], "first_author": "Md Safwan Mondal", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Limited flight endurance significantly restricts the operational range of unmanned aerial vehicles (UAVs) in long duration missions such as surveillance and inspection, where multiple spatially distributed Areas of Interest (AOIs) must be visited. These tasks require efficient routing determining the sequence of visits which directly impacts mission time, energy consumption, and overall feasibility. Pairing UAVs with unmanned ground vehicles (UGVs) for mobile recharging offers a promising solution, but introduces a tightly coupled cooperative routing problem involving UAV route planning, UGV road constrained movement, energy management, and rendezvous scheduling under uncertainty. In this work, we present an integrated planning and autonomy framework for reliable field deployment. We formulate the problem as an energy constrained cooperative routing task and solve it using a Deep Reinforcement Learning (DRL) based planner that jointly optimizes the UAV visitation sequence and rendezvous locations with the UGV, outperforming baseline heuristics in minimizing total mission time. To bridge the gap between planning and execution, we introduce a standardized two layer YAML based mission API that captures environment states and structures lightweight, synchronized action sequences. This framework is supported by a complete autonomy stack using PX4/MAVSDK for UAV control and ROS 2/Nav2 for UGV navigation. Furthermore, we propose a lightweight Rendezvous Aware Replanner (RARP) that operates online to handle environmental uncertainties, reducing energy margin violations from 83.33% to 20.00%. The full system is validated through outdoor field experiments, demonstrating robust cooperative navigation and adaptability in dynamic tasks, including a search and rescue scenario with vision language model (VLM) based hazard detection", "keywords": [ "Rendezvous", "Motion planning", "Software deployment", "Scheduling (production processes)", "Adaptability", "Mobile robot", "Routing (electronic design automation)", "Search and rescue", "Drone" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2607.07350" }, { "id": "https://openalex.org/W4399801587", "doi": "10.1109/icuas60882.2024.10557087", "title": "Optimizing Routes of Heterogenous Unmanned Systems using Supervised Learning in a Multi-Agent Framework: A computational study", "year": 2024, "publication_date": "2024-06-04", "type": "conference-paper", "language": "en", "authors": [ "R. Subramanian", "Md Safwan Mondal", "James D. Humann", "James M. Dotterweich", "Jean-Paul Reddinger", "Marshal Childers", "Pranav A. Bhounsule" ], "first_author": "R. Subramanian", "venue": null, "publisher": null, "abstract": "Fast-paced but power-hungry Unmanned Aerial Vehicles (UAV) may collaborate with slow-paced Unmanned Ground Vehicles (UGV) acting as mobile recharging depots to perform large-scale and long-duration tasks such as in disaster relief management. It is important to be able to create high-quality vehicle routes in a short span of time to enable in-field, real-time deployment. A two-level optimization enables a tractable approach to solving such NP-hard combinatorial optimization problems, and it consists of an outer-level UGV route optimization to compute recharge locations and an inner-level UAV optimization to compute a sequence of nodes and recharging nodes to be visited. We consider various approaches to solve this two-level optimization. Method 1: Genetic algorithm for global search of UGV routes and a constraint programming solver for the UAV routes. Method 2: A-Teams framework that uses a combination of Genetic Algorithm (GA) for global search and Nelder-Mead for local search for UGV routes and constraint programming for inner routes, Method 3: Our proposed A-Teams by adding a supervised learning prediction step to veto out suboptimal evolutions from GA to the UGV route and constraint programming for the inner level, Method 4: Our proposed A-Teams for outer level but with a mixed integer programming solver for the inner-level. Our results on test cases show that the proposed Method 3 produces an optimal solution 30 % faster than Method 2, 79 % faster than Method 1, and 83% faster than Method 4 while being within 2 % of the solution optimality across these methods.", "keywords": [ "Computer science", "Artificial intelligence", "Multi-agent system", "Machine learning", "Distributed computing" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dx.doi.org/10.1109/icuas60882.2024.10557087" }, { "id": "https://openalex.org/W7162904109", "doi": null, "title": "Модель та метод збалансованого розподілу завдань та оптимізації маршрутів для груп безпілотних літальних апаратів", "year": 2026, "publication_date": "2026-05-28", "type": "article", "language": "en", "authors": [ "Микола Ніколаєв", "Михайло Новотарський" ], "first_author": "Микола Ніколаєв", "venue": "The Scientific Issues of Ternopil Volodymyr Hnatiuk National Pedagogical University Series pedagogy", "publisher": "Ternopil Volodymyr Hnatiuk National Pedagogical University", "abstract": "This study addresses centralized mission planning for heterogeneous UAV fleets that perform a single sortie from a common depot to service spatially distributed tasks. The study develops a deterministic planning framework for asymmetric and weakly non-metric travel costs. The framework supports early feasibility control, workload balance, and route-quality improvement. The research materials consist of benchmark mission instances defined by task locations, dwell times, endurance limits, and precomputed directed cost matrices for each UAV. The methods include feasibility masking of task–vehicle assignments, workload balancing with an order-independent surrogate load, and weighted power partitioning with deterministic offset updates. They also include single-vehicle route optimization and exchange-based refinement under a composite objective of total fleet cost and makespan. The results show that the first stage provides a strong trade-off between balance and efficiency while preserving spatial coherence. The second stage yields consistent additional reductions in makespan and in the composite objective with modest computational overhead. Across the benchmarks, the framework maintains near-minimal total cost while markedly reducing the longest route relative to cost-greedy assignment. The study shows that localized route-level refinement improves solution quality without repeated low-level replanning. Early feasibility control is incorporated at the allocation stage. The scientific novelty consists in establishing that cost-matrix-aware workload allocation reduces route-cost imbalance before route sequencing under UAV-specific directed costs. The study also shows that route-level correction under the same cost representation further reduces makespan and the composite mission objective.", "keywords": [ "Workload", "Job shop scheduling", "Offset (computer science)", "Benchmark (surveying)", "Dwell time", "Scheduling (production processes)", "Minification", "Representation (politics)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://itvisnyk.kpi.ua/article/view/355856" }, { "id": "https://openalex.org/W4405418516", "doi": "10.21821/2309-5180-2024-16-5-805-816", "title": "Development of automation and control software for charging systems of multirotor small unmanned aircraft systems for tasks of cargo seaports", "year": 2024, "publication_date": "2024-12-05", "type": "article", "language": "en", "authors": [ "Eugene A. Voznesensky", "N. N. Maiorov", "Andrey N. Terekhov" ], "first_author": "Eugene A. Voznesensky", "venue": "Vestnik Gosudarstvennogo universiteta morskogo i rechnogo flota imeni admirala S O Makarova", "publisher": null, "abstract": "The paper considers the situation of using an unmanned aircraft system to perform autonomous flights between cargo port areas, for example, to collect data for building digital spatial models or to transfer a certain type of cargo. It is noted that due to the considerable size and remoteness of cargo terminals from each other it is necessary to place a network of automated charging stations. The efficiency of autonomous flights is substantiated and a new model of flight assignment of an unmanned aircraft system is given for solving the routing problem, taking into account the location of charging station sites, for example, in cargo port areas. To solve the set tasks, a new hardware system for charging unmanned aerial systems considering a receiver module with a coil on board and a ground landing system for charging is considered, circuit diagrams are given and constraints for performing the battery charging process are specified. The fragments of programme codes of automation of the graphical interface of the webserver with display of all parameters and calculation of the coefficient of efficiency at charging are resulted. The choice of Arduino IDE software environment is justified. The possibility of using the developed automated station for battery charging for a large class of drones of mulitirotor type is noted. On the basis of a series of experiments in the flight research field of the laboratory of unmanned aircraft systems of the SUAI, it is established that the charging station together with the receiving module managed to achieve the efficiency of charge transfer at the level of 73.7 %. The obtained values of efficiency allow to draw a conclusion about the possibility of practical use for real transport tasks and the possibility of network deployment, in particular, on the dedicated specialised areas of the cargo port. The developed automation subprogramme for the deployment of a network of charging stations (e. g. in different areas of the port) allows to create an information system for monitoring the operation of charging stations, which significantly increases the controllability and reliability of the system.", "keywords": [ "Multirotor", "Automation", "Software", "Aeronautics", "Systems engineering", "Engineering", "Control (management)", "Computer science", "Control system", "Automotive engineering", "Aerospace engineering", "Operating system" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://journal.gumrf.ru/jour/article/download/513/415" }, { "id": "https://openalex.org/W7162691874", "doi": "10.20535/2786-8729.8.2026.355856", "title": "Model and method of balanced partitioning and route optimization for multi-unmanned aerial vehicle task planning", "year": 2026, "publication_date": "2026-05-28", "type": "article", "language": null, "authors": [ "Mykola Nikolaiev", "Mykhailo Novotarskyi" ], "first_author": "Mykola Nikolaiev", "venue": "Information, computing and intelligent systems", "publisher": null, "abstract": "This study addresses centralized mission planning for heterogeneous UAV fleets that perform a single sortie from a common depot to service spatially distributed tasks. The study develops a deterministic planning framework for asymmetric and weakly non-metric travel costs. The framework supports early feasibility control, workload balance, and route-quality improvement. The research materials consist of benchmark mission instances defined by task locations, dwell times, endurance limits, and precomputed directed cost matrices for each UAV. The methods include feasibility masking of task–vehicle assignments, workload balancing with an order-independent surrogate load, and weighted power partitioning with deterministic offset updates. They also include single-vehicle route optimization and exchange-based refinement under a composite objective of total fleet cost and makespan. The results show that the first stage provides a strong trade-off between balance and efficiency while preserving spatial coherence. The second stage yields consistent additional reductions in makespan and in the composite objective with modest computational overhead. Across the benchmarks, the framework maintains near-minimal total cost while markedly reducing the longest route relative to cost-greedy assignment. The study shows that localized route-level refinement improves solution quality without repeated low-level replanning. Early feasibility control is incorporated at the allocation stage. The scientific novelty consists in establishing that cost-matrix-aware workload allocation reduces route-cost imbalance before route sequencing under UAV-specific directed costs. The study also shows that route-level correction under the same cost representation further reduces makespan and the composite mission objective.", "keywords": [ "Workload", "Job shop scheduling", "Benchmark (surveying)", "Offset (computer science)", "Task (project management)", "Minification", "Flexibility (engineering)", "Scheduling (production processes)", "Representation (politics)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://itvisnyk.kpi.ua/article/download/355856/347873" }, { "id": "https://openalex.org/W2905698618", "doi": "10.17587/mau/17.206-210", "title": "Маршрутизация