As manufacturing evolves towards high-quality and high-output production, the demand for intelligent, defect-free systems becomes increasingly critical. Zero Defect Manufacturing (ZDM) emerges as a key paradigm, relying on real-time data and predictive analytics to proactively detect and mitigate quality deviations. In this work, a AI-based diagnosis system is proposed as part of an integrated approach to transform quality monitoring from a reactive to a proactive strategy by enabling the early detection of potential defects. This approach considers representing algorithms for predicting product and measurement quality based on live shop floor data, and optimization algorithms for fine-tuning the regression models aiming to enhance the prediction performance. The proposed approach was applied to an automotive assembly line, and the preliminary results shown benefits in improving the results of the diagnosis process.

In the aircraft engine Maintenance, Repair, and Overhaul (MRO) process, effective task planning relies heavily on the expertise of lead engineers. However, when predictive models are used to assist decision-making, issues with incomplete, unbalanced, and inconsistent data can lead to errors in the planning task. Therefore, reliable predictions are crucial for optimising the operational efficiency. This paper proposes a methodology to improve the prediction of maintenance times for an aircraft engine MRO process by integrating K-means clustering with Cosine and Jaccard similarity methods to define a reliable prediction interval. This methodology was compared with the predictions of the Simple Linear Regression method, which resulted in the prediction interval approach significantly reducing prediction errors, increasing prediction accuracy, while optimising process management and maintenance task planning throughout the MRO process.

High-dimensional variability in manufacturing processes presents significant challenges for quality control, demanding predictive strategies capable of capturing complex parameter dependencies. Machine learning (ML) offers robust mechanisms for this purpose, but reliance on black-box models often limits interpretability and hinders producing stakeholders’ identification of meaningful correlations for model optimization. This paper introduces an interactive what-if simulation platform designed to explore structural quality correlations in automotive assembly through explainable ML techniques, enhancing transparency and enabling uncertainty quantification. The platform is based on a modular Digital Twin (DT) architecture aligned with the ISO 23247 standard, guiding expert and non-expert users through correlation-driven feature selection, regression modelling and SHapley Additive exPlanations (SHAP) based post-hoc explanations. A case study using real inspection data from a vehicle assembly line demonstrates the tool’s capacity to support variable relevance assessment, dimensionality reduction, and model interpretability. Furthermore, an uncertainty-aware SHAP analysis enhances confidence in the model’s prediction stability, reinforcing the platform’s suitability for quality-driven decision support and integration into future DT ecosystems.

STEM (Science, Technology, Engineering and Math-ematics) education is crucial, rising the demand for technological skills driven by the Industry 4.0 digital transformation, and fostering problem-solving, autonomy, computational thinking, creativity, innovation, and effective teamwork skills. To properly address the lack of structured materials in secondary schools, an Educational Design Research (EDR) project was developed to introduce young students to STEM, and particularly to electronics field. For this purpose, the developed approach combines instructionism and constructionism methods, through three educational projects and “Drops of knowledge” modules. The developed material was applied to 9th and 12th grades of a secondary school, with the achieved results showing that this immersive approach effectively provides students with the necessary competencies to tackle the initial technological challenges of electronics. The project was recognized by the Directorate General of Education of Portugal as a recommended practice of a digital initiative within secondary schools.

Cyber-physical systems (CPS) rapidly expand within industrial contexts in a new era of digitalization, processing power, and inter-device communication capabilities. These advancements integrate technologies such as the Internet of Things (IoT), artificial intelligence (AI), and cloud and edge computing, granting processes and operations a high degree of autonomy. In addition, these interconnections foster collective intelligence arising from information exchange and collaboration between components, often outperforming individual capabilities. This collective intelligence manifests in fault detection and diagnosis (FDD) tasks within CPS, as it significantly improves the flexibility, performance, and scalability. However, the inherent complexity of CPS poses challenges in determining the best configuration of the collaboration parameters, such as when and how to collaborate, wherein incorrect adjustments may lead to decision errors and compromise the system's performance. With this in mind, this paper proposes seven metrics to evaluate collaboration performance for fault detection and diagnosis in multi-agent systems (MAS)-based CPS, evaluating when the collaboration is beneficial or when the collaboration parameters need to be adjusted. The experiments focus on collaborative fault detection in temperature and humidity sensors within warehouse racks, where the proposed evaluation metrics point out the impact of collaboration on the detection task, as well as possible actions to be adopted to improve the agent's performance.

The emergence of Digital Twins (DT) in Industry 4.0 has enabled the decision support systems taking advantage of more effective recommendation systems (RS). Despite the RS's growing popularity and ability to support decision-makers, these face two significant challenges, cold-start and data sparsity, which limits the system's capability to provide effective and accurate decision support. This paper aims to address these issues by conducting a literature review, analysing the current research landscape, and identifying the main enabling methods, algorithms, and similarity measures to mitigate these challenges. The performed analysis enables the point out of future research directions for developing effective and accurate RS that empower decision-makers.

The significant growth in modern communication technologies has led to an increase in zero-day vulnerabilities that degrade the performance ofindustrialcyber-physical systems (ICPS). Distributed denial of service (DDoS) attacks are one such threat that overwhelms a target with floods of packets, posing a severe risk to the normal operations of the ICPS. Current solutions to detect DDoS attacks are unsuitable for resource-constrained ICPS. This study proposes actively detecting multiscale flooding DDoS attacks in resource-constrained ICPS by analyzing network traffic in the frequency domain. A two-phased technique detects attack presence and attack volume. Both phases use a novel combination of light-weight and theoretically sound statistical methods. The effectiveness of the proposed technique is evaluated using mainstream metrics like true and false positive rates, accuracy, and precision using BOUN DDoS 2020 and CICDDoS 2019 datasets. An implementation of the proposed approach on a programmable logic controllers-based ICPS demonstrated improvements in resource usage and detection time compared to the existing state-of-the-art.

About a decade after the introduction of Industry 4.0 (I4.0) as a paradigm oriented towards the digitization of industrial environments, centered on the concept of industrial Cyber-physical Systems (CPS) to enable the development of intelligent and distributed industrial systems, many companies around the world are still not immersed in this digital transformation era. This transition is not straightforward and requires the aligned with the novel technologies, architectures and standards to migrate entire traditional systems into I4.0 systems. In this context, this paper presents an approach to perform the digitization of non-I4.0 components/systems into I4.0 through an approach based on the Asset Administration Shell (AAS), which is a standardized digital representation of an asset. This approach enables to hold the asset information throughout its lifecycle, provides a standard communication interface with the asset, and is based on a set of modules that are combined with the AAS to provide novel functionalities for the asset, e.g., monitoring, diagnosis and optimization. Moreover, this approach adopts Multi-agent Systems (MAS) to provide mainly autonomy and collaborative capabilities to the system. The agents are able to get information from the AASs, making intelligent decisions and perform distributed tasks following interaction strategies, e.g., collaboration, negotiation and self-organization. The feasibility of the proposed approach was tested by digitizing a small-scale production system comprising several assets.

Industry 4.0 is re-shaping the way companies and individuals operate, but it is also introducing strong demands in education processes to train professionals with adequate competencies in emergent digital technologies, e.g., Internet of Things (IoT), Artificial Intelligence and collaborative robotics. In the last decade, innovative educational methods are being applied, e.g., problem-based learning and project-based learning, to move the traditional education approach into a more student-centric process where the student has a more active role. Recent studies point out that the combination of such educational methods is beneficial, each one selected according to the particularities of the learning subject and objective. Having this in mind, this paper describes the application of a learning methodology that combines different educational methods, namely face-to-face, problem-based learning and project-based learning, in a teaching course unit focusing on IoT technologies. The achieved results show an increase of the student’s assessment performance, motivation and satisfaction, and the opportunity to consolidate their acquired knowledge with hands-on practice. This approach also stimulates the acquisition of soft skills, mainly teamwork, communication, creativity and critical thinking.

