Towards self-organizing logistics in transportation:a literature review and typology

Deploying self-organizing systems is a way to cope with the logistics sector's complex, dynamic, and stochastic nature. In such systems, automated decision-making and decentralized or distributed control structures are combined. Such control structures reduce the complexity of decision-making, require less computational effort, and are therefore faster, reducing the risk that changes during decision-making render the solution invalid. These benefits of self-organizing systems are of interest to many practitioners involved in solving real-world problems in the logistics sector. This study, therefore, identifies and classifies research related to self-organizing logistics (SOL) with a focus on transportation. SOL is an interdisciplinary study across many domains and relates to other concepts, such as agent-based systems, autonomous control, and decentral systems. Yet, few papers directly identify this as self-organization. Hence, we add to the existing literature by conducting a systematic literature review that provides insight into the field of SOL. The main contribution of this paper is two-fold: (i) based on the findings from the literature review, we identify and synthesize 15 characteristics of SOL in a typology, and (ii) we present a two-dimensional SOL framework alongside the axes of autonomy and cooperativity to position and contrast the broad range of literature, thereby creating order in the field of SOL and revealing promising research directions.</p

Industrial Artificial Intelligence in Industry 4.0 - Systematic Review, Challenges and Outlook

UIDB/00066/2020The advent of the Industry 4.0 initiative has made it so that manufacturing environments are becoming more and more dynamic, connected but also inherently more complex, with additional inter-dependencies, uncertainties and large volumes of data being generated. Recent advances in Industrial Artificial Intelligence have showcased the potential of this technology to assist manufacturers in tackling the challenges associated with this digital transformation of Cyber-Physical Systems, through its data-driven predictive analytics and capacity to assist decision-making in highly complex, non-linear and often multistage environments. However, the industrial adoption of such solutions is still relatively low beyond the experimental pilot stage, as real environments provide unique and difficult challenges for which organizations are still unprepared. The aim of this paper is thus two-fold. First, a systematic review of current Industrial Artificial Intelligence literature is presented, focusing on its application in real manufacturing environments to identify the main enabling technologies and core design principles. Then, a set of key challenges and opportunities to be addressed by future research efforts are formulated along with a conceptual framework to bridge the gap between research in this field and the manufacturing industry, with the goal of promoting industrial adoption through a successful transition towards a digitized and data-driven company-wide culture. This paper is among the first to provide a clear definition and holistic view of Industrial Artificial Intelligence in the Industry 4.0 landscape, identifying and analysing its fundamental building blocks and ongoing trends. Its findings are expected to assist and empower researchers and manufacturers alike to better understand the requirements and steps necessary for a successful transition into Industry 4.0 supported by AI, as well as the challenges that may arise during this process.publishersversionepub_ahead_of_prin

ICSEA 2022: the seventeenth international conference on software engineering advances