полета легкого беспилотного летательного аппарата в поле постоянного ветра с учетом ограничения на продолжительность полета", "year": 2016, "publication_date": "2016-01-01", "type": "article", "language": "ru", "authors": [ "Dmitriy V. Moiseev", "В. М. Чинь" ], "first_author": "Dmitriy V. Moiseev", "venue": "Mekhatronika Avtomatizatsiya Upravlenie", "publisher": null, "abstract": "Recently, some articles have appeared which discuss the problem of preflight routing for the unmanned aerial vehicles (UAV). The importance of the problem is determined by the fact that its solving is necessary for flight planning and modeling of UAV with a view of an effective assessment of its mission. The above mentioned papers view routing as a sequence of flights over the setpoints with certain positioning data. The notion of route optimality is associated with, on the one hand, increasing amount of the setpoints included into the route and, on the other hand, with a decrease of the period of time for an en-route flight. In this case, consideration of the problem of routing with a constraint for the en-route flight time is of practical relevance. Thus, it is the amount of the setpoints potentially set by the route that must serve as a maximal criterion. It is quite clear that such statement may result in a situation, when some selected setpoints are out of the optimal route. The paper describes the methods of finding a light UAV circle trip which links the maximum of the equivalent setpoints in a constant wind field taking into account the time constraint. The selected route is the fastest of all the routes linking the setpoints. The methods envisage consecutive solving of the purposely set central and sub-related routing problems. Mathematically, they are the problems of linear Boolean programming. Thus, a flight route can be divided in two stages. At the first stage, it is necessary to define the maximum of the setpoints linked by the routes with the flight time, which does not exceed the one, which is allowed. At the second stage, one should define the fastest of the first stage routes. An example was introduced explaining the details and showing the performance capacity of the present flight routing procedure. The results can be used at the planning stage for modeling of UAV application with a view to improve assessment of its mission effectiveness, as well as for designing of special algorithms and software for the UAV controllers' working area.", "keywords": [ "Routing (electronic design automation)", "Computer science", "Set (abstract data type)", "Flight planning", "Constraint (computer-aided design)", "Sequence (biology)", "Operations research", "Mathematical optimization", "Real-time computing", "Engineering", "Mathematics", "Computer network" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://mech.novtex.ru/jour/article/download/272/113" }, { "id": "https://openalex.org/W4402263404", "doi": "10.23919/acc60939.2024.10644620", "title": "Where to Drop Sensors from Aerial Robots to Monitor a Surface-Level Phenomenon?", "year": 2024, "publication_date": "2024-07-10", "type": "conference-paper", "language": "en", "authors": [ "Chak Lam Shek", "Guangyao Shi", "Ahmad Asghar", "Pratap Tokekar" ], "first_author": "Chak Lam Shek", "venue": null, "publisher": null, "abstract": "We consider the problem of routing a team of energy-constrained Unmanned Aerial Vehicles (UAVs) to deploy sensors for monitoring a surface-level phenomenon in the presence of stochastic wind disturbances. Most of the environmental monitoring work considers the case where the sensors and their carrier are on one platform, and the sensing location can be controlled by controlling the carrier vehicle's position. In contrast, airdropping the sensors onto the ground can intro-duce stochasticity in the sensors' landing (i.e., measurement) locations. We focus on carefully choosing where to drop the sensors so that despite the measurement locations' stochasticity, we can still effectively monitor the surface-level phenomenon. Specifically, we introduce this problem where a team of UAVs must drop sensors within an allotted time budget. The objective is to maximize the mutual information between the phenomenon at Points of Interest and the measurements obtained at the random sensing locations. This is a variant of the Submodular Team Orienteering Problem with an additional constraint on the number of sensors each UAV can carry. We show that the stochasticity in the measurement location makes objective computationally expensive to compute. We propose a surrogate objective with a closed-form expression and an algorithm for the routing problem. We validate the proposed approach through extensive simulations using synthetic and a real-world dataset of Chlorophyll density from a Pacific Ocean sub-region.", "keywords": [ "Robot", "Drop (telecommunication)", "Computer science", "Phenomenon", "Environmental science", "Artificial intelligence", "Physics", "Telecommunications" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://dx.doi.org/10.23919/acc60939.2024.10644620" }, { "id": "https://openalex.org/W2285199480", "doi": "10.14288/1.0071295", "title": "Stability of switched systems with switching delay : application to remote operation of aircraft under distributed control", "year": 2010, "publication_date": "2010-01-01", "type": "dissertation", "language": "en", "authors": [ "Nikolai Matni" ], "first_author": "Nikolai Matni", "venue": "cIRcle (University of British Columbia)", "publisher": "University of British Columbia", "abstract": "Unmanned aerial vehicles are becoming more and more useful tools for not only the military, but law enforcement, search and rescue and scientific data collection. With the advent of inexpensive and reliable wireless communication, remote operators are now able to control fleets of UAVs cooperating towards the accomplishment of their tasks. As the complexity and size of these fleets increase, distributed control methods are needed -- large fleet sizes will lead to intractable centralized problems. Furthermore, UAVs, like most aircraft, are inherently hybrid systems, combining both discrete and continuous dynamics. This thesis attempts to combine hybrid and distributed control theories in a way useful for the operation of UAVs, while taking communication delays inherent to a remote operator into account. Specifically, we consider the stability of block upper-triangular switched linear systems with switching delay, when switching between stable modes. We show that the problem of proving globally uniformly asymptotic stability (GUAS) of a block upper-triangular switched linear system can be reduced to proving GUAS for each of its block diagonal subsystems. This allows for a scalable LMI-based computational test for GUAS under arbitrary switching whose complexity depends linearly on the number of block diagonal elements of the system. In cases for which the system is not GUAS under arbitrary switching, we partition the state space into regions in which switching will preserve GUAS despite a delay between the state measurements and switching time. This is accomplished by adding a delay buffer to standard Piecewise Lyapunov based partitions. Additionally, we show that the effect of the delay buffer on the standard Piecewise Lyapunov based partitions asymptotically approaches zero. Although we tailor these results to block upper-triangular switched linear systems, they are applicable to any switched linear system with switching delay. These results are then extended to nonlinear switched systems. We apply our results to the control of a formation of vehicles under supervisory discrete control, and to switched systems under remote control. We then finish by addressing the issue of interface design for continuous systems under shared control, motivated by applications to pilot-automation interactions.", "keywords": [ "Stability (learning theory)", "Control theory (sociology)", "Control (management)", "Control system", "Computer science", "Control engineering", "Engineering", "Electrical engineering" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://hdl.handle.net/2429/28473" }, { "id": "https://openalex.org/W7127178638", "doi": "10.1109/ieem63636.2025.11357584", "title": "Introducing Adaptive Q-Learning in Agent-Based Models: A Case Study of Household Climate Decision", "year": 2025, "publication_date": "2025-12-07", "type": "conference-paper", "language": null, "authors": [ "Maria Krisnawati", "Yun Prihantina Mulyani", "Bertha Maya Sopha" ], "first_author": "Maria Krisnawati", "venue": null, "publisher": null, "abstract": "This study proposes an Adaptive Q-learning approach within an Agent-Based Modeling (ABM) environment to enhance the effectiveness and stability of household decision-making in climate change mitigation. The a integrates an adaptive learning rate with a dynamic exploration–exploitation strategy that combines ε-greedy and Boltzmann exploration. Implemented in NetLogo 6.3, the model represents the environment as a utility landscape, where agents dynamically navigate various mitigation strategies, including the adoption of electric vehicles, reduction of energy consumption, and investments in renewable energy. Simulation results demonstrate that the proposed approach significantly outperforms standard Q-learning, achieving higher maximum and final utility levels (ANOVA, p < 0.001) and producing more consistent outcomes. Although the number of steps required to reach optimal mitigation is relatively smaller, the difference is not statistically significant (p = 0.24). These findings indicate that the proposed adaptive strategies substantially improve agent decision-making in complex spatial environments. Moreover, the approach has broader applicability beyond climate change mitigation, including UAV navigation, autonomous vehicle routing, and energy optimization.", "keywords": [ "NetLogo", "Stability (learning theory)", "Climate change", "Renewable energy", "Energy (signal processing)", "Reduction (mathematics)", "Adaptive strategies", "Efficient energy use" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1109/ieem63636.2025.11357584" }, { "id": "https://openalex.org/W7135012189", "doi": "10.2139/ssrn.6396622", "title": "Applications of Harmony Search in Transportation/Mobility Optimization: A Systematic Review and Future Directions", "year": 2026, "publication_date": "2026-01-01", "type": "preprint", "language": null, "authors": [ "Minsu Kim", "Zong Woo Geem", "Jiho Yeo" ], "first_author": "Minsu Kim", "venue": "SSRN Electronic Journal", "publisher": "RELX Group (Netherlands)", "abstract": null, "keywords": [ "Scalability", "Harmony search", "Implementation", "Convergence (economics)", "Intelligent transportation system", "Key (lock)", "Public transport" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2139/ssrn.6396622" }, { "id": "https://openalex.org/W7213747991", "doi": "10.5281/zenodo.22859279", "title": "Autonomous Systems: Systems Architecture, Multi-Sensor Perception, and Intelligent Control Dynamics", "year": 2026, "publication_date": "2026-09-20", "type": "other", "language": "en", "authors": [ "Prep4Uni.Online" ], "first_author": "Prep4Uni.Online", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": "This comprehensive foundational curriculum module examines the cyber-physical systems architecture, state estimation theory, and feedback control loops of modern autonomous systems within the Prep4Uni open STEM curriculum. Core Thematic & Pedagogical Foundations: Systems-Level IDEF0 Functional Modeling: Deconstructs autonomous systems education across empirical inputs (automated case studies, dynamic real-world challenges), governing controls (safety verification standards, ISO 26262 frameworks, performance metrics), executing mechanisms (laboratory testbeds, telemetry data, industry mentorship), and verifiable technical outputs. Closed-Loop Autonomy Pipeline: Detailed technical breakdown across five core operational phases: exteroceptive and proprioceptive multi-modal sensing, real-time edge data processing and state estimation ($\\mathbf{x}_t$), path planning under non-holonomic kinematic constraints (A*, RRT*, Model Predictive Control), self-adaptation via fleet learning, and high-torque electromechanical actuator execution. Multi-Sensor Perception & Fusion: Comparative trade-off analysis across optical cameras, solid-state LiDAR point clouds, Doppler RADAR, and ultrasonic arrays, evaluating performance boundaries under varying weather and environmental attenuation. Detailed Industrial Case Studies: Grounded engineering analyses of consumer automated driving (Tesla Vision and fleet neural networks), high-altitude long-endurance aerospace robotics (MQ-9 Reaper UAV), and extraterrestrial planetary exploration (NASA Perseverance rover TRN and AutoNav