Operational research IO 2021—analytics for a better world. XXI Congress of APDIO, Figueira da Foz, Portugal, November 7–8, 2021

This book provides the current status of research on the application of OR methods to solve emerging and relevant operations management problems. Each chapter is a selected contribution of the IO2021 - XXI Congress of APDIO, the Portuguese Association of Operational Research, held in Figueira da Foz from 7 to 8 November 2021. Under the theme of analytics for a better world, the book presents interesting results and applications of OR cutting-edge methods and techniques to various real-world problems. Of particular importance are works applying nonlinear, multi-objective optimization, hybrid heuristics, multicriteria decision analysis, data envelopment analysis, simulation, clustering techniques and decision support systems, in different areas such as supply chain management, production planning and scheduling, logistics, energy, telecommunications, finance and health. All chapters were carefully reviewed by the members of the scientific program committee.info:eu-repo/semantics/publishedVersio

Mission programming for flying ensembles: combining planning with self-organization

The application of autonomous mobile robots can improve many situations of our daily lives. Robots can enhance working conditions, provide innovative techniques for different research disciplines, and support rescue forces in an emergency. In particular, flying robots have already shown their potential in many use-cases when cooperating in ensembles. Exploiting this potential requires sophisticated measures for the goal-oriented, application-specific programming of flying ensembles and the coordinated execution of so defined programs. Because different goals require different robots providing different capabilities, several software approaches emerged recently that focus on specifically designed robots. These approaches often incorporate autonomous planning, scheduling, optimization, and reasoning attributable to classic artificial intelligence. This allows for the goal-oriented instruction of ensembles, but also leads to inefficiencies if ensembles grow large or face uncertainty in the environment. By leaving the detailed planning of executions to individuals and foregoing optimality and goal-orientation, the selforganization paradigm can compensate for these drawbacks by scalability and robustness. In this thesis, we combine the advantageous properties of autonomous planning with that of self-organization in an approach to Mission Programming for Flying Ensembles. Furthermore, we overcome the current way of thinking about how mobile robots should be designed. Rather than assuming fixed-design robots, we assume that robots are modifiable in terms of their hardware at run-time. While using such robots enables their application in many different use cases, it also requires new software approaches for dealing with this flexible design. The contributions of this thesis thus are threefold. First, we provide a layered reference architecture for physically reconfigurable robot ensembles. Second, we provide a solution for programming missions for ensembles consisting of such robots in a goal-oriented fashion that provides measures for instructing individual robots or entire ensembles as desired in the specific use case. Third, we provide multiple self-organization mechanisms to deal with the system’s flexible design while executing such missions. Combining different self-organization mechanisms ensures that ensembles satisfy the static requirements of missions. We provide additional self-organization mechanisms for coordinating the execution in ensembles ensuring they meet the dynamic requirements of a mission. Furthermore, we provide a solution for integrating goal-oriented swarm behavior into missions using a general pattern we have identified for trajectory-modification-based swarm behavior. Using that pattern, we can modify, quantify, and further process the emergent effect of varying swarm behavior in a mission by changing only the parameters of its implementation. We evaluate results theoretically and practically in different case studies by deploying our techniques to simulated and real hardware.Der Einsatz von autonomen mobilen Robotern kann viele Abläufe unseres täglichen Lebens erleichtern. Ihr Einsatz kann Arbeitsbedingungen verbessern, als innovative Technik für verschiedene Forschungsdisziplinen dienen oder Rettungskräfte im Einsatz unterstützen. Insbesondere Flugroboter haben ihr Potenzial bereits in vielerlei Anwendungsfällen gezeigt, gerade wenn mehrere in Ensembles eingesetzt werden. Das Potenzial fliegender Ensembles zielgerichtet und anwendungsspezifisch auszuschöpfen erfordert ausgefeilte Programmiermethoden und Koordinierungsverfahren. Zu diesem Zweck sind zuletzt viele unterschiedliche und auf speziell entwickelte Roboter zugeschnittene Softwareansätze entstanden. Diese verwenden oft klassische Planungs-, Scheduling-, Optimierungs- und Reasoningverfahren. Während dies vor allem den zielgerichteten Einsatz von Ensembles ermöglicht, ist es jedoch auch oft ineffizient, wenn die Ensembles größer oder deren Einsatzumgebungen unsicher werden. Die genannten Nachteile können durch das Paradigma der Selbstorganisation kompensiert werden: Falls Anwendungen nicht zwangsläufig auf Optimalität und strikte Zielorientierung ausgelegt sind, kann so Skalierbarkeit und Robustheit im System erreicht werden. In dieser Arbeit werden die vorteilhaften Eigenschaften klassischer Planungstechniken mit denen der Selbstorganisation in einem Ansatz zur Missionsprogrammierung für fliegende Ensembles kombiniert. In der dafür entwickelten Lösung wird von der aktuell etablierten Ansicht einer unveränderlichen Roboterkonstruktion abgewichen. Stattdessen wird die Hardwarezusammenstellung der Roboter als zur Laufzeit modifizierbar angesehen. Der Einsatz solcher Roboter erfordert neue Softwareansätze um mit genannter Flexibilität umgehen zu können. Die hier vorgestellten Beiträge zu diesem Thema lassen sich in drei Punkten zusammenfassen: Erstens wird eine Schichtenarchitektur als Referenz für physikalisch konfigurierbare Roboterensembles vorgestellt. Zweitens wird eine Lösung zur zielorientierten Missions-Programmierung für derartige Ensembles präsentiert, mit der sowohl einzelne Roboter als auch ganze Ensembles instruiert werden können. Drittens werden mehrere Selbstorganisationsmechanismen vorgestellt, die die autonome Ausführung so erstellter Missionen ermöglichen. Durch die Kombination verschiedener Selbstorganisationsmechanismen wird sichergestellt, dass Ensembles die missionsspezifischen Anforderungen erfüllen. Zusätzliche Selbstorganisationsmechanismen ermöglichen die koordinierte Ausführung der Missionen durch die Ensembles. Darüber hinaus bietet diese Lösung die Möglichkeit der Integration zielorientierten Schwarmverhaltens. Durch ein allgemeines algorithmisches Verfahren für auf Trajektorien-Modifikation basierendes Schwarmverhalten können allein durch die Änderung des Parametersatzes unterschiedliche emergente Effekte in einer Mission erzielt, quantifiziert und weiterverarbeitet werden. Zur theoretischen und praktischen Evaluierung der Ergebnisse dieser Arbeit wurden die vorgestellten Techniken in verschiedenen Fallstudien auf simulierter sowie realer Hardware zum Einsatz gebracht