The research, training, and learning in robotic systems is a difficult task for institutions that do not have an appropriate equipment infrastructure, mainly due to the high investment required to acquire these systems. Possible alternatives are the use of robotic simulation platforms and the creation of remote robotic environments available for different users. The use of the last option surpasses the former one in terms of the possibility to handle real robotic systems during the training process. However, technical challenges appear in the management of the supporting infrastructure to use the robotic systems, namely in terms of access, safety, security, communication, and programming aspects. Having this in mind, this paper presents an approach for the remote operation of real robotic manipulators under a virtual robotics laboratory. To this end, an open access and safe web-based platform was developed for the remote control of robotic manipulators, being validated through the remote control of a real UR3 manipulator. This platform contributes to the research and training in robotic systems among different research centers and educational institutions that have limited access to these technologies. Furthermore, students and researchers can use this educational tool that differs from traditional robotic simulators through a virtual experience that connects real manipulators worldwide through the Internet.

Indoor localization systems enable object tracking, e.g., related to retail, logistics and mobile robotics during their life cycle. This paper describes a real-world scenario implementation, based on Bluetooth Low Energy (BLE) beacons, evaluating a hybrid indoor positioning system (H-IPS) that combines two RSSI-based approaches: Multilateration (MLT) and Fingerprinting (FP). In addition, a Kalman Filter (KF) was employed to decrease the positioning errors of both techniques. Furthermore a track-to-track fusion (TTF) is performed on the two KF outputs to maximize the performance. The results show that the accuracy of H-IPS overcomes the standalone FP in 21%, while the original MLT is outperformed in 52%. Finally, the proposed solution demonstrated a probability of error <2m of 80%, while the same probability for the FP and MLT were 56% and 20%, respectively.

The Internet of Things (IoT) is one of the main foundations of Industry 4.0, providing widespread connectivity of systems and devices, which promotes significant benefits, such as improved performance, responsiveness, and reconfigurability. However, it also brings some security problems, which make these devices and systems vulnerable to cyberattacks, consequently demanding efficient learning and training initiatives to address the challenges regarding the qualification of undergraduate students and active professionals to design more secure systems, as well as to be more aware of cyberthreats during the management and use of them. With this in mind, this paper describes a Capture the Flag competition based on IoT cybersecurity. The participants’ feedback and performance evaluation show that this type of hands-on competition strongly contributes to learning the importance of cybersecurity in IoT-based applications.

Cyber-Physical Systems (CPS) are devoted to be the main participants in Industry 4.0 (I4.0) solutions. In recent years, many authors have focused their efforts on making proposals for the design and implementation of CPS based on different digital technologies. However, the comparative evaluation of these I4.0 solutions is complex, since there is no uniform criterion when it comes to defining the test scenarios and the metrics to assess them. This paper presents a technology-independent CPS demonstrator for benchmarking I4.0 solutions. To that end, a set of testing scenarios, Key Performance Indicators and services were defined considering the available automation cells setup. The proposed demonstrator has been used to test an I4.0 solution based on a Multi-agent Systems (MAS) approach.

Denial of Service is a significant availability threat in Industrial Cyber-Physical systems and smart manufacturing is not an exception. The types, methods, and duration of these attacks have been evolving rapidly and their number has increased dramatically, reaching a new record in history. In particular, digitisation of the manufacturing process and increased connectivity have created a battleground between product quality of service and threats associated with cross-domains and multi-vector attacks that affect the manufacturing system performance. The existing research on cyber-threats related to smart manufacturing system does not consider the comprehensive landscape of denial of service attacks. In this study, we classify well-accepted (distributed) denial of service attacks according to a proposed taxonomy, focusing on both the multi-vector attacks and cross-domain attacks. Utilising the taxonomy, more than fifty different denial of service attacks on smart manufacturing system were classified in terms of Endpoint and Network (distributed) denial of service attacks. As an example, a Cyber-Physical Conveyor System was used to examine the proposed taxonomy.

In the digital transformation era, the collection of data assumes a crucial relevance. In some applications, the use of real sensors to measure the target parameters is constrained by technical or economical limitations. In such situations, it is required to use alternative techniques based on soft sensors that acquire data by estimating the measurement of a variable through the correlation of the data acquired by the neighbouring sensors. However, the co-existence of real and soft sensors requires a computational infra-structure that integrates these heterogeneous data sources and supports the synchronisation of the monitoring system based on the inputs of different measurement nodes. Multi-agent systems provide this distributed infra-structure for the data collection, ensuring modularity, scalability and reconfigurability capabilities. This paper introduces a multi-agent system approach to create a modular and scalable sensing system, based on a diversity of real and soft sensors, to support the monitoring of temperature in thin-film sensing surfaces. The proposed approach was experimentally tested in a plastic injection process, presenting promising results in terms of accuracy and response time, and allowing to obtain more sampling points through the use of computational techniques to complement the real data.

The digital transformation that the world is facing has a strong impact in the professional occupations and job profiles in the factories of the future context, requiring the need of upskilling and re-qualification of the workforce. Taking this into account, an Industrial Collaborative Educational Design (ICoED) is presented comprising three stages and eight steps, and considering a democratic and collaborative participation of the different stakeholders, namely the managers, educators and learners, each one providing its own perspective on the design of the training programme. In this co-design process, the analysis of the skills’ gap is a crucial task to prepare the initial stage of the process, particularly identifying the needs in terms of soft and hard skills. The proposed ICoED process was applied to solve an upskilling problem of an industrial metal stamping company, with the participants performing three workshops to execute the eight steps, reaching a training programme with five modules, each one settled with proper activities, resources and infrastructures.

Industry 4.0 (I4.0) is promoting the digitization of traditional manufacturing systems towards flexible, reconfigurable and intelligent factories based on Cyber-Physical Systems (CPS). In this context, the Reference Architecture Model Industrie 4.0 (RAMI4.0) provides guidelines to develop I4.0 compliant solutions based on industrial standards. As the main RAMI4.0 specification, the Asset Administration Shell (AAS) is a standard digital representation of an industrial asset that plays a pivotal role in enabling interoperable communication among I4.0 components across the value chain. This paper provides an analysis of the current state-of-the-art of implementing AAS, discussing, amongst others, the key enabling technologies used to implement the AAS and the alignment of the research works found in the literature with the I4.0 components criteria.

The I4.0 Component is a key concept of the Platform Industrie 4.0 initiative. The I4.0 Component is made up of an asset performing services and an Asset Administration Shell (AAS) representing the asset in the system. I4.0 Components must meet the requirements of interoperability, identification, representation, information management, integration, and asset management. Industrial agents are a suitable approach to implement I4.0 Components since they meet these requirements and provide additional features such as distributed decision-making capabilities. Nevertheless, the development of generic approaches for the integration of AASs and assets is not straightforward due to the diversity of physical assets in the factories. This paper proposes a four-layered architecture for the implementation of I4.0 Components based on industrial agents, two in the agent-based AAS and the other two in the physical asset. The division into layers enhances the separation of concerns, encapsulating the different integration aspects at different abstraction levels. This approach is complementary to the standard IEEE 2660.1-2020 on recommended practices for industrial agents, which focuses on the deployment of the interface between the agent and the physical asset. As a proof of concept, the proposed architecture will be applied for the integration of a software agent and a robot from an assembly cell, aiming to create an I4.0 Component.

The research about the digital twin concept is growing worldwide, especially in the industrial sector, due to the increasing digitisation level associated to Industry 4.0. The application of the digital twin concept improves performance of a system by implementing monitoring, diagnosis, optimisation, and decision support actions. In particular, the decision-making process is very time consuming since the decision-maker is presented with hundreds of different scenarios that can be simulated and assessed in a what-if perspective. Bearing this in mind, this paper proposes to integrate a digital twin-based what-if simulation with a recommendation system to improve the decision-making cycle. The recommendation system is based on a reinforcement learning technique and takes user knowledge of the system into consideration and trust in the system recommendation. The applicability of the proposed approach is presented in an assembly line case study for recommending the best configurations for the system operation, in terms of the optimal number of AGVs (Autonomous Guided Vehicles) in various scenarios. The achieved results show its successful application and highlight the benefits of using AI-based recommendation systems for what-if simulation in digital twin systems.