pipelines in Mars's Jezero Crater). Stopping Distance & Reaction Dynamics: Theoretical foundations and interactive stopping time mechanics modeling perception delays, computing latency, and braking friction coefficients. Quantitative Engineering Computations: Worked step-by-step mathematical problem sets covering UAV headwind transit times, sensor polling rates, sensor fusion accuracy gains, battery power optimization scaling, delivery fleet path reductions, super-resolution camera metrics, robotic assembly shift throughput, and high-speed LiDAR scan coverage.", "keywords": [ "Trajectory", "Sensor fusion", "Situation awareness", "Robotics", "Actuator", "Kinematics", "Intelligent control", "Aerospace" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.22859279" }, { "id": "https://openalex.org/W7213779982", "doi": "10.5281/zenodo.22859278", "title": "Autonomous Systems: Systems Architecture, Multi-Sensor Perception, and Intelligent Control Dynamics", "year": 2026, "publication_date": "2026-09-20", "type": "other", "language": "en", "authors": [ "Prep4Uni.Online" ], "first_author": "Prep4Uni.Online", "venue": "Zenodo (CERN European Organization for Nuclear Research)", "publisher": "European Organization for Nuclear Research", "abstract": "This comprehensive foundational curriculum module examines the cyber-physical systems architecture, state estimation theory, and feedback control loops of modern autonomous systems within the Prep4Uni open STEM curriculum. Core Thematic & Pedagogical Foundations: Systems-Level IDEF0 Functional Modeling: Deconstructs autonomous systems education across empirical inputs (automated case studies, dynamic real-world challenges), governing controls (safety verification standards, ISO 26262 frameworks, performance metrics), executing mechanisms (laboratory testbeds, telemetry data, industry mentorship), and verifiable technical outputs. Closed-Loop Autonomy Pipeline: Detailed technical breakdown across five core operational phases: exteroceptive and proprioceptive multi-modal sensing, real-time edge data processing and state estimation ($\\mathbf{x}_t$), path planning under non-holonomic kinematic constraints (A*, RRT*, Model Predictive Control), self-adaptation via fleet learning, and high-torque electromechanical actuator execution. Multi-Sensor Perception & Fusion: Comparative trade-off analysis across optical cameras, solid-state LiDAR point clouds, Doppler RADAR, and ultrasonic arrays, evaluating performance boundaries under varying weather and environmental attenuation. Detailed Industrial Case Studies: Grounded engineering analyses of consumer automated driving (Tesla Vision and fleet neural networks), high-altitude long-endurance aerospace robotics (MQ-9 Reaper UAV), and extraterrestrial planetary exploration (NASA Perseverance rover TRN and AutoNav pipelines in Mars's Jezero Crater). Stopping Distance & Reaction Dynamics: Theoretical foundations and interactive stopping time mechanics modeling perception delays, computing latency, and braking friction coefficients. Quantitative Engineering Computations: Worked step-by-step mathematical problem sets covering UAV headwind transit times, sensor polling rates, sensor fusion accuracy gains, battery power optimization scaling, delivery fleet path reductions, super-resolution camera metrics, robotic assembly shift throughput, and high-speed LiDAR scan coverage.", "keywords": [ "Trajectory", "Sensor fusion", "Situation awareness", "Robotics", "Actuator", "Kinematics", "Intelligent control", "Aerospace" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5281/zenodo.22859278" }, { "id": "https://openalex.org/W2915997992", "doi": "10.2118/0717-0069-jpt", "title": "Technology Focus: New-Frontier Reservoirs I (July 2017)", "year": 2017, "publication_date": "2017-07-01", "type": "article", "language": "en", "authors": [ "Simon T. Chipperfield" ], "first_author": "Simon T. Chipperfield", "venue": "Journal of Petroleum Technology", "publisher": "Society of Petroleum Engineers", "abstract": "Technology Focus Congratulations! You are one of a select group to open your JPT and find this page! For your effort, you deserve something special, and I know what you want. In 5 minutes or less, you want to know how to immediately make a step change in your business and improve your own promotion prospects in the process. You want to know the key technologies you should be implementing now. To develop your longer-term strategy, you want an awareness of future trends now. You don’t want just anyone to tell you these answers; you want it to come from the very best people in the industry. Consider your wish granted. According to a poll of leaders with more than 220 years of collective experience (see JPT online for names), the top theme to have affected our industry in the past 2 years is Fracture-Stimulation Optimization—Given the high cost, fracture design has gained its fair share of scrutiny in the downturn. The results of statistical production analysis have varied by play. Fluid types and chemical additives have been removed or modified. Alternative proppants have been evaluated and, in some cases, trialed successfully outside their previously specified safe-performance ranges. Nothing is sacred. The top theme to affect our industry in the next 2 years is predicted to be Intelligent Operations—The industry is moving toward increasing the level of automation in all activities. All areas from exploration to shipping will be touched by these technologies. This includes drones for safer, demanned operations as well as robotics on drilling rigs. The other key area of automation is predictive analytics, which allows us to analyze multivariant problems like never before. Fracture-design optimization, drilling analytics to improve rate of penetration, and optimized operator vehicle routing for field-staff and plant optimization are just a few areas that will be transformed. Now, wasn’t it worth the effort to read this? Recommended additional reading at OnePetro: www.onepetro.org. SPE 182230 Issues and Challenges With Controlling Large Drawdown in the First Offshore Methane-Hydrate Production Test by S. Sakurai, Japan Oil, Gas, and Metals National Corporation, et al. SPE 180399 Petrophysical Evaluation of Natural-Gas Hydrates—North Slope, Alaska by Douglas Hupp, Schlumberger, et al. SPE 180818 Proppant Management: A New Challenge To Develop Unconventional Reservoirs in Argentina by J.C. Bonapace, Halliburton", "keywords": [ "Scrutiny", "Frontier", "Theme (computing)", "Promotion (chess)", "SAFER", "Public relations", "Marketing", "Business", "Computer science", "Political science", "Law", "World Wide Web" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.2118/0717-0069-jpt" }, { "id": "https://openalex.org/W7168216575", "doi": "10.3390/math14142500", "title": "Evolutionary Multi-Objective Optimization of a Multi-Echelon Humanitarian VRP for Smart City Logistics Under Uncertainty", "year": 2026, "publication_date": "2026-07-11", "type": "article", "language": "en", "authors": [ "Esra Çakır" ], "first_author": "Esra Çakır", "venue": "Mathematics", "publisher": "Multidisciplinary Digital Publishing Institute", "abstract": "Efficient and resilient humanitarian logistics is critical for smart cities facing large-scale disasters, where infrastructure disruptions, uncertain demand, and time-critical deliveries complicate operational planning. This study proposes a multi-echelon vehicle-routing framework that integrates trucks, electric unmanned aerial vehicles (UAVs), and micromobility systems under uncertainty. Demand and travel-time variability are modeled through scenario-based representations, while delivery flexibility is captured through triangular fuzzy time windows defined by earliest-acceptable, preferred, and latest-tolerable delivery times. The problem is formulated as a four-objective optimization model that minimizes total cost, response time, CO2 emissions, and fuzzy lateness. To solve the resulting highly constrained multi-objective problem, NSGA-II and NSGA-III are adapted with problem-specific repair operators, including capacity-splitting, range-feasibility correction, and fuzzy time-shift adjustment mechanisms, and embedded in a simulation-based evaluation framework. The proposed approach is validated using a geo-referenced earthquake scenario in Istanbul, constructed from open-source GIS, traffic, and demographic data. The computational results show that evolutionary methods generate feasible solutions within minutes, whereas exact optimization approaches fail to converge for realistic instances. Compared with NSGA-II, NSGA-III achieves superior performance, including a 9% higher hypervolume and improved robustness under stress-test scenarios. Furthermore, the hybrid truck–UAV–micromobility strategy reduces average cost by up to 30%, delivery time by 43%, and CO2 emissions by 65% relative to a truck-only baseline, while eliminating fuzzy lateness. These findings demonstrate that evolutionary multi-objective optimization provides an effective and scalable decision-support framework for uncertainty-aware and sustainable humanitarian logistics in smart cities.", "keywords": [ "Flexibility (engineering)", "Smart city", "Robustness (evolution)", "Fuzzy logic", "Evolutionary algorithm", "Scalability", "Robust optimization", "Optimization problem", "Evolutionary computation" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.3390/math14142500" }, { "id": "https://openalex.org/W7198312143", "doi": null, "title": "Implementation of artificial intelligence and robotic automation technologies in e-commerce logistics to optimize the “last mile”", "year": 2025, "publication_date": "2025-11-01", "type": "dissertation", "language": "uk", "authors": [ "Ігнатенко Анна Віталіївна", "Марина Володимирівна Котова" ], "first_author": "Ігнатенко Анна Віталіївна", "venue": "Electronic Institutional Repository of the National Aviation University of Ukraine (National Aviation University, Ukraine)", "publisher": "National University \"Kyiv Aviation Institute\"", "abstract": "1. Accenture. (2021). Future of Last-Mile Delivery: The E-commerce Logistics Race. 2. Agatz, N. A., Bouman, P., & Schmidt, M. (2018). Optimization Approaches for the Last Mile in E-commerce: Literature Review and Future Research. Transportation Science, 52(4), 1146-1166. 3. Winkelbauer, M., & Störk, J. (2021). Dynamic vehicle routing for ondemand food delivery: a deep reinforcement learning approach. European Journal of Operational Research, 290(3), 903-918. 4. Luo, S., Liu, C., & Zhang, W. (2020). Delivery robots and the last mile: Modeling the operational costs and environmental impacts. International Journal of Production Economics, 223, 107519. 5. Wang, R., & Li, S. (2022). Unmanned Aerial Vehicles (UAVs) in Last-Mile Logistics: Challenges and Opportunities. IEEE Transactions on Intelligent Transportation Systems, 23(1), 12-25. 6. Gunes, Z., & Karagül, A. (2020). The role of Autonomous Mobile Robots (AMR) in fulfillment centers: A review of key design factors and operational benefits. Procedia Computer Science, 180, 52-61. 7. Deloitte. (2019). The future of mobility: The last-mile delivery challenge. 