The Seventeenth International Conference on Software Engineering Advances (ICSEA 2022), held between October 16th and October 20th, 2022, continued a series of events covering a broad spectrum of software-related topics. The conference covered fundamentals on designing, implementing, testing, validating and maintaining various kinds of software. Several tracks were proposed to treat the topics from theory to practice, in terms of methodologies, design, implementation, testing, use cases, tools, and lessons learned. The conference topics covered classical and advanced methodologies, open source, agile software, as well as software deployment and software economics and education. Other advanced aspects are related to on-time practical aspects, such as run-time vulnerability checking, rejuvenation process, updates partial or temporary feature deprecation, software deployment and configuration, and on-line software updates. These aspects trigger implications related to patenting, licensing, engineering education, new ways for software adoption and improvement, and ultimately, to software knowledge management. There are many advanced applications requiring robust, safe, and secure software: disaster recovery applications, vehicular systems, biomedical-related software, biometrics related software, mission critical software, E-health related software, crisis-situation software. These applications require appropriate software engineering techniques, metrics and formalisms, such as, software reuse, appropriate software quality metrics, composition and integration, consistency checking, model checking, provers and reasoning. The nature of research in software varies slightly with the specific discipline researchers work in, yet there is much common ground and room for a sharing of best practice, frameworks, tools, languages and methodologies. Despite the number of experts we have available, little work is done at the meta level, that is examining how we go about our research, and how this process can be improved. There are questions related to the choice of programming language, IDEs and documentation styles and standard. Reuse can be of great benefit to research projects yet reuse of prior research projects introduces special problems that need to be mitigated. The research environment is a mix of creativity and systematic approach which leads to a creative tension that needs to be managed or at least monitored. Much of the coding in any university is undertaken by research students or young researchers. Issues of skills training, development and quality control can have significant effects on an entire department. In an industrial research setting, the environment is not quite that of industry as a whole, nor does it follow the pattern set by the university. The unique approaches and issues of industrial research may hold lessons for researchers in other domains. We take here the opportunity to warmly thank all the members of the ICSEA 2022 technical program committee, as well as all the reviewers. The creation of such a high-quality conference program would not have been possible without their involvement. We also kindly thank all the authors who dedicated much of their time and effort to contribute to ICSEA 2022. We truly believe that, thanks to all these efforts, the final conference program consisted of top-quality contributions. We also thank the members of the ICSEA 2022 organizing committee for their help in handling the logistics of this event. We hope that ICSEA 2022 was a successful international forum for the exchange of ideas and results between academia and industry and for the promotion of progress in software engineering advances

An Industrial Data Analysis and Supervision Framework for Predictive Manufacturing Systems

Due to the advancements in the Information and Communication Technologies field in the modern interconnected world, the manufacturing industry is becoming a more and more data rich environment, with large volumes of data being generated on a daily basis, thus presenting a new set of opportunities to be explored towards improving the efficiency and quality of production processes. This can be done through the development of the so called Predictive Manufacturing Systems. These systems aim to improve manufacturing processes through a combination of concepts such as Cyber-Physical Production Systems, Machine Learning and real-time Data Analytics in order to predict future states and events in production. This can be used in a wide array of applications, including predictive maintenance policies, improving quality control through the early detection of faults and defects or optimize energy consumption, to name a few. Therefore, the research efforts presented in this document focus on the design and development of a generic framework to guide the implementation of predictive manufacturing systems through a set of common requirements and components. This approach aims to enable manufacturers to extract, analyse, interpret and transform their data into actionable knowledge that can be leveraged into a business advantage. To this end a list of goals, functional and non-functional requirements is defined for these systems based on a thorough literature review and empirical knowledge. Subsequently the Intelligent Data Analysis and Real-Time Supervision (IDARTS) framework is proposed, along with a detailed description of each of its main components. Finally, a pilot implementation is presented for each of this components, followed by the demonstration of the proposed framework in three different scenarios including several use cases in varied real-world industrial areas. In this way the proposed work aims to provide a common foundation for the full realization of Predictive Manufacturing Systems

Real-world Machine Learning Systems: A survey from a Data-Oriented Architecture Perspective

Machine Learning models are being deployed as parts of real-world systems with the upsurge of interest in artificial intelligence. The design, implementation, and maintenance of such systems are challenged by real-world environments that produce larger amounts of heterogeneous data and users requiring increasingly faster responses with efficient resource consumption. These requirements push prevalent software architectures to the limit when deploying ML-based systems. Data-oriented Architecture (DOA) is an emerging concept that equips systems better for integrating ML models. DOA extends current architectures to create data-driven, loosely coupled, decentralised, open systems. Even though papers on deployed ML-based systems do not mention DOA, their authors made design decisions that implicitly follow DOA. The reasons why, how, and the extent to which DOA is adopted in these systems are unclear. Implicit design decisions limit the practitioners' knowledge of DOA to design ML-based systems in the real world. This paper answers these questions by surveying real-world deployments of ML-based systems. The survey shows the design decisions of the systems and the requirements these satisfy. Based on the survey findings, we also formulate practical advice to facilitate the deployment of ML-based systems. Finally, we outline open challenges to deploying DOA-based systems that integrate ML models.Comment: Under revie

Decision Analytics and Decentralized Ledger Technologies for Determination and Preservation of Spare Part Value in Aircraft Maintenance