The provision of home health care services is becoming an important research area, mainly because in Portugal the population is ageing and it is necessary to perform home care services. Home care visits are organized taking into account the medical treatments and general support that elder/sick people need at home. This health service can be provided by nurses teams from Health Units, requiring some logistics for this purpose. Usually, the visits are manually planned and without computational support. The main goal of this work is to carry out the automatic nurse’s allocation of home care visits, of one Bragança Health Unit, in order to minimize the nurse’s workload balancing, spent time in all home care visits and, consequently, reduce the costs involved. The developed methodology was coded in MatLab Software and the problems were efficiently solved by the particle swarm optimization method. The nurse’s allocation solution of home care visits for the presented case study shows a significant improvement and reduction in the maximum time, in the nurse workload balancing, as well as the patients waiting time.

Modern industry demands techniques that ensure the operability of its processes, and even though the exponential technological advance in the last two decades in the manufacturing field, failures, waste, and unexpected interruptions are still present in this sector's daily routine. Within the Industry 4.0 context, fault-tolerant (FT) production systems remain a complex issue and sometimes represent a vulnerable aspect. Fault-tolerance techniques dedicated to autonomous and distributed systems, in a cyber-physical system (CPS) perspective, need to be investigated to follow the evolutionary pace of the manufacturing scenarios. This paper overviews these concepts and analyses the current situation in developing FT for CPS systems through a systematic literature review. The paper also discusses the research challenges in this new kind of FT systems due to new distributed architectures and emerging technologies, matching the several fault- tolerance phases.

The 4th industrial revolution advent promotes the reorganization of the traditional hierarchical automation systems towards decentralized Cyber-Physical Systems (CPS). In this context, Artificial Intelligence (AI) can address the new requirements through the use of data-driven and distributed problem solving approaches, such those based on Machine-Learning and Multi-agent Systems. Although their promising perspectives to enable and manage intelligent Internet of Things environments, the traditional Cloud-based AI approaches are not suitable to handle many industrial scenarios, constrained by responsiveness and data sensitive.

In logistics of home health care services in the Health Units, the managers and nurses need to carry out the schedule and the vehicles routes for the provision of care at the patients' homes. Currently, in Portugal, these services are increasingly used but the problem is still, usually, solved manually and without computational resources. The increased demand for home health care due to the boost of the elderly people number entails a high associated cost which, sometimes, does not guarantee the quality of the service. In this sense, the periodic vehicle routing problem is a generalization of the classical vehicle routing problem in which routes are determined for a time horizon of several days. In this work, it is provided a periodic vehicle routing problem applied in the Health Unit in Bragança. An integer linear programming formulation for the real database, allowed to solve the problem in an efficient and optimized way using the CPLEXR software.

Due to the increasing of life expectancy in the developed countries, the demand for home health care services is growing dramatically. Usually, home services are planned manually and lead to various optimization problems in their activities. In this sense, health units are confronted with appropriate scheduling which may contain multiple, often conflicting, objectives such as minimizing the costs related to the traveling distance while minimizing the traveling time. In order to analyze and discuss different trade-offs between these objectives, it is proposed a multi-objective approach to home health care scheduling in which the problem is solved using the Tchebycheff method and a Genetic algorithm. Different alternative solutions are presented to the decision maker that taking into account his/her preferences chooses the appropriate solution. A problem with real data from a home health care service is solved. The results highlight the importance of a multi-objective approach to optimize and support decision making in home health care services. Moreover, this approach provides efficient and good solutions in a reasonable time.

Industries are becoming more and more digitized to better implement intelligent and predictive maintenance support systems, aligned with Industry 4.0, which requires the progressive digitization of data collection and processes. Maintenance interventions, in an evolving technological context, are increasingly more complex and difficult for technicians to perform. In these environments, the use of Augmented Reality (AR) to help assist and guide in the maintenance operations, can accomplish a considerable gain in productivity. AR allows to superimpose information objects in real scenes, such as text, images, audiovisuals, and 2D/3D model animations, making available contextual information about the process, based on location and perspective. This paper describes the design and implementation of a prototype augmented reality application to support maintenance tasks inside a metal stamping production unit, that produces components for the automotive sector. It aims to train and guide personnel during the maintenance operations, and offering an extra channel to reach expert help.

Internet of Things (IoT) envisages a reality in which people and objects are interconnected in such a way that a series of services, previously unthinkable, become real. The impact of IoT technologies is already tangible in industry, particularly under the Industry 4.0 initiative, but yet far to be fully exploited in other areas, such as building automation. This paper highlights the importance of using IoT and other emergent technologies to develop building automation applications that serves as base in smart cities, particularly supporting the interoperability among home automation solutions provided by different manufacturers. For this purpose, a low cost IoT enabler solution for building automation is presented, based on the use of cyber-physical systems, as backbone to integrate different IoT technologies and building automation technologies. The proposed approach was successfully implemented in an open space laboratory.

The increase volume of vehicles circulating in large cities and the limited space for parking are factors that motivate the adoption of systems capable of dealing with such problems. In this context, smart parking systems are suitable solutions to avoid the traffic congestion, the air pollution and the long search to find a free parking spot. The inclusion of emergent ICT technologies and artificial intelligence techniques, and particularly using multi-agent systems, combined under the scope of Cyber-Physical Systems (CPS), ensure flexibility, modularity, adaptability and the decentralization of intelligence through autonomous, cooperative and proactive entities. Such smart parking systems can be easily adapted to any type of vehicle to be parked and scalable in terms of the number of parking spots and drivers/vehicles. A fundamental issue in these agent-based CPS parking systems is the interconnection between the cyber and physical counterparts, i.e. between the software agents and the physical asset controllers to access the parking spots. This paper focuses on developing an agent-based CPS for a smart parking system and particularly addressing how the software agents are interconnected with the physical asset controllers using proper Internet of Things technologies. The proposed approach was implemented in two distinct parking systems, one for bicycles and another for cars, showing an efficient, modular, adaptable and scalable operation.

Multi-stage manufacturing, typical in important industrial sectors, is inherently a complex process. The application of the zero-defect manufacturing (ZDM) philosophy, together with recent technological advances in Cyber-Physical Systems (CPS), presents significant challenges and opportunities for the implementation of new system architectures that contributes for the continuous improvement of the production. This paper describes the experience gained in the GO0D MAN project which aims at realizing a fully functional, replicable and therefore widely exploitable solution, employing multi-agent systems, smart on-line inspection tools, data analytics and knowledge management technologies. In particular, the paper presents the challenges tackled during the deployment of the GO0D MAN system architecture in three relevant industrial use cases, which represent more than 80% of the manufacturing sector.

The digital transformation that is on-going worldwide, and triggered by the Industry 4.0 initiative, has brought to the surface new concepts and emergent technologies. One of these new concepts is the Digital Twin, which recently started gaining momentum, and is related to creating a virtual copy of the physical system, providing a connection between the real and virtual systems to collect and analyze and simulate data in the virtual model to improve the performance of the real system. The benefits of using the digital twin approach is attracting significant attention and interest from research and industry communities in the last few years, and its importance will increase in the upcoming years. Having this in mind, this paper surveys and discusses the digital twin concept in the context of the 4th industrial revolution, particularly focusing the concept and functionalities, the associated technologies, the industrial applications and the research challenges. The applicability of the digital concept is illustrated by the virtualisation of an UR3 collaborative robot which used the V-REP simulation environment and the Modbus communication protocol.