8. European Commission. (2020). Study on Regulatory framework for the deployment of autonomous vehicles in the EU.", "keywords": [ "Software deployment", "Automation", "Robot", "Key (lock)", "Robotics", "Mobile robot" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://er.kai.edu.ua/handle/KAI/69426" }, { "id": "https://openalex.org/W4402950074", "doi": "10.1108/ijpdlm-07-2024-557", "title": "Guest editorial: NOFOMA 2023 – Logistics during global crises", "year": 2024, "publication_date": "2024-09-28", "type": "editorial", "language": "en", "authors": [ "Markku Kuula", "Gyöngyi Kovács" ], "first_author": "Markku Kuula", "venue": "International Journal of Physical Distribution & Logistics Management", "publisher": "Emerald Publishing Limited", "abstract": "It may seem in recent times that we are living through multiple crises, each with their own cycles. While recovering from a global pandemic, we are facing new economic and geopolitical situations, realignments and outright crises. Each new situation comes with its own implications for production, logistics and supply chain management overall.Other aspects also factor in, each with its unique impact on the supply chain: Climate change alters the usability of especially inland waterways (Christodoulou et al., 2020), but even railways struggle with melting tracks in the heat. It is not only the patterns and the impact of hydro-meteorological disasters that are changing but when and how we need to respond to them (Sohn, 2022). An ever-increasing scarcity of critical raw materials may hamper production across many industries (Mouloudi and Evrard Samuel, 2022), paired with difficulties not only to access raw materials but also labour conditions for mining them; alongside the conflicts and geopolitical tit for tats they generate (Hofmann et al., 2018). Pandemics may lead to lockdowns in production and blockages of transportation routes (Ahlqvist et al., 2023; Falagara Sigala et al., 2022; Rogerson et al., 2024). Not that we need a pandemic to block routes, as the Ever Given so aptly illustrated when blocking the Suez Canal in 2021. Geopolitical realignments play out economically (e.g. when Brexit altered preferred trade partners and routes way beyond customs and economic unions; Charpin, 2022) or through outright conflicts (Gruchmann et al., 2024). Conflicts and wars are on the rise, currently with, e.g. Ukraine, Gaza and Sudan in the spotlight. They have already resulted beyond their immediate impacts on their populations. Wider economic repercussions include sanctions and embargoes on some vs support to other parties, a rise of the defence and security sectors overall and geopolitical realignments, such as the NATO accession of both Finland and Sweden. Sanctions and embargoes play out through supply chains (Charpin, 2022), with changes in sourcing and delivery patterns and even in transportation routes through no-fly zones. Even without big events, consumption patterns change, altering anything from package sizes to delivery patterns (Bjerkan and Babri, 2024), and demographic changes require a rethinking of many public sector supply chains, starting with health care and elderly care. It is against this backdrop that the NOFOMA 2023 conference selected the theme of “Logistics During Global Crises”.Logistics and supply chain management are not only impacted by many of these factors but can in fact both alleviate and mitigate some or cause others. Supply chain risk management has for a long time been focusing on mitigation strategies for various types of risks: from due diligence efforts to dual or multiple sourcing, capacity reservation systems, safety stock, prepositioning in global supply chains and the core of supply chain design and configuration, facility location and outsourcing. Offshoring vs reshoring come in cycles across globalisation and deglobalisation, depending on whether the focus is on cost efficiencies in predictable peace times or quick access to materials and components during pandemics or as an answer to sanctions and embargoes. Newer concepts include backshoring, nearshoring or nowadays friendshoring, with the latter also being deployed in light of geopolitical and economic realignments (Charpin, 2022) with regards to, for example, critical raw materials and energy (cf. Vivoda, 2023; Zhang et al., 2024).As the pandemic goes on, however, we tend to underestimate how global supply chains really are. Bringing back final assembly to a country does not necessarily overcome dependencies on components on raw materials from others. The many types of risk management and mitigation strategies in supply chains are an art form (Ahlqvist et al., 2023; Gruchmann et al., 2024) and have given rise to many important concepts: supply chain resilience, robustness, responsiveness, flexibility and agility, to name but a few. A few other things learnt from the pandemic include that dual sourcing needs to factor in geographical spread or that production changeover is something one can also deploy in transportation, with, e.g. passenger planes being converted to cargo planes for pharmaceuticals and personal protective equipment (Falagara Sigala et al., 2022). Even drones have been suggested as a way to deliver test swabs to medicine while reducing contagion (Kunovjanek and Wankmüller, 2021).From a wider perspective, the choices we make in this area bear vast implications for the planet and its inhabitants. Considering the planet may seem remote from a supply chain perspective (Wieland, 2021), yet products, services, all use and consumption and, thereby, all choices in the supply chain have their own ecological footprints and handprints. As of late, the attention has turned more to, e.g. water and energy footprints in the supply chain, to circular supply chains and to how the supply chain impacts biodiversity. Greening the supply chain has been around for a while, albeit changing what it all encompasses (Seuring et al., 2022) and how urgent these measures are. Greening is also in focus for reasons in production: more and more innovative products, production systems and even transportation being digitalised and electrified requires that we have better energy solutions. They come with their own implications for energy supply chains and also with a renewed focus on the energy footprint of supply chains (Halldórsson and Svanberg, 2013), not only from a cost but also a robustness and energy security perspective.The human element of sustainable supply chain management has become more wind under its wings as well. Already years ago, “modern slavery” was in the limelight, especially in the UK and later in Germany with the Lieferkettengesetz. Now the EU has brought due diligence into focus with the corporat", "keywords": [ "Business", "Marketing", "Operations management", "Economics" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1108/ijpdlm-07-2024-557" }, { "id": "https://openalex.org/W2500580130", "doi": null, "title": "Route planning in wireless sensor networks for data gathering purposes", "year": 2015, "publication_date": "2015-01-01", "type": "dissertation", "language": "pt", "authors": [ "Andriy Mazayev" ], "first_author": "Andriy Mazayev", "venue": "Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)", "publisher": null, "abstract": "As redes de sensores têm vindo a ganhar importância em vários setores da nossa sociedade, sendo a aplicação mais visível a monitorização ambiental. Estas redes têm inúmeras aplicações que vão desde a detecção de fogos, medições do nível de emissões de radiação, detecções de danos estruturais e muito mais, sendo capazes de fazer medições de forma sistemática, e possibilitando desta forma, tomar decisões antecipadas e, assim, evitar acidentes que podem custar vidas humanas. O envio de dados recolhidos pelos sensores é tradicionalmente feito através de múltiplos saltos (multi hop). Este método baseia-se na passagem dos dados de dispositivo para dispositivo, através da infraestrutura da rede, permitindo que a informação viage desde a fonte até ao destino graças à passagem dos dados entre elementos vizinhos. Para encontrar a forma mais eficiente e rápida de fazer chegar a informação ao destino foram propostos, e estão disponíveis na literatura, vários algoritmos que tentam garantir o uso eficaz dos recursos da rede durante o envio dos dados. No entanto, nem mesmo os algoritmos mais eficientes são capazes de resolver um problema inerente a esta arquitectura: a entrega dos dados está dependente de vários elementos intermediários. Para além disso, o próprio meio ambiente em que as redes de sensores são utilizadas pode ser um obstáculo durante a comunicação. Possíveis acidentes tais como fogos ou desabamentos podem danificar os canais de comunicação e, desta forma, tornar uma rede de sensores inutilizável. As desvantagens presentes na abordagem clássica de recolha de dados levou os investigadores a procurarem outras alternativas. Estudos recentes têm mostrado que a utilização de elementos móveis em redes de sensores sem fio pode reduzir drasticamente o consumo de energia neste tipo de redes e, assim, prolongar o seu tempo de vida. As abordagens mais recentes no processo de recolha dos dados numa rede de sensores têm recaído sobre o veículo aéreo não tripulado, frequentemente conhecido como drone. Embora estes elementos sejam mais velozes do que os elementos móveis terrestres, e devido à sua natureza que permite a sua utilização numa vasta gama de situações, há um pequeno número de estudos sobre a eficácia da sua utilização. Ao contrário da abordagem multi salto, onde os algoritmos procuram caminhos eficientes dentro da própria infraestrutura de rede, a abordagem que envolve elementos móveis necessita de algoritmos que criem caminhos para os próprios elementos móveis. O processo de desenho e criação das rotas é conhecido por problema de recolha de dados (Data Gathering Problem). O problema de recolha de dados a ser resolvido nesta tese pode ser descrito como o processo de criação de caminhos para um conjunto de veículos aéreos não tripulados que têm de recolher dados alojados nos sensores. Terão que ser considerados os seguintes aspectos: Janelas temporais de cada sensor. Intervalo de tempo em que o drone tem que visitar o sensor em causa; Volume dos dados a serem enviados pelos sensores; Tempo de vida útil dos dados. Intervalo de tempo máximo permitido para que a informação seja entregue no destino; Conjunto de veículos aéreos. Cada veículo aéreo tem a sua capacidade de memória, isto é, limite máximo da quantidade de dados que um drone é capaz de recolher. O processo de criação dos caminhos tem que respeitar todas as restrições temporais dos sensores e, ao mesmo tempo, terá que ser minimizada a distância necessária para recolher os dados de todos os sensores. A utilização dos veículos aéreos não tripulados é uma abordagem recente e, talvez por isso, ainda não ganhou a merecida atenção. Como tal, de momento, não existem standards de medição de desempenho de diferentes algoritmos capazes de resolver este tipo de problemas. Nesta tese, tentamos preencher esta lacuna propondo um padrão de medição de desempenho que pode ser utilizado por todos. São apresentadas duas formalizações matemáticas do problema, uma genérica que pode ser utilizada em vasta gama de situações com semelhantes restrições e uma formalização estendida que representa o problema a ser resolvido nesta tese. A formalização genérica permite, identificar este problema com o do encaminhamento de veículos com janelas temporais (Vehicle Routing Problem with Time Windows). É feita ainda uma análise aprofundada de algumas classes de algoritmos já existentes que podem ser úteis na resolução deste tipo de problemas. Heurísticas construtivas, de melhoramento e meta-heurísticas são introduzidas e alguns dos seus principais métodos são discutidos. Com base estes métodos foi desenvolvida uma heurística híbrida capaz de criar caminhos eficientes não só para o problema recolha de dados numa rede de sensores mas também capaz de resolver o problema de encaminhamento de veículos. A heurística apresentada é baseada em três etapas de optimização. A primeira, pré-optimização, que com base nos dados de entrada, cria uma boa solução parcial que será utilizada nos passos seguintes. A segunda fase, designada por optimização, que recebe a solução parcial criada no passo anterior, é responsável pela construção de uma solução inicial que satisfaça todas as restrições de entrada. A última fase, pós-optimização, que recebe como dado de entrada a solução obtida durante a optimização, através de uma combinação de métodos tanto determinísticos como não determinísticos explora de forma eficiente o espaço de procura em busca da melhor solução. Para medir o desempenho da heurística híbrida desenvolvida nesta tese foi feito um extensivo conjunto de testes considerando de redes de sensores de várias dimensões, redes com 25, 50, 75 e 100 sensores, e diferentes níveis de restrições temporais. Os resultados obtidos mostram que o algoritmo proposto é capaz de resolver o problema de recolha de dados de forma bastante eficiente apresentando, para a maioria dos conjuntos de dados e para diferentes intervalos de prazos de entrega, um alto grau de certeza relativamente à qualidade ", "keywords": [ "Wireless sensor network", "Computer science", "Telecommunications", "Data collection", "Wireless network", "Computer network", "Wireless", "Mathematics" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "http://hdl.handle.net/10400.1/7998" }, { "id": "https://openalex.org/W4403618371", "doi": "10.48550/arxiv.2409.05545", "title": "Adaptive Probabilistic Planning for the Uncertain and Dynamic Orienteering Problem", "year": 2024, "publication_date": "2024-09-09", "type": "preprint", "language": "en", "authors": [ "Qiuchen Qian", "Yanran Wang", "David Edward Boyle" ], "first_author": "Qiuchen Qian", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "The Orienteering Problem (OP) is a well-studied routing problem that has been extended to incorporate uncertainties, reflecting stochastic or dynamic travel costs, prize-collection costs, and prizes. Existing approaches may, however, be inefficient in real-world applications due to insufficient modeling knowledge and initially unknowable parameters in online scenarios. Thus, we propose the Uncertain and Dynamic Orienteering Problem (UDOP), modeling travel costs as distributions with unknown and time-variant parameters. UDOP also associates uncertain travel costs with dynamic prizes and prize-collection costs for its objective and budget constraints. To address UDOP, we develop an ADaptive Approach for Probabilistic paThs - ADAPT, that iteratively performs 'execution' and 