Aircraft spare parts are used to quickly replace defective parts and ideally avoid expensive aircraft-on-ground situations. Understanding the Fair Market Value of surplus parts is of eminent importance for the competitive advantage of a company. Decisions such as purchase, sale, storage or scrapping are made on the basis of the determined value. Domain experts state that the value of a part depends significantly on its specific characteristics, condition and workshop event history. If the documentation of this history is incomplete, this can lead to a complete loss of value of the part, since, for example, safety-relevant parts may no longer be used without complete documentation. For companies that want to be able to survive in the highly competitive Maintenance Repair and Overhaul market, the use of digital technologies for data-based decision making has become unavoidable. The drowning of data while at the same time thirsting for information affects all market participants who manage their spare parts using digital technologies. The competitive advantage over others is now to use this data efficiently and make decisions based on data rather than experience and instinct. On the other hand, processes still exist in this industry that require documentation in paper form. One such process is the documentation of workshop events for safety-relevant spare parts by means of certificates. Low mutual trust and the heterogeneity of regional requirements in a global market prohibit the establishment of a central instance for data management. The determination of a Fair Market Value was carried out manually for a long time, with great personnel effort and low reliability. The design of an Automated Spare Part Valuation concept provides a basis for data owners to use the amount of data reliably. Similar implementations in industry and with integrated automated evaluation prove the usability. The problem of incomplete certificates of workshop events is addressed and solved by the conception, implementation and evaluation of a Blockchain-based Certification System. The characteristics of a blockchain, in particular its decentralization and persistence, meet the requirements that could not previously be met in an environment with a lack of trust and due to the danger of a single point of failure.Flugzeugersatzteile dienen dem schnellen Austausch von defekten Teilen und vermeiden im Idealfall teure Aircraft-on-Ground-Situationen. Das Verständnis für einen Fair Market Value der überschüssigen Teile ist von eminenter Bedeutung für den Wettbewerbsvorteil eines Unternehmens. Entscheidungen wie Kauf, Verkauf, Einlagerung oder Verschrottung werden auf Basis des ermittelten Werts getroffen. Domänenexperten geben an, dass der Wert eines Teils maßgeblich von seinen spezifischen Charakteristika, seinem Zustand und seiner Werkstattereignishistorie abhängt. Ist der Nachweis dieser Historie lückenhaft, so kann es zum vollständigen Wertverlust des Teils kommen, da etwa sicherheitsrelevante Teile ohne lückenlose Nachweise nicht weiter verwendet werden dürfen. Für Unternehmen, die in der Lage sein wollen im starken Wettbewerb des Maintenance Repair and Overhaul Markts zu bestehen ist der Einsatz digitaler Technologien zur datenbasierten Entscheidungsfindung mittlerweile unumgänglich. Das Ertrinken an Daten bei gleichzeitigem Verdursten an Informationen trifft alle Marktteilnehmer, die ihre Ersatzteile mittels digitaler Technologien verwalten. Der Wettbewerbsvorteil gegenüber anderen besteht nun darin, diese Daten effizient zu nutzen und Entscheidungen weniger nach Erfahrung und Instinkt, sondern datenbasiert zu treffen. Andererseits existieren auch in dieser Branche immer noch Prozesse, die eine Dokumentation in Papierform erfordern. Ein solcher Prozess ist die Dokumentation von Werkstattereignissen für sicherheitsrelevante Ersatzteile durch Zertifikate. Ein geringes Vertrauen untereinander und die Heterogenität regionaler Anforderungen in einem globalen Markt verbieten die Etablierung einer zentralen Instanz zur Verwaltung der Daten. Die Ermittlung eines Fair Market Value erfolgte lange Zeit manuell, unter großem personellen Aufwand und geringer Zuverlässigkeit. Die Konzeption eines Automated Spare Part Valuation Konzepts bildet eine Grundlage für Inhaber von Daten, um die Menge an Daten verlässlich zu nutzen. Ähnliche Umsetzungen in der Industrie und mit integrierter automatisierter Bewertung belegen die Einsatzfähigkeit. Das Problem der lückenhaften Zertifikate von Werkstattereignissen wird durch die Konzeptionierung, Implementierung und Evaluation eines Blockchain-based Certification System adressiert und gelöst. Die Eigenschaften einer Blockchain, insbesondere die Dezentralität und Persistenz, erfüllen die Anforderungen, die in einem Umfeld mit mangelndem Vertrauen und aufgrund der Gefahr eines Single Point of Failure, bisher nicht zu erfüllen waren

