Packing products into a pallet or other medium is an unavoidable activity for producing companies. In many cases, packing is based on operator experience and training using packing patterns that have worked before. Automated packing, on the other hand, requires a systematic procedure for devising packing solutions. In the scientific literature, this problem is known as 3D bin packing (3DBP) and many authors have proposed exact and heuristic solutions for many variations of the problem. There is, however, a lack of datasets that can be used to test and validate such solutions. Many of the available datasets use randomly generated products with extremely limited connection to real practice. Furthermore, they contain a reduced number of product configurations and ignore that packing relates to customers orders, which have specific relative mixes of products. This paper proposes a software toolbox for generating arbitrarily large datasets for 3DBPP based on real industry data. The toolbox was developed in connection with the analysis of a real dataset from the food and beverages sector, which enabled the creation of several synthetic datasets. The toolbox and the synthetic datasets are publicly available and can be used to generate additional data for testing and validating 3DBP solutions. The industry is increasingly becoming data dependent and driven. The ability to generate good quality synthetic data to support the development of solutions to real industry problems is of extreme importance. This work is a step in that direction in a domain where open data are scarce.

The Industrial Revolution, which originally involved the change from an agrarian and handicraft economy to a market dominated by factory mechanization during the early 18th century, has profoundly shaped the world. It has progressed through four disruptive phases: Industry 1.0 through Industry 4.0. Industry 1.0 encompassed early automation, while Industry 2.0 began at the end of the 19th century, when enormous technological advances were made, such as mass production, electrification, and new modes of transportation. Industry 3.0 began during the 1970s, a decade that gave rise to the electronics, telecommunications, and computing that enable full automation and robotics. Industry 4.0 kicked off at the dawn of the third millennium, marked by the ubiquitous use of Internet technologies, which have radically transformed how people, society, and industry interact.

The description of structure and complex interactions in Multi-agent-based Industrial Cyber-physical (MAS-ICPS) systems has been elusively addressed in the literature. Existing works, grounded on model-based engineering, have been successful at characterizing and solving system integration problems. However, they fail to describe accurately the collective and dynamic execution behaviour of large and complex industrial systems, particularly in more discrete production domains, such as: automotive, home appliances, aerospace, food and beverages, etc. In these domains, the execution flow diverts dynamically due to production disturbances, custom orders, fluctuations in demand in mixed model production, faults, quality-control and product rework, etc. These dynamic conditions require re-allocation and reconfiguration of production resources, redirection of production flows, re-scheduling of orders, etc. A meta-model for describing the structure and complex interactions in MAS-ICPS is defined in this paper. This contribution goes beyond the State-Of-The-Art (SOTA) as the proposed meta-model describes structure, as many other literature contributions, but also describes the execution behaviour of arbitrarily complex interactions. The previous is achieved with the introduction of general execution flow control operators in the meta-model. These operators cover, among other aspects, delegation of the execution flow and dynamic decision making. Additionally, the contribution also goes beyond the SOTA by including validation mechanisms for the models generated by the meta-model. Finally, the contribution adds to the current literature by providing a meta-model focusing on production execution and not just on describing the structural connectivity aspects of ICPSs.

Cyber-Physical Systems (CPS) is a key concept in Industry 4.0, acting as a backbone to develop smart processes, machines and products. Multi-Agent Systems (MAS) is a suitable paradigm for the realization of such industrial CPS systems, supporting the distribution of intelligence and decision-making capabilities among a network of autonomous and cooperative agents. Standardization is a key factor for the acceptance of industrial CPS and agent-based systems, assuming a critical role in establishing specifications for the hardware integration, which is an important requirement for industrial agents. This paper focuses on the recently established IEEE 2660.1-2020 standard that defines a recommended practice to solve the interface problem when applying industrial agents, namely integrating intelligent software agents with low-level automation devices in the CPS context. The paper illustrates the applicability of the standard in three different application scenarios related to power and energy systems, factory automation and building automation, and discusses future directions in terms of standardization in the field of industrial agents, that are required for its wider adoption in the realization of industrial CPS solutions.