In recent years, the intense urbanization, and consequently the traffic congestion, has been a major concern of large cities. In this context, the development of smart parkings is a suitable solution to deal with this problem. However, the complexity and requirements imposed by such large-scale systems are an obstacle to its easy implementation. In this sense, it is fundamental to adopt emergent ICT and Artificial Intelligence technologies that are capable to address the imposed requirements. Multi-Agent Systems (MAS) is a suitable approach to face this challenge by providing modularity, flexibility, reconfigurability and fast response to condition change based on its decentralized nature. The use of such agent-based solutions to control physical assets, create novel systems entitled Cyber-Physical Systems (CPS) where the interconnection between the cyber and the physical parts is a crucial issue. This paper focuses the interface between the software agents of a smart parking system with the physical control devices of the parking spots. For this purpose, different interface practices were implemented and tested, considering different interaction schemes and technologies. These alternative interface practices were analyzed taking into consideration the response time, scalability and re-usability parameters.

The ongoing fourth Industrial Revolution depends mainly on robust Industrial Cyber-Physical Systems (ICPS). ICPS includes computing (software and hardware) abilities to control complex physical processes in distributed industrial environments. Industrial agents, originating from the well-established multi-agent systems field, provide complex and cooperative control mechanisms at the software level, allowing us to develop larger and more feature-rich ICPS. The IEEE P2660.1 standardisation project, "Recommended Practices on Industrial Agents: Integration of Software Agents and Low Level Automation Functions" focuses on identifying Industrial Agent practices that can benefit ICPS systems of the future. A key problem within this project is identifying the best-fit industrial agent practices for a given ICPS. This paper reports on the design and development of a tool to address this challenge. This tool, called IASelect, is built using graph databases and provides the ability to flexibly and visually query a growing repository of industrial agent practices relevant to ICPS. IASelect includes a front-end that allows industry practitioners to interactively identify best-fit practices without having to write manual queries.

Programmable Logic Controllers (PLCs) execute critical control software that drives Industrial Automation and Control Systems (IACS). PLCs can become easy targets for cyber-adversaries as they are resource-constrained and are usually built using legacy, less-capable security measures. Security attacks can significantly affect system availability, which is an essential requirement for IACS. We propose a method to make PLC applications more security-aware. Based on the well-known IEC 61499 function blocks standard for developing IACS software, our method allows designers to annotate critical parts of an application during design time. On deployment, these parts of the application are automatically secured using appropriate security mechanisms to detect and prevent attacks. We present a summary of availability attacks on distributed IACS applications that can be mitigated by our proposed method. Security mechanisms are achieved using IEC 61499 Service-Interface Function Blocks (SIFBs) embedding Intrusion Detection and Prevention System (IDPS), added to the application at compile time. This method is more amenable to providing active security protection from attacks on previously unknown (zero-day) vulnerabilities. We test our solution on an IEC 61499 application executing on Wago PFC200 PLCs. Experiments show that we can successfully log and prevent attacks at the application level as well as help the application to gracefully degrade into safe mode, subsequently improving availability.

Industry 4.0 promotes the use of emergent technologies, such as Internet of Things (IoT), Big Data, artificial intelligence (AI) and cloud computing, sustained by cyber-physical systems to reach smart factories. The idea is to decen-tralize the production systems and allow to reach monitoring, adaptation and optimization to be made in real time, based on the large amount of data available at shop floor that feed the use of machine learning techniques. This technological revolution will bring significant productivity gains, resources savings and reduced maintenance costs, as machines will have information to operate more efficiently, adaptable and following demand fluctuations. This paper discusses the application of supervised Machine Learning techniques allied with artificial vision, to implement an intelligent, collaborative and adaptive robotic inspection station, which carries out the quality control of Human Machine Interface (HMI) consoles, equipped with pressure buttons and LCD displays. Machine learning techniques were applied for the recognition of the operator's face, to classify the type of HMI console to be inspected, to classify the state condition of the pressure buttons and detect anomalies in the LCD displays. The developed solution reaches promising results, with almost 100% accuracy in the correct classification of the consoles and anomalies in the pressure buttons, and also high values in the detection of defects in the LCD displays.

In the current manufacturing world, the role of maintenance has been receiving increasingly more attention while companies understand that maintenance, when well performed, can be a strategic factor to achieve the corporate goals. The latest trends of maintenance leans towards the predictive approach, exemplified by the Prognosis and Health Management (PHM) and the Condition-based Maintenance (CBM) techniques. The implementation of such approaches demands a well structured architecture and can be boosted through the use of emergent ICT technologies, namely Internet of Things (IoT), cloud computing, advanced data analytics and augmented reality. Therefore, this paper describes the architecture of an intelligent and predictive maintenance system, aligned with Industry 4.0 principles, that considers advanced and online analysis of the collected data for the earlier detection of the occurrence of possible machine failures, and supports technicians during the maintenance interventions by providing a guided intelligent decision support.

The integration of industrial automation systems and software agents has been practiced for many years. However, such an integration is usually done by experts and there is no consistent way to assess these practices and to optimally select one for a specific system. Standards such as the ISO/IEC 25023 propose measures that could be used to obtain a quantification on the characteristics of such integration. In this work, the suitability of these characteristics and their proposed calculation for assessing the connection of industrial automation systems with software agents is discussed. Results show that although most of the measures are relevant for the integration of agents and industrial automation systems, some are not relevant in this context. Additionally, it was noticed that some measures, especially those of a more technical nature, were either very difficult to computed in the automation system integration, or did not provide sufficient guidance to identify a practice to be used.

Agent-based systems, an approach derived from distributed artificial intelligence, have been introduced for designing large complex systems. They are also suitable to solve challenging problems in industrial environments, being an appropriate technology for realizing cyber-physical systems. In such configuration, they need to interface with automation and control devices. However, until now there is no widely accepted practice nor pattern to interface the software agents with the automation functions. This work addresses this issue and introduces corresponding integration patterns in order to achieve full interoperability and reusability. This work, therefore, provides a methodology for mapping existing practices into a set of generic templates and also discusses the applicability of the proposed approach to different industrial application domains.

The increasing need for more adaptive production environments is a big motivator for the adoption of agentbased technologies in industrial systems, as they provide better mechanisms for handling dynamically and intelligently various kinds of production disturbances. Unlike with the utilization of most conventional automation languages, the use of agents enables, in an easy way, the setup of dynamic and autonomous adaptive processes to handle large and complex engineering system functions and interactions. Agent-technologies in cyberphysical systems contexts require at some point integration with automation controllers. However, most commonly available and used agent system implementations in the industry were not designed for hard real-time control use cases, and do not utilize real-time operating systems or dedicated hardware. Hence, they cannot match the hard-real-time performance of automation controllers. This work provides some insights on the performance that can be achieved with agent-based approaches that integrate with low-level automation system functions. It considers the performance of the agent-based practices in light of non-real-time dedicated hardware or operating systems. The results show that agents are well suited for the majority of soft-real-time control applications.

Although the everlasting goal of achieving a zerodefect production system is not new, the current state of the art has not yet allowed to reach this objective. This paper describes the architectural system foundations of the European R&D GO0DMAN project where a multi-agent system is developed as the mean to provide a real-time, multi-level and multi-stage approach to reach a zero-defect production system in multi-stage environments. Particularly, the paper focuses the integration of software agents and quality control stations forming smart inspection tools, aligned with the cyber-physical systems principles. The proposed approach is validated using an electrical motor testbed, where the agent-based system allows the digitization of the running testing system in a multi-stage schema.