'online planning' based on an initial 'offline' solution. The execution phase updates system status and records online cost observations. The online planner employs a Bayesian approach to adaptively estimate power consumption and optimize path sequence based on safety beliefs. We evaluate ADAPT in a practical Unmanned Aerial Vehicle (UAV) charging scheduling problem for Wireless Rechargeable Sensor Networks. The UAV must optimize its path to recharge sensor nodes efficiently while managing its energy under uncertain conditions. ADAPT maintains comparable solution quality and computation time while offering superior robustness. Extensive simulations show that ADAPT achieves a 100% Mission Success Rate (MSR) across all tested scenarios, outperforming comparable heuristic-based and frequentist approaches that fail up to 70% (under challenging conditions) and averaging 67% MSR, respectively. This work advances the field of OP with uncertainties, offering a reliable and efficient approach for real-world applications in uncertain and dynamic environments.", "keywords": [ "Orienteering", "Probabilistic logic", "Mathematical optimization", "Computer science", "Operations research", "Mathematics", "Artificial intelligence" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2409.05545" }, { "id": "https://openalex.org/W7119329065", "doi": null, "title": "Resolvedor Monte Carlo de espaço de ação contínuo para problemas de processos decisórios Markovianos com restrições de probabilidade", "year": 2024, "publication_date": "2024-12-09", "type": "dissertation", "language": "en", "authors": [ "Gustavo de Moura Souza" ], "first_author": "Gustavo de Moura Souza", "venue": "LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)", "publisher": "LA Referencia", "abstract": "The use of Unmanned Aerial Vehicles (UAV) expands constantly across various domains, from military applications to environmental monitoring and urban planning. Path planning for UAVs, especially in complex and dynamic environments, presents significant challenges, particularly when factoring in uncertainty and risk constraints. This dissertation focuses on developing a path planning algorithm utilizing Monte Carlo Tree Search (MCTS) that addresses chance constrained routing problems in continuous action spaces. Previous research in UAV path planning has extensively explored both deterministic and probabilistic methods. This study builds upon the probabilistic framework, aiming to leverage the strengths of MCTS in handling uncertainty and providing a flexible yet efficient path planning solution. The methodology involved the adaptation of the Vulcan algorithm to a continuous action space and its integration to Gymnasium, resulting in the development of the Nashor algorithm. It incorporates significant modifications, including continuous feasibility assessment at each state transition, to ensure practical and reliable route planning. Experimental setups included simulations in a custom-designed virtual environment named Itomori, created within the Gymnasium framework, facilitating the testing of the algorithm under various conditions, including different levels of GPS uncertainty and obstacle densities. Data were collected from numerous simulation runs across various map configurations and different discretization techniques, Hierarchical Partitions, as well as evaluations of the algorithms performance under varying risk constraints. Experiments demonstrated that the discretization techniques could sufficiently allow Nashor to find solutions for all tested maps, including the most challenging ones. The algorithms performance is significantly influenced by the acceptable level of risk, showing that, while impractically high uncertainty levels make solution finding difficult, Nashor still managed to identify viable routes under realistic uncertainty scenarios. Nashors ability to adapt to different levels of risk and uncertainty marks a important advancement in the field. Additionally, the development of the Itomori simulation environment offers a versatile platform for future research, allowing for the testing and validation of new path planning algorithms under a variety of conditions. This work contributes new studies in MCTS and discretization and also paves the way for further advancements in simulations involving risk by facilitating the communication between the environment and new algorithms.", "keywords": [ "Probabilistic logic", "Motion planning", "Monte Carlo method", "Monte Carlo tree search", "Path (computing)", "Discretization", "Leverage (statistics)", "Tree (set theory)", "Global Positioning System" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.teses.usp.br/teses/disponiveis/55/55134/tde-23042025-131038/" }, { "id": "https://openalex.org/W4292955593", "doi": "10.48550/arxiv.2208.09861", "title": "The Single Robot Line Coverage Problem: Theory, Algorithms, and Experiments", "year": 2022, "publication_date": "2022-08-21", "type": "preprint", "language": "en", "authors": [ "Saurav Kumar Agarwal", "Srinivas Akella" ], "first_author": "Saurav Kumar Agarwal", "venue": "arXiv (Cornell University)", "publisher": "Cornell University", "abstract": "Line coverage is the task of servicing a given set of one-dimensional features in an environment. It is important for the inspection of linear infrastructure such as road networks, power lines, and oil and gas pipelines. This paper addresses the single robot line coverage problem for aerial and ground robots by modeling it as an optimization problem on a graph. The problem belongs to the broad class of arc routing problems and is closely related to the rural postman problem (RPP) on asymmetric graphs. The paper presents an integer linear programming formulation with proofs of correctness. Using the minimum cost flow problem, we develop approximation algorithms with guarantees on the solution quality. These guarantees also improve the existing results for the asymmetric RPP. The main algorithm partitions the problem into three cases based on the structure of the required graph, i.e., the graph induced by the features that require servicing. We evaluate our algorithms on road networks from the 50 most populous cities in the world, consisting of up to 730 road segments. The algorithms, augmented with improvement heuristics, run within 3s and generate solutions that are within 10% of the optimum. We experimentally demonstrate our algorithms with commercial UAVs.", "keywords": [ "Heuristics", "Correctness", "Computer science", "Arc routing", "Linear programming", "Algorithm", "Robot", "Mathematical optimization", "Integer programming", "Graph", "Routing (electronic design automation)", "Theoretical computer science" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://arxiv.org/pdf/2208.09861" }, { "id": "https://openalex.org/W4289450859", "doi": "10.13031/aim.202200118", "title": "Robust Route Planning for Autonomous Vehicles", "year": 2022, "publication_date": "2022-01-01", "type": "conference-paper", "language": "en", "authors": [ "Zhicheng Zhu", "Yiannis G. Ampatzidis", "Abhisesh Silwal", "Pãnos M. Pardalos" ], "first_author": "Zhicheng Zhu", "venue": "2022 Houston, Texas July 17-20, 2022", "publisher": null, "abstract": "Abstract. Autonomous vehicles carrying agrochemicals have been used to treat weeds in large fields. Route planning is one of the core tasks in weed management to reduce the operational cost. In this planning problem, a fleet of vehicles with limited tank capacity needs to cover each path of the field to treat different types of weeds and replenish them at the depot if needed. In the literature, the problem is usually formulated by a capacitated arc routing model, which is nonlinear and requires additional efforts for linearization. Moreover, most of the existing research assumes the weed distribution in fields is perfectly known, which is not true in practice due to the limit of current technologies and therefore leaves the robustness of the plan in question. In this research, we study this route planning problem in the presence of the weed distribution estimation error. We first provide a linear vehicle routing model to formulate the problem with deterministic weed distributions. Based on this model, we propose a robust optimization model to incorporate the uncertainty of weed distributions by using a set, call uncertainty set, that includes all possible realizations of weed distributions. The objective of the robust model becomes seeking a route feasible to all weed distribution realizations in the uncertainty set with the minimum cost. Nowadays, unmanned aerial vehicles provide a cost-effective way to estimate weed distribution. We, therefore, take advantage of this new technology by including the estimation of weed distributions from UAV in the construction of uncertainty sets. Numerical results demonstrate the need of using the robust approach and show that the proposed model leads to a better out-of-sample performance.", "keywords": [ "Robustness (evolution)", "Computer science", "Mathematical optimization", "Weed", "Mathematics" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.13031/aim.202200118" }, { "id": "https://openalex.org/W7122449440", "doi": "10.1108/imds-01-2026-997", "title": "Guest editorial: Emerging insights from synchromodality and co-modality research", "year": 2026, "publication_date": "2026-01-12", "type": "editorial", "language": "en", "authors": [ "Xuan Yang", "Pengyu Yan", "Russell George Thompson", "Xiang T.R. Kong" ], "first_author": "Xuan Yang", "venue": "Industrial Management & Data Systems", "publisher": "Emerald Publishing Limited", "abstract": "Over the past decade, the global transport and logistics sector has undergone a rapid transformation driven by technological innovation, environmental imperatives and the pursuit of operational resilience. Advances in digital platforms, real-time data integration and algorithmic decision support have significantly enhanced the ability to coordinate flows across modes, enabling more flexible and demand-responsive operations (Kong et al., 2025). Within this evolving landscape, synchromodality and co-modality have emerged as two complementary paradigms redefining how multiple transport modes are jointly planned and utilised, supported by new digital infrastructures that allow dynamic switching and seamless multimodal orchestration.Synchromodality refers to the dynamic and real-time management of freight flows across interconnected modes – such as rail, road, barge and air – where shippers can switch between modes in response to operational or environmental conditions (Verweij, 2011). It represents an evolution beyond intermodal and co-modal transport, emphasising synchronisation, digital integration and decision flexibility (Haller et al., 2015; Tavasszy et al., 2017). In contrast, co-modality highlights the joint use of passenger and freight transport infrastructure, advocating for the optimal combination of services within a unified system to enhance both efficiency and sustainability (Zhu et al., 2023). While synchromodality prioritises real-time mode substitution and system-wide coordination, co-modality focuses on shared capacity and the cross-utilisation of transport assets for people and goods. Together, they offer a systemic response to contemporary challenges – congestion, emissions, the energy transition and urban accessibility – by promoting seamless, intelligent and resource-efficient mobility networks.This Special Issue of Exploring Synchromodality and Co-modality: Pathways to Sustainable and Efficient Transportation System, brings together interdisciplinary perspectives and emerging evidence on how digitalisation, optimisation, behavioural insight and governance mechanisms can advance integrated mobility. The collected studies highlight innovative modelling approaches, empirical validations and real-world applications that jointly illustrate how these paradigms can accelerate the transition toward sustainable, adaptive and data-driven transport systems. Beyond the operational layer, the contributions also underscore the socio-technical and institutional changes needed to foster logistics ecosystems capable of real-time responsiveness and cross-modal collaboration.From an academic standpoint, synchromodality has evolved from a classical multimodal concept into a supply-chain-centric framework where flexibility, cooperation and synchronisation form its core principles (Nobis, 2007; Rondinelli and Berry, 2000). Dong et al. (2018) reframed it as “Synchromodality from a Supply Chain Perspective,” arguing that a holistic view of logistics processes amplifies mode-shift potential without sacrificing cost or service levels. Pfoser et al. (2016) identified cooperation and transparency as decisive success factors, while Giusti et al. (2019) emphasised the pivotal role of orchestration platforms in enabling real-time decision-making. More recently, the emergence