The concept of Cyber-physical Production System has recently emerged. It emphasizes the expected and very close connection between industrial software systems and production assets on the next generation of intelligent industrial systems. The cyber-physical conceptualization hints at assets making their functions available through the so called administration shells. These are software structural design tenets that from, a functional perspective, will not only incorporate software interfacing aspects but also autonomous behaviour. There is an important body of practice in what is now perceived as cyber-physical integration. However, such work has taken a somehow informal approach to formalizing system engineering aspects and in particular to the impact of time-related constraints affecting, the design locally and, the CPPS as a whole. This paper provides a model that relates physical, logical and computational aspects of cyber-physical design from structural and behavioural perspectives. Its goal is to provide a starting point for designing cyber-physical components while supporting informed decision making in respect to software design, integration and deployment practices that enable these components to operate in cyber-physical synchronization.

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 best-fit 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.

Biological collective systems have been an important source of inspiration for the design of production systems, due to their intrinsic characteristics. In this sense, several high level engineering design principles have been distilled and proposed on a wide number of reference system architectures for production systems. However, the application of bio-inspired concepts is often lost due to design and implementation choices or are simply used as heuristic approaches that solve specific hard optimization problems. This paper proposes a bio-inspired reference architecture for production systems, focused on highly dynamic environments, denominated BIO-inspired Self-Organising Architecture for Manufacturing (BIOSOARM). BIOSOARM aims to strictly adhere to bio-inspired principles. For this purpose, both shopfloor components and product parts are individualized and extended into the virtual environment as fully decoupled autonomous entities, where they interact and cooperate towards the emergence of a self-organising behaviour that leads to the emergence of the necessary production flows. BIOSOARM therefore introduces a fundamentally novel approach to production that decouples the system’s operation from eventual changes, uncertainty or even critical failures, while simultaneously ensures the performance levels and simplifies the deployment and reconfiguration procedures. BIOSOARM was tested into both flow-line and “job shop”-like scenarios to prove its applicability, robustness and performance, both under normal and highly dynamic conditions.

Learning and intelligent algorithms are the basis for Cyber-Physical Production Systems (CPPS). They enable flexibility through reconfiguration ease and fault tolerance by ensuring that the CPPS adapts to changing conditions. However, in order to exhibit such characteristics, CPPS require a proper support for reliably handling the real time behavior of the physical systems they are in control of. This paper presents and discusses basic requirements for control software, which enables flexible and adaptable automated Production Systems (aPS) modelled according to the CPPS concept. In doing so, the paper exposes the main architectural guidelines and rationale for where to place and operate intelligent algorithms in the context of industrial automation for continuous processes.

Establishing mass-customization practices, in a sustainable way, at a time of increased market uncertainty, is a pressing challenge for modern producing companies and one that traditional automation solutions cannot cope with. Industry 4.0 seeks to mitigate current practices limitations. It promotes a vision of a fully interconnected ecosystem of systems, machines, products, and many different stakeholders. In this environment, dynamically interconnected autonomous systems support humans in multifaceted decision-making. Industrial Internet of Things and cyberphysical systems (CPSs) are just two of the emerging concepts that embody the design and behavioral principles of these highly complex technical systems. The research within multiagent systems in manufacturing, by embodying most of the defining principles of industrial CPSs (ICPSs), is often regarded as a precursor for many of todays emerging ICPS architectures. However, the domain has been fuzzy in specifying clear-cut design objectives and rules. Designs have been proposed with different positioning, creating confusion in concepts and supporting technologies. This paper contributes by providing clear definitions and interpretations of the main functional traits spread across the literature. A characterization of the defining functional requirements of ICPSs follows, in the form of a scale, rating systems according to the degree of implementation of the different functions.

The increasing need for more adaptive production environments is a big motivator for the adoption of agent-based 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 cyber-physical 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.