Machine learning shows that the Covid-19 pandemic is impacting U.S. public companies unequally by changing risk structures

Covid-19 has impacted the U.S. economy and business organizations in multiple ways, yet its influence on company fundamentals and risk structures have not been fully elucidated. In this paper, we apply LDA, a mainstream topic model, to analyze the risk factor section from SEC filings (10-K and 10-Q), and describe risk structure change over the past two years. The results show that Covid-19 has transformed the risk structures U.S. companies face in the short run, exerting excessive stress on international interactions, operations, and supply chains. However, this shock has been waning since the second quarter of 2020. Our model shows that risk structure change (measured by topic distribution) from Covid-19 is a significant predictor of lower performance, but smaller companies tend to be stricken harder

Trusted Artificial Intelligence in Manufacturing; Trusted Artificial Intelligence in Manufacturing

The successful deployment of AI solutions in manufacturing environments hinges on their security, safety and reliability which becomes more challenging in settings where multiple AI systems (e.g., industrial robots, robotic cells, Deep Neural Networks (DNNs)) interact as atomic systems and with humans. To guarantee the safe and reliable operation of AI systems in the shopfloor, there is a need to address many challenges in the scope of complex, heterogeneous, dynamic and unpredictable environments. Specifically, data reliability, human machine interaction, security, transparency and explainability challenges need to be addressed at the same time. Recent advances in AI research (e.g., in deep neural networks security and explainable AI (XAI) systems), coupled with novel research outcomes in the formal specification and verification of AI systems provide a sound basis for safe and reliable AI deployments in production lines. Moreover, the legal and regulatory dimension of safe and reliable AI solutions in production lines must be considered as well. To address some of the above listed challenges, fifteen European Organizations collaborate in the scope of the STAR project, a research initiative funded by the European Commission in the scope of its H2020 program (Grant Agreement Number: 956573). STAR researches, develops, and validates novel technologies that enable AI systems to acquire knowledge in order to take timely and safe decisions in dynamic and unpredictable environments. Moreover, the project researches and delivers approaches that enable AI systems to confront sophisticated adversaries and to remain robust against security attacks. This book is co-authored by the STAR consortium members and provides a review of technologies, techniques and systems for trusted, ethical, and secure AI in manufacturing. The different chapters of the book cover systems and technologies for industrial data reliability, responsible and transparent artificial intelligence systems, human centered manufacturing systems such as human-centred digital twins, cyber-defence in AI systems, simulated reality systems, human robot collaboration systems, as well as automated mobile robots for manufacturing environments. A variety of cutting-edge AI technologies are employed by these systems including deep neural networks, reinforcement learning systems, and explainable artificial intelligence systems. Furthermore, relevant standards and applicable regulations are discussed. Beyond reviewing state of the art standards and technologies, the book illustrates how the STAR research goes beyond the state of the art, towards enabling and showcasing human-centred technologies in production lines. Emphasis is put on dynamic human in the loop scenarios, where ethical, transparent, and trusted AI systems co-exist with human workers. The book is made available as an open access publication, which could make it broadly and freely available to the AI and smart manufacturing communities

Challenges and Opportunities in Applied System Innovation

This book introduces and provides solutions to a variety of problems faced by society, companies and individuals in a quickly changing and technology-dependent world. The wide acceptance of artificial intelligence, the upcoming fourth industrial revolution and newly designed 6G technologies are seen as the main enablers and game changers in this environment. The book considers these issues not only from a technological viewpoint but also on how society, labor and the economy are affected, leading to a circular economy that affects the way people design, function and deploy complex systems