This paper describes the development of a multiagent system (MAS) to support the implementation of zero-defect manufacturing strategies in multi-stage production systems. The MAS infrastructure, combined with on-line inspection tools, data analytics and knowledge generation, constitutes a suitable approach to integrate process and quality control in multi-stage environments. This will allow the early detection of product defects, the adaptation to operating condition changes and the optimisation of manufacturing processes. This type of integrated management structure is aligned with a zero-defect manufacturing production model which is of paramount importance in the actual state-of-the-art manufacturing paradigms. As a proof of concept, the devised manufacturing supervision model was deployed into an experimental multi-stage system that run a set of several tests on electrical motors. The agent-based solution was implemented using the JADE framework and the exchange of information structured by proper data models and industrial based Internet-of-Things and Machine-to-Machine technologies, such as OPC-UA, REST and JSON. The obtained results demonstrate the suitability of the devised integrated management model as a vehicle to achieve dynamic and continuous system improvement in multi-stage manufacturing environments.

Agent-technologies have been used for higher-level decision making in addition to carrying out lower-level automation and control functions in industrial systems. Recent research has identified a number of architectural patterns for the use of agents in industrial automation systems but these practices vary in several ways, including how closely agents are coupled with physical systems and their control functions. Such practices may play a pivotal role in the Cyber-Physical System integration and interaction. Hence, there is a clear need for a common set of criteria for assessing available practices and identifying a bestfit practice for a given industrial use case. Unfortunately, no such common criteria exist currently. This work proposes an assessment criteria approach as well as a methodology to enable the use case based selection of a best practice for integrating agents and industrial systems. The software product quality model proposed by the ISO/IEC 25010 family of standards is used as starting point and is put in the industrial automation context. Subsequently, the proposed methodology is applied, and a survey of experts in the domain is carried out, in order to reveal some insights on the key characteristics of the subject matter.

The digitalization advent is characterized by the increasing availability of huge amounts of data that allows to generate new knowledge to support decision-making, problem solving and process optimization. The successful implementation of this digital transformation strongly depends on the skills and competences that professionals can have in the different dimensions of the multidisciplinary vision associated to the data scientist profile. This paper describes a study, conducted under the scope of the Da.Re Erasmus + project, that surveys the existing trainings paths dedicated to the academic education of data scientists and the required hard and soft skills of the job offers for these professionals. The survey covers three countries of the project consortium, namely Portugal, Italy and UK.

At the dawn of the 4thindustrial revolution, the use of software agents, service-oriented architectures and related technologies as primary constructs of Cyber-Physical Industrial systems is of high relevance. Current developments in this area have been consistently supported by an active community of researchers and practitioners in the past decades. Most of the main actors in the area are members of the IEEE IES Technical Community on Industrial Agents (TCIA). This work analyzes the evolution of this research and development network. It does so by identifying and investigating specialized sub-communities within the larger umbrella of Industrial Agents, their research interests and directions. In total of 7430 documents from 8045 authors were collected from the Google Scholar profiles of the TCIA members. The analysis reveals different research trends, transitions over the years and the emergence of application and domain foci, that are critically discussed.

Presently, many industries are facing strong challenges related to the demand of customized and high-quality products. These pressures lead to internal company's conflicts where current production systems have a rigid structure, forcing the company into a organization stall when a fast product change is required. Therefore, the need to smoothly migrate traditional systems into more feature-rich and cost-effective systems, namely Cyber-Physical Production Systems (CPPS), became a highly discussed topic. PERFoRM project focuses the conceptual transformation of existing production systems towards plug&produce ones to achieve flexible and reconfigurable manufacturing environments. In particular, the smooth migration process is considered crucial to effectively transpose existing production systems into truly CPPS. This paper describes the use of Petri nets to design the migration process under the PERFoRM perspective, taking advantage of its inherent capabilities to design, analyze, simulate and validate such complex processes.

Smart grids rely on the integration of distributed energy resources towards an intelligent and distributed manner to organize the electrical power grid enabled by a bidirectional flow of information to improve reliability and robustness, fault detection and system operation, and plug-and-playability of energy devices. The integration of information and communication technologies (ICT), one of the key enablers of smart grids, will ease the deployment of intelligent and distributed systems implementing the automation functions. In this context, there is a need to assess how these systems, developed using these emergent technologies, e.g., multi-agent systems, data analytics and machine learning, will behave and affect the working conditions of the power grid. This paper aims to explore the development of a transparent and loose-coupled interface between the behavioral control system and the physical or simulated power system environment, in a coupled simulation perspective, aiming to assess and improve the development of such systems during the design phase.

The Standards Group of the IEEE Industrial Electronics Society (IES) has been active in standards for industrial electronics in recent years, focusing on sensors and sensors networks, real-time industrial communications and industrial agents in the automation fields. It has also participated and collaborated with other IEEE societies such as the Instrumentation and Measurement Society (IMS) in the IEEE 1451 sensor networks standards family, and with government entities such as the US National Institute of Standards and Technology (NIST). This paper gives a brief synopsis of IES standards activities and the trends it sees in industrial electronics standardization in the coming emerging technologies such as Internet of Things (IoT)/Industrial IoT (IIoT), 5G communications, industrial wireless and possibly transportation electrification. All these technologies are expected to be disruptive to the industry in the coming years and standards must be generated to be effective and beneficial to industry and society. The IES Standards Group anticipates more contributions to the IEEE 1451 standards family, industrial agents, industrial wireless applications with NIST, and possibly with standards activities within Industry 4.0 in the coming years.

Industrial agent technologies have been integrated in key elements coupling industrial systems and software logic, which is an important issue in the design of cyber-physical systems. Although several efforts have been tried out over the last decades to integrate software agents with physical hardware devices, and some commonalities can be observed among the existing practices, there is no uniform way overall. This work presents an empirical survey of existing practices in three application area, namely factory automation, power & energy systems and building automation. It identifies pertaining common issues and discusses how they integrate low level automation functions by utilizing industrial agents. The surveyed practices reveal high diversity, customized traditional integration focusing mostly on I/O functions, without security, and an overall approach that is mostly coupled rather than embedded.

Smart devices and Internet of Things (IoT) technologies are becoming each day more common. At the same time, besides the exponentially increasing demand to analyze the produced data, there is an evolving trend to perform the data analysis closer to the data sources, particularly at the Fog and Edge levels. In this sense, the development of testbeds that can, e.g., simulate smart devices in IoT environments, are important to explore and develop the technologies to enable the complete realization of such IoT concepts. This paper describes the digitization of an electric motor, through the incorporation of sensing and an analytical computational environment, towards the development of a testbed for IoT and Big Data technologies. The smart electric motor testbed provides real-time data streams, enabling a continuous monitoring of its operation along all the device life-cycle through advanced data analytics. Furthermore, the paper discusses how specific data analytics features fit the different IoT layers, while preliminary experiments demonstrate the testbed potentials.

Nowadays, many organizations intend to convert their existing production systems towards ones that are terized by adaptability, openness, flexibility and modularity. This requires a redesign of existing information processing systems especially related to control, leading possibly to cyber-physical production systems (CPPS). However, the implementation of new control technologies will have a direct impact on the normal operational status of production while engineers will also face several challenges and obstacles in adopting intelligent automation systems. New step-wise migration strategies are required to holistically support industries in their journey towards CPPS taking into account technical, economic and social aspects. This paper discusses the migration state-of-the-art strategies, analyzing them and providing a first attempt to define a migration approach for innovative production systems.

The world is moving towards to the fourth industrial revolution, usually linked with the Industrie 4.0 initiative, enables the digitization of manufacturing factories by using Cyber-Physical Systems and emergent technologies like Internet of Things and Internet of Services. The seamless reconfiguration of these complex industrial cyber-physical systems is an important challenge for the complete implementation of this revolution, being necessary to re-think the way such mechanisms can be designed and engineered. This paper presents an agentbased reconfiguration system for the dynamic and seamless reconfiguration of a physically-reconfigurable modular micro-flow production system in the area of manufacturing of aerospace engine components.