The concept of Industry 4.0, or the Fourth Industrial Revolution, has the potential for radically increased system reconfigurability and flexibility. At its core, the notion of cyber-physical system, as the new generation of embedded systems with advanced artificial intelligence and improved communication capabilities, is seen as the key enabling concept that will render production activities more sustainable. The cyber-physical conceptualization dramatically reduces the integration effort by virtually eliminating the need, time, and cost for reprogramming. However, there are still important challenges that need to be addressed before one can start to design cyber-physical production systems consistently. These intertwine and are not as easily solvable as the popular science descriptions may suggest. This paper brings them forward and develops a critical comparative analysis between today’s automation solutions and their potential cyber-physical counterparts. The analysis considers the technical and conceptual challenges that are included in the process of migrating today’s, mostly bespoke, automation solutions to highly modularized, dynamic, and interactive cyber-physical production systems (CPPSs). In this context, this paper considers the interplay between form and function of industrial components, at the light of their cyber-physical formulation. At the same time, it addresses the system-level (de)composability and interaction design challenges that arise from the integration of modular CPPSs.

At the dawn of the 4^th industrial 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.

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.

Recent advances in information technologies and artificial intelligence are enabling the creation of intelligent and highly reconfigurable factories which will lead to unprecedented production development. The notion of Cyber-Physical Production System (CPPS), denoting a system where mechatronic components are coupled to a smart logical entity that enables these factory units to interact in an adaptive way, has been presented as one of the cornerstones of what is perceived to be the 4^th Industrial Revolution. However, more than two decades of research have shown that such a vision is not as new as recent programmes suggest and that certain developments are only now reaching a maturity stage that renders them usable with limitations. These are due to a set of persistent design challenges that undermine their acceptance along with the fact that the preconditions for operating such systems globally are far from being satisfied. This paper provides an integrated, clear and critical view on pending design issues that still need to be satisfied. In this context, the paper concentrates on the conceptual and technical design barriers that relate to the development of cyber-physical production components and their operation in the context of a CPPS.

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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 4^th Industrial 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 4^th Industrial Revolution landscape to close the gap and promote advanced education in this context. This paper describes the implementation of the 2^nd edition of the event as well as the experience and reflections resultant from it.

As web related technologies continue to find their way into production related activities, their true potential can only be explored with a shop floor that is cyber-physically agile. This means the ability to quickly reconfigure a production system during production changeovers while significantly reducing setup times and non value-adding activities keeping the overall performance of the control system. Currently, system reconfiguration in mainly supported by reprogramming. However, reprogramming of large complex systems is often a time consuming and risky activity since it has a non negligible potential for introducing errors which normally result in long debugging times. In this context, this paper details an agent-based system architecture that enables quick system reconfiguration and minimizes the reprogramming effort. For this purpose a production system is abstracted as a multiagent system that dynamically interacts with a native, programmable-logic-controller-based control system and is able to re-parametrize it. The focus on re-parametrization means that the native system can still operate in the absence of the agent system which improves the overall robustness of the solution, using readily available equipment and enables its integration with legacy systems.

The harmonization of software (cyber part) and hardware (physical part) in a cyber-physical system or component is an important challenge. One of the theoretical advantages of the cyber-physical formulation is the increased system operability. This results from a harmonized cyber interface that governs the interactions of different systems and components. However, the connection between both parts has been characterized by a much higher degree of heterogeneity due to distinct actuation/sensing devices and different controlling layers. The cyber-physical relation is specially important in the scope of industrial systems where the same generic cyber model will apply to components that, despite having the same function, denote a fairly different physical implementation. Agents have been widely considered as an implementation mechanism for creating cyber-physical industrial systems and JAVA has been one of the dominant programming languages. In this context, the paper proposes and discusses a JAVA-based Hardware Abstraction Layer (HAL) that ensures the generic connectivity between the cyber and the physical parts through reconfiguration, rather than reprogramming. The performance of the proposed HAL was tested under different conditions in a test case where agents connect to standard programmable logic controllers over a TCPIP network.