Geo-Information Technology and Its Applications

Geo-information technology has been playing an ever more important role in environmental monitoring, land resource quantification and mapping, geo-disaster damage and risk assessment, urban planning and smart city development. This book focuses on the fundamental and applied research in these domains, aiming to promote exchanges and communications, share the research outcomes of scientists worldwide and to put these achievements better social use. This Special Issue collects fourteen high-quality research papers and is expected to provide a useful reference and technical support for graduate students, scientists, civil engineers and experts of governments to valorize scientific research

Multi-Robot Systems: Challenges, Trends and Applications

This book is a printed edition of the Special Issue entitled “Multi-Robot Systems: Challenges, Trends, and Applications” that was published in Applied Sciences. This Special Issue collected seventeen high-quality papers that discuss the main challenges of multi-robot systems, present the trends to address these issues, and report various relevant applications. Some of the topics addressed by these papers are robot swarms, mission planning, robot teaming, machine learning, immersive technologies, search and rescue, and social robotics

探索ダイナミクス分析とその進化的アルゴリズムヘの応用

富山大学・富理工博甲第181号・李小司・2020/9/28富山大学202

Artificial intelligence empowered virtual network function deployment and service function chaining for next-generation networks

The entire Internet of Things (IoT) ecosystem is directing towards a high volume of diverse applications. From smart healthcare to smart cities, every ubiquitous digital sector provisions automation for an immersive experience. Augmented/Virtual reality, remote surgery, and autonomous driving expect high data rates and ultra-low latency. The Network Function Virtualization (NFV) based IoT infrastructure of decoupling software services from proprietary devices has been extremely popular due to cutting back significant deployment and maintenance expenditure in the telecommunication industry. Another substantially highlighted technological trend for delaysensitive IoT applications has emerged as multi-access edge computing (MEC). MEC brings NFV to the network edge (in closer proximity to users) for faster computation. Among the massive pool of IoT services in NFV context, the urgency for efficient edge service orchestration is constantly growing. The emerging challenges are addressed as collaborative optimization of resource utilities and ensuring Quality-ofService (QoS) with prompt orchestration in dynamic, congested, and resource-hungry IoT networks. Traditional mathematical programming models are NP-hard, hence inappropriate for time-sensitive IoT environments. In this thesis, we promote the need to go beyond the realms and leverage artificial intelligence (AI) based decision-makers for “smart” service management. We offer different methods of integrating supervised and reinforcement learning techniques to support future-generation wireless network optimization problems. Due to the combinatorial explosion of some service orchestration problems, supervised learning is more superior to reinforcement learning performance-wise. Unfortunately, open access and standardized datasets for this research area are still in their infancy. Thus, we utilize the optimal results retrieved by Integer Linear Programming (ILP) for building labeled datasets to train supervised models (e.g., artificial neural networks, convolutional neural networks). Furthermore, we find that ensemble models are better than complex single networks for control layer intelligent service orchestration. Contrarily, we employ Deep Q-learning (DQL) for heavily constrained service function chaining optimization. We carefully address key performance indicators (e.g., optimality gap, service time, relocation and communication costs, resource utilization, scalability intelligence) to evaluate the viability of prospective orchestration schemes. We envision that AI-enabled network management can be regarded as a pioneering tread to scale down massive IoT resource fabrication costs, upgrade profit margin for providers, and sustain QoS mutuall