Manufacturing companies generate huge amounts of relevant data that, potentially, can help the decision makers to monitor and supervise the health status of the shop-floor. Many problems are present that prevent this monitoring, namely the identification of which data should be collected, how to execute this collection or how this data should be analysed and displayed to the user. This paper deals with the last issue, where a dynamic and responsive User Interface is developed enabling the decision maker, having an in-the-mesh intervention, to constantly monitor relevant Key-Performance Indicators (KPI). The paper describes the development of such tool, considering a web-based approach under a platform agnostic perspective. The user interface presents an dynamic view over the aggregated KPI, where data is annotated with relevant warning information.

Several research agendas worldwide are targeting the development of Industrial Cyber-physical Systems as the next generation of intelligent embedded devices with improved interaction capabilities. These devices, and their potential uses, are though to deliver a radical increase in system sustainability, reconfigurability and flexibility which is perceived to be the root of the so called 4thIndustrial Revolution. However such technical systems, at the envisioned revolutionary scale, do not exist just yet and require a convergent and multidisciplinary research and development efforts. The academia curricula are also, albeit slowly, adjusting to the emerging education requirements. The Summer School on Intelligent Agents in Automation is a joint effort from several researchers in core areas of the 4thIndustrial Revolution landscape to close the gap and promote advanced education in this context. This paper describes the implementation of the 2ndedition of the event as well as the experience and reflections resultant from it.

Multiple software agent-based solutions have been developed during the last decades, and applied with varying success to different domains offering control, reconfiguration, diagnosis, monitoring, etc. However, the promise that they once posed in terms of a new alternative decentralized approach offering modularity, flexibility and robustness, is only partially fulfilled. This paper investigates some key factors, i.e., design, technology, intelligence/algorithms, standardization, hardware, challenges, application and cost, which are hypothesized to be linked to the Industrial Agent acceptance. Empirical data was acquired via a conducted survey, and statistically analyzed to investigate the support of the posed hypotheses. The results indicate that all the factors are seen important issues that play a role toward deciding for or against an industrial agent solution.

The papers in this special section focus on the deployment of smart agents and cyber-physical systems (CPS) for usse in future industrial systems and applications.

Cyber Physical Systems (CPS) are expected to shape the evolution of production towards the fourth industrial revolution named Industry 4.0. The increasing integration of manufacturing processes and the strengthening of the autonomous capabilities of manufacturing systems make investigating the role of humans a primary research objective in view of emerging social and demographic megatrends. Understanding how the employees can be better integrated to enable increased flexibility in manufacturing systems is a prerequisite to allow technological solutions, as well as humans, to harness their full potential. Humans can supervise and adjust the settings, be a source of knowledge and competences, can diagnose situations, take decisions and several other activities influencing manufacturing performances, overall providing additional degrees of freedom to the systems. This paper, studies two different integration models: Human-in-the-Loop and Human-in-the-Mesh. They are both analysed in the context of four industrial cases of deployment of cyber physical systems in production.

Cyber-physical systems constitutes a framework to develop intelligent, distributed, resilient, collaborative and cooperative systems, promoting the fusion of computational entities and physical devices. Agent technology plays a crucial role to develop this kind of systems by offering a decentralized, distributed, modular, robust and reconfigurable control structure. This paper describes the implementation of a summer school aiming to enhance the participants' knowledge in the field of multi-agent systems applied to industrial environments, being able to gain the necessary theoretical and practical skills to develop real industrial agent based applications. This is accomplished in an international framework where individual knowledge and experiences are shared in a complementary level.

The world is assisting to the fourth industrial revolution, with several domains of science and technology being strongly developed and, specially, being integrated with each other, allowing to build evolvable complex systems. Data digitization, big-data analysis, distributed control, Industrial Internet of Things, Cyber-Physical Systems and self-organization, amongst others, are playing an important role in this journey. This paper considers the best practices from previous successful European projects addressing distributed control systems to develop an innovative architecture that can be industrially deployed. For this purpose, a particular design process has to be addressed in order to consider the requirements and functionalities from various use cases. To investigate the known practices, four use cases are enlighted in this paper, which cover a wide spectrum of the European industrial force, as well as industrial standards to support a smooth migration from traditional systems to the emergent distributed systems.

With the emergence of the Industry 4.0 concept, or the fourth industrial revolution, the industry is bearing witness to the appearance of more and more complex systems, often requiring the integration of various new heterogeneous, modular and intelligent elements with pre-existing legacy devices. This challenge of interoperability is one of the main concerns taken into account when designing such systems-of-systems, commonly requiring the use of standard interfaces to ensure this seamless integration. To aid in tackling this challenge, a common format for data exchange should be adopted. Thus, a study to select the foundations for the development of such a format is hereby presented, taking into account the specific needs of four different use cases representing varied key European industry sectors.

The manufacturing industry is facing a technology paradigm change, as also captured in the Industrie 4.0 vision as the fourth industrial revolution. Future smart industries will require to optimize not only their own manufacturing processes but also the use of products and manufacturing resources, their maintenance and their recycling. In this context the strengths and weaknesses of two key concepts, namely Cyber-Physical Systems (CPS) and Intelligent Product (IP) are discussed, and it is suggested that an integration of these two approaches to meet the introduced emergent requirements is beneficial. The integration of CPS and IP is shown via two real-world industrial cases, covering different phases of the product life-cycle, namely the production, use and maintenance phases.

The self-sustainability of micro grids is an important challenge in the smart grids field due to the need to balance the fast growing energy consumption, reduce greenhouse gas emissions and increase energy independence by using renewable resources. The use of decentralized paradigms, and particularly multi-agent systems and holonic control, enable to face this challenge by implementing intelligent mechanisms that allow an efficient management of the power flow. In this paper, a holonic based model is introduced, considering load scheduling and forecast mechanisms to improve the micro grids self-sustainability, and consequently reduce the energy cost and the energy dependency from the main utility. The designed holonic based model and strategies were developed by using the agent technology, and particularly the JADE framework, showing important improvements in the self-sustainability of micro grids working in different operating modes.

The role of human in the Industrie 4.0 vision is still considered as irreplaceable. Therefore, user interfaces of cyber-physical systems involved in the production automation need to be well designed and taking into consideration the industrial application requirements. With the advances in augmented and virtual reality data visualization and novel interaction techniques like mid-air gestures, these approaches seem to be suitable for integration into the industry environment. This paper describes the implementation of an augmented reality application for smart glasses with mid-air gestures and smart phone with touch interaction to compare and evaluate the usage of such interfaces in a production cell comprising an industrial robot.

Future industrial systems can be realized using the cyber-physical systems (CPSs) that advocate the coexistence of cyber and physical counterparts in a network structure to perform the system's functions in a collaborative manner. Multiagent systems share common ground with CPSs and can empower them with a multitude of capabilities in their efforts to achieve complexity management, decentralization, intelligence, modularity, flexibility, robustness, adaptation, and responsiveness. This work surveys and analyzes the current state of the industrial application of agent technology in CPSs, and provides a vision on the way agents can effectively enable emerging CPS challenges.

In the nowadays highly unstable manufacturing market, companies are faced, on a daily basis, with important strategic decisions, such as “does the company has the necessary capacity to accept a high volume order?” or “what measures need to be implemented if the product demand increases x% a year?”. Decision-makers, i.e. company's managers, rely on their experience and insights supported by classical tools to take such decisions. Classical mathematical solvers or agent-based systems are typical architectural solutions to implement strategic planning tools to support decision-makers on this important task. Within the ARUM (Adaptive Production Management) project, a hybrid strategic planning tool was specified and developed, combining the optimization features of classical solvers with the flexibility and agility of agent systems. This paper briefly presents such architecture and focuses on the generation of the “what-if game” mechanism to support the generation of more intelligent and dynamic planning solutions.