Purpose - This paper aims to provide a method and decision support tool to enhance swift reconfiguration of Plugamp;Produce (Pamp;P) systems in the presence of continuously changing production orders. Design/methodology/approach - The paper reviews different production scenarios and system design and configuration methods and more particularly specifies the need of decision support tools for Pamp;P systems that integrate configuration and planning activities. This problem is then addressed by proposing a method that helps reduce the solution space of the reconfiguration problem and allows the timely selection of the most promising reconfiguration alternative. Findings - The proposed method was found to be helpful in reducing the reconfiguration alternatives that need to be considered and in selecting the most promising one for different orders. The advantages and limitations of this method are identified, and an illustrative test case of the approach is presented, corroborating the method applicability in the absence of large queues in the system. Originality/value - This paper addresses a less explored domain within the Pamp;P systems research field, which is the system reconfiguration. It proposed a method to support system validation and reconfiguration jointly with an illustrative test case. This represents an original contribution to the Pamp;P research field, and it can have impact in improving agility and decreasing the complexity of reconfiguration activities to cope with constantly changing production orders.

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 systems 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.

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.

In recent years, the fields of reconfigurable manufacturing systems, holonic manufacturing systems, and multiagent systems have made technological advances to support the ready reconfiguration of automated manufacturing systems. While these technological advances have demonstrated robust operation and been qualitatively successful in achieving reconfigurability, their ultimate industrial adoption remains limited. Among the barriers to adoption has been the relative absence of formal and quantitative multiagent system design methodologies based on reconfigurability measurement. Hence, it is not clear that the degree to which these designs have achieved their intended level of reconfigurability, which systems are indeed quantitatively more reconfigurable, and how these designs may overcome their design limitations to achieve greater reconfigurability in subsequent design iterations. To our knowledge, this paper is the first multiagent system reference architecture for reconfigurable manufacturing systems driven by a quantitative and formal design approach. It is rooted in an established engineering design methodology called axiomatic design for large flexible engineering systems and draws upon design principles distilled from prior works on reconfigurability measurement. The resulting architecture is written in terms of the mathematical description used in reconfigurability measurement, which straightforwardly allows instantiation for system-specific application.

Scheduling is a fundamental activity in modern shop floors. It is also known to be a highly complex problem which has motivated several sub-formulations and the subsequent models. Traditional approaches, typically enumerative or heuristic, struggle to contain the computation complexity and often present solutions for restricted cases that feature unrealistic assumptions in respect to the system size, flow of products and the system logistics/behaviour. The multiagent-based architecture presented in this paper is aligned with a set of emerging architectures that seek to explore more heterarchical decision and control models to circumvent the limitations of the traditional approaches. The main distinguishing factor of the proposed architecture is that it directly addresses (re)routing/local scheduling of products in plug and produce systems. It does not make any assumptions on the alignment of the orders and, instead, it dynamically handles the potential rescheduling of the orders already on the system based on the available resources, and their state, in a time efficient way. The architecture was tested under a simulation environment, that is geometrically accurate and that supports plug and produce in runtime, to characterize its performance under dynamic conditions.

The current manufacturing scenario is characterized by high market unpredictability. Agility is therefore a central challenge for modern companies that need to understand and be proactive towards their product offer in respect to “what is offered, when it is offered, where, how and by whom” (Brown & Bessant 2003).

The “what” and the “when” are particularly relevant to the research in emerging paradigms as they account for variety, customization and volume; and timing, speed and seasonality (Brown & Bessant 2003).

In this scenario, several design approaches and models have been proposed in the last decade to enable re-configurability and subsequently enhance the companies’ ability to adjust their offer in nature and time.