of digital logistics platforms has further strengthened this paradigm by facilitating decentralised yet coordinated decision-making, allowing multiple actors to align procurement, transport planning and service contracting through market-based or algorithmic mechanisms (Kong et al., 2023). Haller et al. (2015) and Sakti et al. (2023) likewise associated synchromodality with the vision of the Physical Internet, highlighting synchronisation as a foundation for adaptive, platform-driven and learning-based logistics.In parallel, co-modality research has followed a progressive trajectory – from conceptual framing to technological enablement, operational optimisation and stakeholder coordination. Early work by Trentini and Mahléné (2010) introduced the concept of passenger–goods cohabitation as a pathway to reduce economic and environmental externalities. Building on this foundation, Ronald et al. (2016) showed how ICT and on-demand mobility technologies make such integration practically feasible. More recent studies by Yang et al. (2023, 2024) developed crowdsourced and schedule-based co-modality models, demonstrating that synchronised passenger–freight systems can achieve cost-effective, low-emission urban distribution. Finally, Ma et al. (2023) examined cooperative and competitive interactions among operators through game-theoretical analysis, revealing how coordinated arrangements can yield Pareto-improving outcomes. Collectively, these studies trace a clear evolution from conceptual vision to intelligent and collaborative implementation, highlighting synchronisation, cooperation and digital intelligence as the core enablers of sustainable co-modality.Across the globe, synchromodality has found fertile ground in major logistics corridors and ports. In Europe, the Port of Rotterdam epitomises digitally orchestrated logistics through its Port Vision 2030 and integrated platforms such as Pronto, Navigate and Portmaster, which align maritime, barge, rail and truck operations (portofrotterdam.com). Similar ambitions are visible in the Horizon 2020 SYNCHRO-NET project (synchronet.eu), which merged cloud optimisation and smart-steaming technologies to improve both energy efficiency and network robustness. In the USA, the Georgia Ports Authority's Savannah model extends this philosophy inland: its Mason Mega Rail complex and associated hubs synchronise maritime and rail flows (gaports.com), while the Port of Los Angeles' “Port Optimizer™” platform links terminals, carriers, and shippers in a single digital environment, enabling proactive modal decisions (portoflosangeles.org). In China, the Western Land–Sea New Corridor and the Ningbo–Zhou", "keywords": [ "Flexibility (engineering)", "Sustainability", "Digital transformation", "Mode (computer interface)", "Big data", "Sustainable transport", "Efficient energy use", "Multimodal transport" ], "cited_by_count": 1, "is_retracted": false, "is_paratext": false, "open_access_url": "https://www.emerald.com/imds/article-pdf/126/1/1/11181760/imds-01-2026-997en.pdf" }, { "id": "https://openalex.org/W7164414340", "doi": "10.1117/12.3094870", "title": "MonoGraph-depth: dynamic graph feature refinement for monocular depth estimation", "year": 2026, "publication_date": "2026-06-11", "type": "conference-abstract", "language": null, "authors": [ "Ishan Narayan", "Shashi Poddar" ], "first_author": "Ishan Narayan", "venue": null, "publisher": null, "abstract": "Monocular depth estimation in aerial imagery is often compromised by boundary smearing, where the sharp elevation changes of buildings ”bleed” into the surrounding terrain. While Vision Transformers (ViTs) can resolve absolute scale, their high computational cost and tendency to over-smooth spatial details limit their effectiveness for high-resolution topographical mapping. We propose MonoGraph-Depth, a dual-scale architecture that treats depth estimation as a dynamic graph-routing problem. By utilizing a Bipartite Global Graph Attention network, our model anchors the absolute scale of the scene using a linear-time memory footprint. To preserve structural sharpness, we introduce a Windowed GraphSAGE module that uses a dynamic edge-prediction mechanism to identify vertical ledges. By autonomously severing graph connections at these boundaries, the network prevents feature bleeding and maintains the geometric integrity of urban infrastructure. Finally, we employ PixelShuffle sub-pixel convolutions to recover fine-grained details without the blurring inherent in standard upsampling. Evaluated on the WHU Aerial MVS dataset, MonoGraph-Depth significantly outperforms established benchmarks in boundary precision and scale accuracy. Our results establish a memory-efficient, high-precision blueprint for next-generation aerial photogrammetry and autonomous UAV navigation.", "keywords": [ "Feature (linguistics)", "Graph", "Pattern recognition (psychology)", "Monocular", "Cut" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.1117/12.3094870" }, { "id": "https://openalex.org/W4413679960", "doi": "10.23919/fusion65864.2025.11124105", "title": "Optimizing Rescue Operations in Urban Forests: A Data Mining-Enhanced ACO Approach", "year": 2025, "publication_date": "2025-07-07", "type": "conference-paper", "language": "en", "authors": [ "Almir Monteiro", "Cláudio André da Silva Alves", "Pedro Henrique González" ], "first_author": "Almir Monteiro", "venue": null, "publisher": null, "abstract": "Green infrastructure plays a pivotal role when combating the temperature rises caused by climate change. In addition, in the context of smart and sustainable cities, those spaces, such as urban forests, can be used as places for tours and physical activities in contact with nature, even in big urban areas. However, as the number of people using those spaces increases, so does the risk that an individual gets lost. This kind of incident must be quickly addressed in order to avoid severe health outcomes to the individual. Those rescue operations mobilize firefighters and expensive equipment, such as helicopters, making them extremely costly to public authority. In order to reduce the search time in those rescue operations, this paper considers the Data Mule Routing Problem with Limited Autonomy (DMRP-wLA). This research proposes a hybrid algorithm, composed of an Ant Colony Optimization algorithm enhanced by data mining techniques, to find high-quality solutions to the highlighted problem. Experimental results indicate that ACO-DM consistently outperforms traditional ACO approaches, achieving the best average solution costs in 11 out of 15 evaluated scenarios. In comparison with existing algorithms in the literature, the proposed ACO-DM approach provided substantial cost reductions—between 20 % and 60 % in large-scale instances—while maintaining practical UAV operational times.", "keywords": [ "Computer science" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.23919/fusion65864.2025.11124105" }, { "id": "https://openalex.org/W4391273318", "doi": "10.11896/jsjkx.201000155", "title": "Load-balanced Geographic Routing Protocol in Aerial Sensor Network", "year": 2022, "publication_date": "2022-02-01", "type": "article", "language": "en", "authors": [ "LIU Ai-jun HUANG Xin-quan" ], "first_author": "LIU Ai-jun HUANG Xin-quan", "venue": "DOAJ (DOAJ: Directory of Open Access Journals)", "publisher": null, "abstract": "The unbalanced burden on the nodes nearing the ground station pose challenges on the multi-hop data transmission in aerial sensor networks(ASNs).In order to achieve reliable and efficient multi-hop data transmission in ASNs,a reinforcement-learning based queue-efficient geographic routing(RLQE-GR) protocol is proposed.The RLQE-GR protocol maps routing problem into the general reinforcement learning(RL) framework,where each UAV is treated as one state and each successful packet forwarding is treated as one action.Based on the framework,the RLQE-GR protocol designs a reward function related to geographical location,link quality and available transmission queue length.Then,the Q-function is employed to converge all the sta-teaction values(Q-values),and each packet is forwarded based on potential state-action values.To converge all Q values and minimize performance deterioration during the convergence process,a beacon mechanism is employed in RLQE-GR protocol.In contrast to existing geographic routing protocols,the RLQE-GR protocol simultaneously takes the queue utilization,link quality and relative distance into consideration for forwarding packets.This makes the RLQE-GR protocol achieve load balancing,meanwhile not introducing strict performance deteriorations on routing hop and link quality.Moreover,due to the near-optimization character of RL theory,the RLQE-GR protocol can achieve routing performance optimization on packet delivery ratio and end-to-end delay.", "keywords": [ "Zone Routing Protocol", "Computer network", "Routing protocol", "Computer science", "Protocol (science)", "Enhanced Interior Gateway Routing Protocol", "Routing (electronic design automation)", "Dynamic Source Routing", "Geography", "Medicine" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doaj.org/article/d51b31f818f14279831b3be26785f6a1" }, { "id": "https://openalex.org/W7128235602", "doi": "10.5821/dissertation-2117-454074", "title": "Optimizing two-echelon city logistics with autonomous vehicles: an integrated assessment framework for sustainable last-mile deliveries", "year": 2024, "publication_date": "2024-11-04", "type": "dissertation", "language": "en", "authors": [ "Clément Lemardelé" ], "first_author": "Clément Lemardelé", "venue": null, "publisher": null, "abstract": "(English) As the improvement of last-mile logistics operations becomes a more and more pressing challenge to make our cities more sustainable, both from an economic and environmental perspective, a wide sample of new technologies and innovative distribution schemes are currently being piloted by logistics companies and studied within the research community. Beyond the necessary shift in the powertrain technologies, from internal combustion engine vehicles to electric ones, more efficient delivery strategies and fleet typologies are needed. This is especially flagrant in the particular use case of the courier, express, parcel market, which has surged in the last few years and is the source of many inefficiencies in our neighborhoods. In this context, two-echelon networks appear as potential solutions. In these schemes, the journey of freight is divided into two distinct parts, first from the carrier’s distribution center to a network of micro-hubs and then to its final destination. Nevertheless, even if there is a reduction of the total travelled kilometers and greenhouse gas emissions, two-echelon deliveries using e-cargobikes, which is the most common model at the moment, may result in more operation costs than conventional deliveries with vans because of more complex logistics processes and higher infrastructure costs. In addition, e-cargobikes have a lower capacity, which affects their operational efficiency since more delivery routes are needed, leading to an increase of the vehicle fleet. To make two-echelon delivery schemes economically sustainable, which will eventually lead to large-scale deployments, autonomous vehicles (AVs), both ground ones and air delivery drones, seem to be promising solutions with the potential to reduce the delivery operation costs and, at the same time, the emissions of greenhouse gases. Nevertheless, the exact benefits of AVs, jointly with other technologies, such as intelligent parcel lockers, still need to be assessed at a large scale and from a holistic point of view, beyond purely operational considerations. As a consequence, in this doctoral thesis, we build a complete assessment framework for AV technologies in the context of city logistics, from planning to operations, which combines the continuous approximation methodology and a discrete optimization procedure to efficiently tackle the capacitated multi-trip vehicle routing problem with time windows and battery capacity constraints. More specifically, distinct parsimonious logistics models including the usage of air drones, ground robots and parcel lockers are built considering the characteristics of these different fleets. Moreover, a thorough life-cycle analysis is performed to quantify the environmental impact of these technologies. These different logistics models are then assessed at a large European scale and integrated with discrete optimization methods to give a holistic vision of AV operations for last-mile logistics. This doctoral thesis will help practitioners, researchers and city representatives understand the main challenges at stake with autonomous technologies from a logistics perspective, apart from the social and regulatory aspects, which are not considered. The obtained results are encouraging since the combination of a two-echelon