Robotics constitutes a multidisciplinary area, congregating knowledge from different scientific domains. The learning of robotic systems requires the acquisition of multidisciplinary scientific bases, and high integration and synthesis abilities, which is not an easy task. This paper describes the implementation of a lifelong course that aims to provide a global insight on robotics field, introducing the concepts and technologies for different domain applications, namely industrial robotics, autonomous mobile robotics and robotics applied in medicine. This is accomplished in an international framework where individual knowledge and experiences will be confronted in a multidisciplinary level and intercultural environment.

Micro grids represent an emergent vision to address the challenges imposed by recent trends in smart electrical grids, where the large-scale integration of distributed energy production units plays an important role. Nevertheless, the realization of this vision requires the use of advanced intelligent approaches to manage the micro grids elements, such as distributed renewable energy production units, loads and storage devices. Multi-agent systems provide a suitable framework to design and implement such systems, where autonomous agents are endowed with predictive data analytics capabilities, e.g., for the prediction of renewable energy production and consumption, taking advantage of the large amount of data produced in these environments. In this context, this paper presents an agent-based system for the management of micro grids, where predictive capabilities were embedded in agents to provide real-time data analytics. The proposed approach was applied to an experimental case study where a set of predictive models was tested for short and long term forecasting of the energy produced by photovoltaic units.

Cyber-physical systems are an emergent paradigm to design complex, adaptive and smart systems, combining computational applications with physical hardware devices. Multi-agent systems play an important role in such systems to provide flexibility, robustness and adaptation, but their alignment will require the integration of agents with physical devices. This process is usually complex and time consuming due to the proprietary protocols provided by the hardware automation devices. This paper describes the deployment of an agent-based system in a small-scale flexible production system composed by a set of heterogeneous automation devices, such as programmable logic controllers and robots.

The continuous change in the manufacturing world is demanding more flexible, responsive and accurate planning tools, which are able to assist the decision-makers to take tactical and strategic decisions on short notice with a high level of confidence. For this purpose, these tools should dynamically explore different operative scenarios in the planning procedure and produce information about key performance indicators. This paper describes the development of an agent-based strategic planner, combining the flexibility of multi-agent systems principles with the optimization capability of a Mixed Integral Programming technique. The tool is integrated in an ecosystem of heterogeneous decision-making systems through an Enterprise Service Bus that also provides access to legacy data.

Manufacturing companies are currently forced to reconsider their production processes by adopting more flexible, robust, and adaptive systems, aiming to improve their competitiveness. Multiagent systems (MASs) technology is suitable to address this challenge by providing an alternative way to design these complex systems based on the decentralization of the control functions over distributed entities. This paper describes the installation of a MAS solution in an industrial factory plant producing laundry washing machines. The installed solution focuses on the integration of quality and process control, and contributes to the maximization of the factory profitability facing changing conditions by applying self-adaptation procedures at the local and global levels. The preliminary results show improvements in the production efficiency and product quality, as well as a reduction of the scrap costs.

Enterprise Service Bus (ESB) is a middleware infrastructure that provides a way to integrate loosely-coupled heterogeneous software applications based on the services principles. The life-cycle management of services in such environments is a critical issue for the component's reuse, maintenance and operation. This paper introduces a service life-cycle management module that extends the traditional functionalities with advanced monitoring and data analytics to contribute for the robustness, reliability and self-organization of networks of clusters based on ESB platforms. The realization of this module was embedded in the JBoss ESB, considering a sniffer mechanism to collect the service messages crossing the bus and a Liferay portal to display relevant information related to the services' health.

Micro grids play an important role in the context of smart grids, improving the system reliability, resilience and stability. In case of micro grids disconnected from the main grid, due to geographical reasons or failure in the service provider, the self-sustainability management is a key issue. This paper discusses agent-based automation and control approaches to handle the self-sustainability in electrical stand-alone micro grids. The proposed agent-based model is applied to a case study and simulated using the Netlogo agent-based modelling platform, allowing to analyze different control and prediction strategies to improve their self-sustainability.

The intention of this paper is to provide an overview of using agent and service-oriented technologies in intelligent energy systems. It focuses mainly on ongoing research and development activities related to smart grids. Key challenges as a result of the massive deployment of distributed energy resources are discussed, such as aggregation, supply-demand balancing, electricity markets, as well as fault handling and diagnostics. Concepts and technologies like multiagent systems or service-oriented architectures are able to deal with future requirements supporting a flexible, intelligent, and active power grid management. This work monitors major achievements in the field and provides a brief overview of large-scale smart grid projects using agent and service-oriented principles. In addition, future trends in the digitalization of power grids are discussed covering the deployment of resource constrained devices and appropriate communication protocols. The employment of ontologies ensuring semantic interoperability as well as the improvement of security issues related to smart grids is also discussed.

The large-scale integration of distributed energy resources into a complex cyber-physical system demands proper automation platforms for monitoring, control, optimization and reconfiguration, addressing intelligent energy systems. This paper discusses the use of multi-agent systems as a suitable solution to address this challenge by analyzing their benefits when applying them to the field of Smart Grids and surveying existing works and initiatives. The principles and use cases established in the MASGrid project illustrate the application of multi-agent technology in active power distribution systems.

Multi-agent systems are a suitable approach to address the current requirements imposed to companies demanding for more flexible, adaptive and reconfigurable systems. In spite of the promising perspective, only a reduced number of such solutions are running in industrial scenarios. Standardization is recognized as a major issue in the development of devices, systems or processes in industrial environments, constituting a real road-blocker for the wider acceptance of agent-based solutions in industry. This paper discusses the importance of standardization compliance in the development of industrial agent-based solutions and the actions to be performed to overcome this issue.

New manufacturing control architectures rely on the decentralization of the system entities to handle in a more effective way the current constraints imposed to the highly dynamic manufacturing world. Despite of their benefits, these architectures miss the performance levels of those achieved by traditional centralized systems running without failures. This paper proposes a holonic multi-agent system architecture, based on ADACOR foundations, which considers the self-organization concept to create a two vector solution embedded in micro and macro levels. The paper describes the self-organization mechanism embed at the micro level, named behavioural self-organization, being validated using the AIP-PRIMECA flexible manufacturing system.

Nowadays, manufacturing control systems have evolved from reactive and informative decision support system to a proactive and intelligent manufacture management mechanism. Industries require both optimal and adaptive manufacturing processes in order to respond competitively to market requirements. In response, advanced manufacturing control systems are configured as artificial intelligence distributed architectures capable to support environment disturbances. However, these techniques, specifically Multi-Agent systems and Holonic Manufacturing Systems, are weak supporting optimal performance. Conversely, Operational Research decision support systems achieve optimality under centralized architectures. Still, these are weak supporting adaptable processes under environmental disturbances. Consequently, researchers recently have focused in articulating optimality and adaptation paradigms in order to construct a robust optimal-wise and adaptable mechanism. This paper surveys the literature in manufacturing control systems that use these articulated adaptable optimal mechanism, constructs a proposed typology according structural features and discusses future possibilities for balancing optimality and adaptability characteristics.

This paper discusses the methodology for the development of a strategic planning tool, and particularly the architectural solution to be adopted, within an integrated ICT solution for the improvement of planning and scheduling systems for the manufacturing of small production lots of complex products. After the analysis of different possible architectural solutions, the paper proposes a hybrid solution that combines existing solvers with the multi-agent system principles. The proposed approach introduces several benefits, namely in terms of flexibility, robustness, and achievement of exploratory alternative solutions, playing interactive what-if simulation supporting decision-makers to take strategic decisions.

The contribution of this paper is a conceptual architecture based on intelligent services for manufacturing support systems. Current approaches cannot cope with the dynamism inherent in the manufacturing domain, where unexpected and drastic changes occur at any time and affect cross-boundary systems. Our approach alleviates these issues and focuses on the need for supporting real-time automatic negotiation, planning, scheduling and optimisation, intra and across factories. We present an execution example of our approach in the aerospace industry, instantiating the conceptual architecture using different technologies. With this we push forward a new definition of Intelligent Services and indicate future work in the area.