From a paradigmatic point of view research has concentrated on the organizational structure of the shop-floor and the associated controls aspects. Concepts like Reconfigurable Manufacturing Systems (RMS) (Koren & Shpitalni 2010) and Fractal Factories (FF) (Montreuil 1999) support the physical construction of production systems by regulating their layout and making a few assumptions on their logical organization. On the other hand, concepts like Bionic Manufacturing Systems (BMS)(Ueda 1992), Holonic Manufacturing Systems (HMS)(Van Brussel et al. 1998), Evolvable Assembly Systems (Ribeiro et al. 2010) essentially provide the theoretical guidelines for the logical/computational organization of the system (see (Tharumarajah 1996) for a comparison between BMS, HMS and FF and (Setchi & Lagos 2004) for the rationale supporting the shift from Dedicated Lines to Flexible Manufacturing System and finally RMS).

While these paradigms provide the conceptual framework and the main design guidelines their actual interpretation and implementation has led to a wider set of architectures (Monostori, Váncza & Kumara 2006; Leitão 2009; Parunak 2000; Pěchouček & Mařík 2008).

These architectures align the high-level principles with the technological offer and limitations while seeking to address the re-configurability requirements of (Mehrabi, Ulsoy & Koren 2000; Rösiö & Säfsten 2013):

• module mobility – modules are easy and quick to move and install;
• “diagnosability” – it is quick to identify the sources of quality and reliability problems;
• “integrability” – modules are easy to integrate into the rest of the system.
• “convertibility” – it is easy and quick to switch between existing products and it is easy to adapt the system to future products;
• scalability – it is easy to enlarge and downsize the production system;
• “automatibility” – a dynamic level of automation is enabled;
• modularity – all system elements are designed to be modular;
• customization – the capability and flexibility of the production system is designed according to the products to be produced in the system.

Instant deployment, as addressed in the present chapter directly addresses mobility, “integrability”, “convertibility”, scalability and customization. Mechatronic modularity is a prerequisite and is enforced by the proposed architecture and the considered modular design. “Diagnosability” was not specifically tackled.

In this context, the chapter analyses the agent-based architecture related with the Instantly Deployable Evolvable Assembly System (IDEAS) project that is inspired by the Evolvable Assembly System (EAS) paradigm (Ribeiro et al. 2010) as a mechanism to enable fast deployment of mechatronic modules. EAS advocates the use of process-oriented modules and envisions the production system as a collection of processes and the associated interacting agents.

The architecture and the related test cases are used to draw the main lessons learned in respect to technological and conceptual implications.

In this context, the remainder of this text is organized as follows: section 1.1 discusses the main deployment challenges, section 1.2 details the reference architecture and associated concepts, section 1.3 presents the principal implementation decisions, section 1.4 features the main lessons learned, sections 1.5 discusses the benefits of the proposed approach and finally section 1.6 reflects on the main conclusions.

Agent based systems have been explored, if not practically, at least conceptually, in a wide range of domains. The notion of agent has taken, also, many shapes and meanings according to the application area. These have ranged from pure computational applications, such as UNIX daemons, Internet crawlers, optimization algorithms, etc; to embodied agents as in mobile robotics. The notion of cyber-physical system has been very recently coined to denote the next generation of embedded systems. Unlike an embedded system, a cyber-physical system is designed from scratch to promote the symbiosis and fusion between a physical element, its controller, and its abstract or logical representation/existence. To an enormous extent the concept echoes the idea of embodiment (Pfeifer, Lungarella & Iida 2007), whereby the body shapes the cognitive abilities of its control gear, and self-organization (Holland & Melhuish 1999), in the sense that a resilient whole results from the collective interactions of many parts. Some rather similar principles have been the basis for Holonic Manufacturing Systems (HMS) (Bussmann & Mcfarlane 1999), Bionic Manufacturing Systems (BMS) (Ueda 1992), Evolvable Assembly Systems (EAS) (Onori 2002) and an overwhelming number of industrial agent based architectures that have followed them (Van Brussel et al. 1998; Leitao, Colombo & Restivo 2005; Barata 2003; Lastra 2004; Shen et al. 2006; Marik & Lazansky 2007; Vrba et al. 2011; Leitão 2009; Monostori, Váncza & Kumara 2006).