network of delivery micro-hubs with ground autonomous delivery robots has the potential to reduce city logistics costs by around 20% in adequate conditions. Finally, externalities could also be reduced but a very particular focus should be set on the reduction of the greenhouse gas emissions from the autonomous vehicle production, to avoid observing an increase of the global warming potential on a life-cycle basis. (Català) A mesura que la millora de les operacions logístiques d’última milla es converteix en un repte cada cop més gran per les nostres ciutats, tant des d'una perspectiva econòmica com mediambiental, una àmplia mostra de noves tecnologies i esquemes de distribució innovadors estan sent testejats per les empreses logístiques i analitzats dins de la comunitat científica. Més enllà del canvi necessari en les tecnologies de propulsió, dels vehicles amb motor de combustió interna als models elèctrics, calen estratègies de lliurament i tipologies de flotes més eficients. Això és especialment flagrant en el mercat de la missatgeria, enviaments express i paqueteria, que ha augmentat en els últims anys i és l’origen de moltes ineficiències als nostres barris. En aquest context, les xarxes de distribució multinivell apareixen com a possibles solucions. En aquests esquemes, el viatge de les mercaderies es divideix en dues parts diferenciades, primer des del centre de distribució del transportista fins a una xarxa de micro-hubs i després fins al seu destí final. No obstant això, encara que hi hagi una reducció del total de quilòmetres recorreguts i de l’impacte mediambiental, els lliuraments amb bicicletes, que és el model més comú actualment, poden generar més costos d'operació per les empreses que els lliuraments convencionals amb furgonetes perquè els processos logístics són més complexos i els costos d'infraestructura més elevats. A més, les bicicletes cargo tenen una capacitat inferior, la qual cosa afecta la seva eficiència operativa, perquè es necessiten més rutes de lliurament i més vehicles per la mateixa demanda. Perquè els esquemes de lliurament binivell siguin econòmicament sostenibles, el que finalment conduirà a desplegaments a gran escala, els vehicles autònoms, tant terrestres com aeris, semblen solucions prometedores amb el potencial de reduir els costos d’operació de lliurament i, al mateix temps, les emissions de gasos d’efecte hivernacle. Tot i així, els beneficis exactes dels vehicles autònoms, juntament amb altres tecnologies, com ara les taquilles intel·ligents, encara s’han d'avaluar a gran escala i des d’un punt de vista holístic, més enllà de consideracions purament operatives. Com a conseqüència, en aquesta tesi doctoral", "keywords": [ "Greenhouse gas", "Distribution (mathematics)", "Sustainable transport", "Total cost", "Cost reduction", "Total cost of ownership", "City logistics", "Sample (material)" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://doi.org/10.5821/dissertation-2117-454074" }, { "id": "https://openalex.org/W3194440298", "doi": null, "title": "Analysis of Sequential Decision Making Processes Under Exogenous Uncertainty", "year": 2020, "publication_date": "2020-11-17", "type": "dissertation", "language": "en", "authors": [ "Sudarshan Rajan" ], "first_author": "Sudarshan Rajan", "venue": "OakTrust (Texas A&M University Libraries)", "publisher": "Texas A&M University", "abstract": "In this dissertation, we consider sequential optimization decision making problems, which entail making optimal decisions at specified epochs of time to maximize or minimize the specific objective of the decision maker. The system in question evolves in a probabilistic manner through time. This means that the evolution of uncertainty is revealed in a sequential manner as and when the random events occur. The decision maker bases decisions as the uncertainty evolves, up to the point of the decision epoch, and known information about its probabilistic evolution through distributions. The class of problems we focus on in this research is multistage sequential decision making problems where the uncertainty is purely exogenous in nature. \\nWe consider the aforementioned class of research problems and provide an overview of the various methodologies used in solving them. Specifically, we compare the two important existing paradigms which are used to model problems that have the above attributes, namely multistage stochastic programming (MSP) and Markov decision processes (MDP). The need to make this comparison arises from the fact that research communities use these two approaches to solve sequential decision making problems even though, both these approaches have historically evolved from different pedagogical views. The ideas and algorithms used to solve these problems could potentially be useful to both communities working on these problems. As far as we have looked, there have been few attempts in the literature which consider problems that are solved using either methodologies and then compare and contrast the two approaches which would then give an insight into which method might be computationally more efficient to use for a given problem.\\nBy and large, traditionally there is a set of problems which are solved using either methods, and usually they are used to obtain a robust and tractable solution to the problem. It remains to be seen if using an alternate method would provide comparable results and are computationally as/more viable. This research effort intends to bridge this gap in the current literature and make an attempt to draw some inferences into the suitability of using one method versus the other as applied to specific problem instances. \\n\\\\\\\\We describe these approaches and draw an equivalence between these methods while highlighting subtle differences in both these methodologies using a numerical example. We then extend this research to solve a large scale sequential routing problem for Unmanned Aerial Vehicles (UAV) for reconnaissance patrolling missions, formulated as a two stage stochastic integer program with a single recourse action and present the results. We then propose an extension of the above problem to include multiple recourse actions and shall attempt to use standard heuristic techniques such as progressive hedging- a scenario-based decomposition technique well-suited to solving stochastic mixed-integer programs, to formulate and solve this sequential routing UAV problem with multiple recourse actions. In addition we also propose an alternative MDP formulation of the same problem posed as a multi-stage sequential decision making problem. The ultimate goal of this research is to investigate the cross applicability of algorithms used in each of these methodologies to more efficiently solve large scale sequential decision making problems.\\nIn essence this research effort attempts to shed new light into the cross applicability of various standard methodologies into solving the above mentioned class of problems. We expect to contribute to the literature by suggesting ways to solve specific instances of sequential decision making problems using appropriate techniques which are tractable and computationally efficient to derive optimal policies to address these problems.", "keywords": [ "Computer science", "Econometrics", "Economics" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://hdl.handle.net/1969.1/192937" }, { "id": "https://openalex.org/W7110493011", "doi": null, "title": "Enhancing Situational Awareness in Emergency Response: Combining Remote Sensing and Teleoperated Systems", "year": 2025, "publication_date": "2025-06-24", "type": "conference-paper", "language": null, "authors": [ "Halbgewachs, Magdalena Felicitas", "Angermann, Lucas Dominik", "Lechner, Konstanze" ], "first_author": "Halbgewachs, Magdalena Felicitas", "venue": "elib (German Aerospace Center)", "publisher": "Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)", "abstract": "The application of remote sensing and geospatial technologies is becoming increasingly crucial in addressing multifaceted challenges across environmental monitoring, humanitarian aid, and disaster response. With growing demands for rapid, data-driven decision-making in remote and crisis-prone areas, advanced Earth observation capabilities enable more efficient planning, coordination, and resource deployment. The combination of satellite imagery, high-resolution aerial data, and advanced analytics within situational awareness platforms provides novel opportunities for comprehending and responding to dynamic, often unpredictable environments. The development of an advanced web application for a Global Mission Operation Centre (GMOC) is central to both the MaiSHU and RESITEK projects, demonstrating the progressive enhancement of situational awareness tools. Initially, in the MaiSHU project, the web application was used to support teleoperators in navigating amphibious SHERP vehicles in complex and unstructured environments where traditional humanitarian efforts face operational limitations. In June 2024, during a field campaign in Northern Bavaria, Germany, two realistic scenarios highlighted this application: a food delivery mission to a flood-isolated village in South Sudan, supported by the United Nations World Food Programme (WFP), and a flood evacuation exercise in a dangerous environment with the Bavarian Red Cross (BRK), simulating recent flood events in southern Germany. The interactive web application at the GMOC combines multi-layered geospatial data to provide continuous situational awareness, enable high level route planning, and support real-time operations. It integrates and visualizes multi-layered geospatial and remote sensing data, thereby forming a comprehensive situational picture that is essential for mission preparation and execution. This includes simulated flood masks for the exercise region, which facilitated detailed visualizations of prospective flood zones and aided in the identification and planning of accessible routes for the teleoperated vehicle. Time-series of optical satellite imagery provided valuable insights into the region's evolving landscape, enabling the monitoring of environmental changes that could impact mission safety and route stability. The additional incorporation of high-resolution aerial imagery, taken by DLR aircraft, facilitated a more comprehensive understanding of the terrain and infrastructure. The specific characteristics of the local terrain allowed for the implementation of more precise route adjustments. Further, the integration of up-to-date drone imagery, captured before and during the event itself, proved to be a crucial element in the provision of situational updates. The addition of these datasets in the GMOC web application facilitated the creation of three-dimensional terrain models, thereby enhancing the visual and spatial understanding of the environment necessary for detailed route planning. The described data layers enabled mission planners to optimize SHERP vehicle routes based on variables such as terrain slope, surface type, and radio signal coverage. A designated route segment was designed to traverse a river, validating the SHERP’s amphibious capabilities and testing the continuity of operations under varying surface conditions. The pre-planned routes were transmitted to a Local Mission Operation Centre (LMOC), where remote drivers received precise navigation information, guiding the SHERP vehicle along the safest and most efficient paths based on the analysed remote sensing and geospatial data. The web application’s capabilities included real-time GPS monitoring of the SHERP, providing continuous updates on its location, orientation, and previously travelled paths. This tracking data allowed operators to refine navigation strategies and make informed, real-time adjustments. Supplementary real-time geotagged photo uploads from the field augmented the situational overview, while an integrated communication layer ensured continuous connectivity by highlighting areas with signal coverage. Building on the advancements of MaiSHU, the RESITEK project further develops the web application by integrating additional functionalities and additional vehicle types, including ground and aerial units, both manned and unmanned. The platform in RESITEK is undergoing enhancements to serve as an integrative tool for diverse data visualization and situational analysis, thereby supporting continuous monitoring and user-centric planning. This progression is intended to demonstrate comprehensive interoperability in a collaborative exercise involving various stakeholders and realistic emergency scenarios. The project incorporates AI-based image analysis for real-time monitoring and damage detection, highlighting crisis-relevant information in complex 2D and 3D displays to optimize decision-making during disaster response. The joint developments in MaiSHU and RESITEK underline the essential role of geospatial