Industrial agents technology leverages the benefits of multiagent systems, distributed computing, artificial intelligence techniques and semantics in the field of production, services and infrastructure sectors, providing a new way to design and engineer control solutions based on the decentralization of control over distributed structures. The key drivers for this application are the benefits of agent-based industrial systems, namely in terms of robustness, scalability, reconfigurability and productivity, all of which translate to a greater competitive advantage. This manuscript monitors the chronology of research and development of the industrial applications of multiagent and holonic systems. It provides the comprehensive overview of methodologies, architectures and applications of agents in industrial domain from early nineties up to present. It also gives an outlook of the current trends as well as challenges and possible future application domains of industrial agents.

The paper introduces a concept for the combination of mechatronic unit and multi-agent system approaches. It highlights how combining both approaches to a new kind of integrated system architecture may enforce the dissemination and reusability of methods and automatisms developed for one specific system architecture to other similar ones. Additionally it will show how this integrated architecture leads to a better exchange of Automation architectures in general. Thus, all systems compliant with the described architecture may benefit from methods and automatism applicable for a broad range of systems. The suitability of this integration approach is verified by using the example of a washing machine production line.

Multi-agent system (MAS) is being pointed out as a suitable technology to develop systems that demand flexibility, robustness and re-configurability. Consequently, a significant effort has been noticed to apply MAS to industrial domains exhibiting these characteristics, like manufacturing and smart grids. In spite of the adequacy of the MAS principles to solve the industrial requirements, the truly deployment of MAS for industrial applications is far to be solved. This paper discusses the current challenges for the deployment of MAS in the context of industrial applications, mainly focusing the integration of agents with physical equipment and the ability to run agents directly in industrial or low cost controllers. An experimental MAS solution for a smart grid case study was deployed aiming to support the discussion.

The fusion of mechatronics, communication, control and information technologies has allowed the introduction of new automation paradigms into the production environment. The virtualization of the production environment facilitated by the application of the service-oriented architecture paradigm is one of major outcomes of that fusion. On one side, service-oriented automation works based on exposition, subscription and use of automation functions represented by e.g. web services. On the other side, the evolution of traditional industrial systems, particularly in the production area, as a response to architectural and behavioural (functional) viewpoints of the ISA95 enterprise architecture, where a close inter-relation between SCADA, DCS and MES systems facilitate the management and control of the production environment. Automation functions are increasingly performed by the composition and orchestration of services. Among other methods, the application of formal Petri net based orchestration approaches is being industrially established. This paper presents the major characteristics that such a Petri net based orchestration presents when it is developed, implemented and deployed in an industrial environment.

Multi-agent systems paradigm is a suitable approach to implement distributed manufacturing systems addressing the emergent requirements of flexibility, robustness and responsiveness. In such systems, an ontology is a crucial piece to provide a common understanding on the vocabulary used by the intelligent, distributed agents during the exchange of shared knowledge. This paper describes the design of an ontology to define the structure of the knowledge that is used within a multi-agent system integrating process and quality control in production lines for home appliances, which is being developed within the EU FP7 GRACE (inteGration of pRocess and quAlity Control using multi-agEnt technology) project. The ontology schema is validated by instantiating for a case study derived from a washing machines production line.

In the last decade multi-agent systems (MAS) have been thoroughly investigated as a suitable paradigm for process control. Despite the difficulties in designing and maintaining a MAS architecture for real production scenarios, several EU research projects have been financed and many companies are looking at the improvement in the production efficiency of such systems. The EU FP7 GRACE project aims at the integration of process and quality control in a multi-agent environment. This paper discusses the integration of quality control stations into the GRACE MAS. The stations themselves will become autonomous agents, capable of self-reconfiguration according to the needs of the production to improve shop floor efficiency while maintaining the same (and possibly higher) quality level for the manufactured products. Details about the quality control agent behaviour, its integration with the physical hardware and communication with the other agents will be given. All the concepts have been tested in an experimental environment where a vision inspection station behaves as one of the agents of the MAS platform, communicating and exchanging data with the other agents and optimizing its operations over time.

The virtual manufacturing concept is based on the use of computational resources during the design of products and manufacturing systems, reducing the ramp-up time to market and the costs for the system's design, operation and reconfiguration. Combined with emergent paradigms for manufacturing control, such as multi-agent systems and service-orientation principles, and using a formal language to describe the system behavior, e.g. Petri nets, a truly and complete virtual production environment is achieved. This paper discusses this powerful approach to the virtual design and production environment driven by formal models, allowing users to design and deploy collaborative and reconfigurable automation applications in a faster and cost-effective fashion. A special attention is devoted to the process of migration from the virtual environment to the real world, aiming to reduce the transition time and increasing the efficiency of the process.

The expected behavior of industrial automation systems can be defined and modeled, but since not every event is predicted at design time, additional care has to be taken during the operation to handle situations such as exceptions, failures and new production orders. Another remark is also the limitation of modeling languages used in the control to permit the intervention of higher grade decision mechanisms. This paper discusses the application of decision support system for Petri net based processes to control automation components and devices. The decision mechanisms are used for path planning, production scheduling and preventive maintenance, to support the description of processes in Petri nets formalism. As such, the same Petri nets designed for the control are also used as analytical information input to the decision support system and for the detection process options that require decision. The solution provides a dynamic complement to the static modeling and operational flexibility. Experiments were done in a real service-oriented industrial factory-cell to prove the specified approach.

Service-oriented paradigm is becoming a suitable approach to address the demand for distributed and reconfigurable control systems in industrial and factory automation environments. This paper discusses a decision support system approach for the conflict resolution and exception handling in a service-oriented control architecture for industrial automation, that uses high-level Petri nets to implement the system process control. The proposed decision support system is based on two granularity levels, one related to decision mechanisms embedded in local control components and other related to upper and global intelligent entities that provide decision-making services.

Answering to the need to have innovative manufacturing control systems tailored to the current economical, technological, and customer trends, where dynamic and volatile environments prevail, adaptive holonic control architecture (ADACOR) aims to increase the agility and reconfigurability of the production system, contributing for the improvement of the enterprise competitiveness when it works in dynamic and volatile environments. The paper describes the implementation and experimental validation of an ADACOR-based holonic manufacturing control system in a real flexible manufacturing system, using multiagent systems technology. The results extracted from a set of experimental tests allowed to verify the correctness, applicability, and merits of the ADACOR concepts and also contribute to prove the applicability of multiagent systems in industrial environments.

This paper proposes a methodology for the development of collaborative (agent-based) production systems, using high-level Petri nets. The proposed methodology supports the development life-cycle from specifications analysis through to design-validation and implementation of collaborative and re-configurable production systems and their control systems, in an integrated manner. It covers a wide spectrum of application domains, ranging from intelligent mechatronic devices to multi-agent distributed manufacturing control systems.

This paper discusses a new approach to the development life-cycle of agent-based production control applications, from the design to the operation, based in a catalogue of high-level Petri nets. The high-level Petri net-based approach facilitates the conception, definition and formal specification of an "encapsulation process" in industrial production systems. The catalogue includes elements for the identification of manufacturing components, the development of agent-based control units, the formal validation of the models and the formal specification of complete collaborative automation scenarios

The holonic manufacturing paradigm allows a new approach to the emergent requirements faced by the manufacturing world, through the concepts of modularity, decentralisation, autonomy, re-use of control software components. The formal modelling and validation of the structural and behavioural specifications of holonic control systems assumes a critical role. This paper discusses the formal validation of the Petri Net models designed to represent the behaviour and specifications of the holon classes defined at ADACOR architecture.

This paper discusses the opportunity to use multiagents technology in automation and distributed manufacturing systems and the expected improvements. To support the discussion, it is described a manufacturing cell control application developed using the multi-agent technology, and the results are compared with other control application developed in the past by some of the authors, using a traditional approach, for the same flexible manufacturing cell.