and remote sensing data in crisis management and emergency response. Together, these projects demonstrate the transformative role of Earth observation technologies—including satellite-based crisis information, real-time data updates, and multi-modal situational displays—in enhancing teleoperated and multi-vehicle mission planning, ultimately facilitating more effective and reliable operations in remote and inaccessible areas.", "keywords": [ "Situation awareness", "Geospatial analysis", "Flood myth", "Drone", "Earth observation", "Emergency management", "Teleoperation", "Analytics" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://elib.dlr.de/215007/" }, { "id": "https://openalex.org/W7207882851", "doi": null, "title": "Dynamic network service control and management techniques across multi-provider networks（複数ネットワークを跨ったネットワークサービスの動的制御と管理）", "year": null, "publication_date": null, "type": "dissertation", "language": "en", "authors": [ "Atsushi Taniguchi" ], "first_author": "Atsushi Taniguchi", "venue": "Institutional Repositories DataBase (IRDB)", "publisher": "National Institute of Informatics", "abstract": "Communication networks are now an essential infrastructure of society.Various services are constructed across multi-provider networks, which are usually configured and operated statically.Dynamic Network Service (NS) control and management across multi-provider networks enables resource utilization and operability to be provided more efficiently.This dynamic NS control and management mechanism reduces human work load and significantly improves overall work efficiency.Networks constructed across multi-provider networks are expected to be used in various services (e.g., automated driving, remote drone control, and remote surgery).For such applications, reliability is crucial to eliminate accidental emergencies.However, there is currently no established method for deploying NSs across multi-provider networks dynamically and for assessing their reliability.NS deployment across multi-provider networks should also be defined in global standardizations such as the European Telecommunications Standards Institute (ETSI) Industry Specification Group (ISG) Network Functions Virtualization (NFV).The current documents, however, have significant issues (e.g., overload, security, and location specification) for realistic operation scenarios of multi-provider networks.This dissertation describes four typical use cases featuring multi-provider networks (e.g., basic use case of NS deployment, modification to NS connectivity, network selection from two provider networks, and NS expansion to other provider networks) and operational flows.In this dissertation, I also proposes a scheme for exchanging network information between providers.My evaluation using the current specifications and realistic parameters revealed that an NFV Orchestrator (NFVO) could be overloaded when an NS was deployed in multi-provider networks.This dissertation thoroughly describes the multi-provider NS deployment from several practical aspects unlike the previous academic work.My proposals were adopted as a new feature of the next ETSI NFV specifications by clarifying use cases that can only be achieved with an exchange model.Another critical component to meet service requirements is the selection of an appropriate network, as the network reliability depends on the operation of each network provider.I also proposes a method that enables the lower and upper reliability to be computed in a distributed manner without requiring privacy disclosure.While this problem may seem similar to a traditional reliability evaluation assuming a single-domain network, the existence of multiple domains introduces the following two challenges.The first is the high computation complexity; i.e., network reliability evaluation, is known to be #P-complete, which has prevented reliability evaluations of multi-domain networks.The second is intra-domain privacy; i.e., network providers that never disclose internal data required for reliability evaluation.My i method is solidly based on graph theory and is supported by a simple protocol that secures intra-domain privacy.Experiments on real datasets show that the method can compute the reliability for 14-domain networks in one second.The reliability is bounded with reasonable errors; e.g., bound gaps are less than 0.1%.The privacy issue has not been studied in the long history of network reliability.This will be a key issue in the future of NSs, since multi-provider NSs have been recently discussed in standardization bodies.A feasibility evaluation is required to clarify the requirements for network path control scenarios of NS deployment across multi-provider networks.This dissertation presents a dynamic network path control scenario with dynamic inter-domain and intra-domain network path control using optical switching and control plane technologies.The focus was inter-domain network path creation, Quality of Service (QoS) recovery for protecting high priority traffic over an OIF-based User Network Interface (OIF UNI) using the policy controllers, and a failure recovery of a Label Switched Path (LSP) established over an External Network to Network Interface (ENNI).Routing problems that arose in the multi-domain network were successfully solved and the actual service activation time of inter-domain Ethernet transport services between Japanese and US domains was evaluated.The QoS recovery successfully achieved the migration of high-priority video traffic between Tokyo and Osaka with no errors within around 30 seconds.There was no packet loss for high-priority traffic flow when migrating traffic between a Multi-Protocol Label Switching (MPLS)-LSP and an Optical LSP.The link failure recovery was also confirmed by using a hierarchal LSP over a network testbed without the outflow of any control packets to an outside domain.The measured restoration time of the link failure recovery was 272 ms.This indicates that the proposed traffic control scheme can be applied to NSs that require QoS recovery within minutes with low packet loss rates in multi-provider networks.", "keywords": [ "Software deployment", "Operability", "Service provider", "Service (business)", "Network management", "Key (lock)", "Virtualization", "Virtual network", "Network service", "Network element" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": false, "open_access_url": "https://ir.soken.ac.jp/record/6110/files/A2162%E6%9C%AC%E6%96%87.pdf" }, { "id": "https://openalex.org/W4232645569", "doi": "10.35940/ijrte.10.3", "title": null, "year": 2021, "publication_date": "2021-09-30", "type": "paratext", "language": null, "authors": [], "first_author": null, "venue": "International Journal of Recent Technology and Engineering (IJRTE)", "publisher": null, "abstract": "Sachin B. Kandekar, Swati K. Wakchaure Volume-10 Issue-3, September 2021 Strength of Concrete using Clay as a Partial Replacement of Binder Content with and Without Lime - Garima Bhardwaj, Neelam Bhardwaj Volume-10 Issue-3, September 2021 Can Artificial Intelligence Control Antimicrobial Resistance? - Rajeev Tripathi Volume-10 Issue-3, September 2021 Substantial Content Reclamation for Clustering - Kethavath Prem Kumar, Thirumalaisamy Ragunathan, Devara Vasumathi Volume-10 Issue-3, September 2021 Energy Efficient and VM Consolidation Framework using Improved Spider Monkey Optimization Algorithm - Chingozha Misheck P, Zvokuomba Kwashira Volume-10 Issue-3, September 2021 Infrastructure Vandalism and Protection in a Vulnerable Zimbabwean Environment: Review of the Structural Materialism Theory - Dereje DejeneMengistu, M. Srinivasa Rao, V.V.S.Kesava Rao Volume-10 Issue-3, September 2021 Green Vehicle Routing Under Customer Demand Uncertainty - Soutrik Mukherjee Volume-10 Issue-3, September 2021 Bi-Directional Drone Design and its Path Planning - Soutrik Mukherjee Volume-10 Issue-3, September 2021 Design of Novel Fish De-Scaler System - Pratibha Gayke, Rohini S Kulkarni Volume-10 Issue-3, September 2021 Depression Scale Recognition over Fusion of Visual and Vocal Expression using Artificial Intellectual Method - Md. Aaqeel Hasan, Jaypal Medida, N. Laxmi Prasanna. Volume-10 Issue-3, September 2021 Sliding Window Protocol for Internet of Things - R Ashutosh V Kulkarni, Aravindkumar B Harwalkar Volume-10 Issue-3, September 2021 Analysis and Design of Stadium with Truss System and Shell Roof Subjected to Wind and Seismic Loading - K. Pradeeba, A.Rajasekaran Volume-10 Issue-3, September 2021 Flexural Strengthening of Reinforced Concrete Beams with Externally Bonded Hybrid FRP Laminate - Harry Budiharjo Sulistyarso, Joko Pamungkas, Sri Rahayu G, Indah Widiyaningsih, Kevin Yudha Perwira Volume-10 Issue-3, September 2021 Field Test of “U-Champ” Biosurfactant in Oil Spill Bioremediation - Taketo Kamasaka, Kodai Miyamoto, Makoto Sakamoto Volume-10 Issue-3, September 2021 Real-Time Cloth Simulation for Hula Costume CAD Development - Taketo Kamasaka, Kodai Miyamoto, Takahiro Ishizu, Kenji Aoki, Makoto Sakamoto Volume-10 Issue-3, September 2021 Intuitive Manipulation of Virtual Clay in Augmented Reality - Devesh Banwani, Yatin Kalra Volume-10 Issue-3, September 2021 Maintaining and Evaluating the Integrity of Digital Evidence in Chain of Custody - Muhammad R. Jamal, Khaled S. Al Rasheed Volume-10 Issue-3, September 2021 Vibration Measurement of a Rotating Shaft using Electrostatic Sensor - Kodai Miyamoto, Taketo Kamasaka, Makoto Sakamoto Volume-10 Issue-3, September 2021 Basic Research on the Use of XR Technology to Support Science Education - Joseph Mom, Soo Tyokighir, Gabriel Igwue Volume-10 Issue-3, September 2021 Evaluation of Some Path Reduction Factor Models Performance in Tropical Location - Temesgen Abera Takiso, Birtukan Tekle Manbecho Volume-10 Issue-3, September 2021 Recent Improvements of the PV Solar Energy Generation Performance - Kota Akshith Reddy, Satish C. J, Polsani Jahnavi, Chintapalli Teja Naveen, Gangapatnam Sai Ananya Volume-10 Issue-3, September 2021 Analysis of the Fuzziness of Image Caption Generation Models due to Data Augmentation Techniques - Hirakjyoti Sarma, Dimpal Huzuri, Manoj Kumar Deka Volume-10 Issue-3, September 2021 A Real Time Implementation of an IOT based Vehicle Health Monitoring System - Varun Chopra Volume-10 Issue-3, September 2021 Cyber Intelligence in Smart Vehicles - Rinu R, Manjula S H Volume-10 Issue-3, September 2021 Plant Disease Detection and Classification using CNN - Pranali Yogesh KajaleVolume-10 Issue-3, September 2021 Design Optimization of Composite Lay-up Sequence and Orientation to Achieve Minimum Weight for Racing Seat - Kodai Miyamoto, Taketo Kamasaka, Makoto Sakamoto, Tsunehiro Yoshinaga Volume-10 Issue-3, September 2021 Basic Study on Expressway Congestion Mitigation using Cellular Automata - Anusha T M, Sharath Kumar M D, H S Jagadeesh Volume-10 Issue-3, September 2021 Evaluation of Fatigue Potential for Crumb Rubber Modified Bitumen in Stone Mastic Asphalt Mixes - Gayathri. T. J, E. Muthu Kumar Volume-10 Issue-3, September 2021 Factors Affecting Stress and Job Satisfaction its Impact on Performance of Medical Representatives at Central Kerala Region with Special Reference to Era of Coronavirus (COVID-19) –Pandemic Outbreak - Pradeep J, Aparna S. Bhaskar Volume-10 Issue-3, September 2021 Shoreline Change Detection from Cuddalore to Nagapattinam Coast, Tamil Nadu - Mamadou Diarra, Ayikpa Kacoutchy Jean, Ballo Abou Bakary, Kouassi Brou Médard Volume-10 Issue-3, September 2021 Study of Deep Learning Methods for Fingerprint Recognition - Melchiezedhieck J. Bongao, Arvin F. Almadin, Christian L. Falla, Juan Carlo F. Greganda, Steven Valentino E. Arellano, Phillip Amir M. Esguerra Volume-10 Issue-3, September 2021 SBC-Based Object and Text Recognition Wearable System using Convolutional Neural Network with Deep Learning Algorithm - K. Varalaxmy Nithya, V.Iswariya, K.Harini, K.HemalathaVolume-10 Issue-3, September 2021 Development of Foot Step Power Generation using Piezo Electric Sensors - Lakshmisetty Ruthvik Raj, Bitra Harsha Vardhan, Mullapudi Raghu Vamsi, Keerthikeshwar Reddy Mamilla, Poorna Chandra Vemula Volume-10 Issue-3, September 2021 Data Augmentation using Auxiliary Classifier for Improved Detection of Covid 19 - Ganesh V, Ajey Kumar V G, Aravindan S, Sudha Ravindranath, Vidya A Volume-10 Issue-3, September 2021 Estimation and Analysis of Rainfall Runoff for Urban Hydrology using TR 55 SCS CN and GIS Approach in Hebbal Valley of Bengaluru, South India - Temesgen Abraham Gebreselassie, P. Jagadeeswara Rao Volume-10 Issue-3, September 2021 Geospatial Location Based Study for Establishment of New Public Health Centers: A Case of Adama City, Oromia, Ethiopia - K Pooja, Shailaja. S Volume-10 Issue-3, September 2021 A Relevant SNC Applicatio", "keywords": [ "Computer science" ], "cited_by_count": 0, "is_retracted": false, "is_paratext": true, "open_access_url": "https://doi.org/10.35940/ijrte.10.3" } ]