It is therefore safe to assert that industrial agent systems are a preceding, probably more restricted, case of cyber-physical systems.

Although each application area has its specific challenges arguably, the design, deployment and assessment of industrial agent systems are particularly complex. Given the multidisciplinary nature of today's industrial systems, their cyber-physical realization entails challenges that range from pure computer science and embedded controller design to production optimization and sustainability.

The main challenges comprising the design, deployment and assessment of industrial agent-based systems are therefore examined.

Multiagent Systems (MAS) have been widely known as the base for inherent robust and available systems and there are many characteristics (Wooldridge & Jennings 1994; Wooldridge & Jennings 1995) such as autonomy, social-ability, proactive response, reactivity, self-organization, etc; which have been identified as core ingredients for the MAS reliability.

However, to call "agent" to a software abstraction and create a system based on these abstractions is not a guarantee that the system will exhibit the expected characteristics. Unfortunately this misconception is quite common.

There have been significant international and industrial efforts in addressing the different design, deployment and assessment challenges. The reader is naturally referred to the contents of this book to learn about the latest results and technical details. Previous international projects are not limited to but include: SIRENA - early development of a devices profiles for web services (DPWS) stack (Jammes & Smit 2005; Bohn, Bobek & Golatowski 2006) and subsequent project SODA - focusing on the development of a service based ecosystem using DPWS, Inlife - focusing in service oriented diagnosis of distributed intelligent systems (Barata, Ribeiro & Colombo 2007), SOCRADES - investigating the creation of new methodologies, technologies and tools for the modelling, design, implementation and operation of networked hardware/software systems embedded in smart physical objects (De Souza et al. 2008), AESOP - tackling web service-oriented process monitoring and control (Karnouskos et al. 2010), GRACE - exploring process and quality control integration using a MAS framework (Stroppa et al. 2012) and IDEAS - focusing in instant deployment of agentified components (Ribeiro et al. 2011a).      

The subsequent details are therefore organized to first highlight the commonest structural arrangements considered in current agent architectures and more specifically on bringing some context on their potential applications and limitations. Secondly, since emerging architectures are increasingly inspired by concepts and methods from the complexity sciences, the gaps between them and the concrete instantiation of industrial MAS are discussed. The presentation of the design challenges and opportunities follows as well as the conventional deployment approaches. Finally, the impact of MAS design is discussed from a system validation perspective.

Nowadays manufacturing production systems are becoming more and more responsive in order to succeed in ahighly unstable environment. The capability of a production system to effectively and efficiently adapt and evolveto face the changing requirements – imposed by volatile and dynamic global markets – is a necessary conditionto enable manufacturing enterprises to be agile. Since the agility of a manufacturing enterprise is always limitedby the agility of its own building blocks than it needs to be spread over the whole enterprise including the operationand information technologies (OT/IT). Turning to production systems, one of the significant challenges isrepresented by the possibility to provide easy and rapid (re-)configuration of their internal components and/orprocesses. Innovative technologies and paradigms have been explored during the years that combined with theincreasing advancement in manufacturing technologies enable the implementation of the “plug and produce”paradigm. The “plug and produce” paradigm is the foundation of any agile production system, since to be agile itis inevitably required to reduce the installation and (re-)engineering activities time – changing/adapting the systemto new requirements – while promoting configuration rather than programming. Therefore, the “plug andproduce” paradigm is a necessary but not sufficient condition for implementing agile production systems. Modernproduction systems are typically known for their plethora of heterogeneous component/equipment. In this complexscenario, the implementation of the “plug and produce” paradigm implies the existence of a well-definedontological model to support components/equipment abstraction with the objective to allow interactions,collaboration and knowledge sharing between them. The PRIME semantic language specifies the semanticstructure for the knowledge models and overall system communication language.

Emerging market conditions press current shop floors hard. Mass customization implies that manufacturing system have to be extremely dynamic when handling variety and batch size. Hence, the ability to quickly reconfigure the system is paramount. This involves both the stations that carry out the production processes and the transport system. Traditionally system reconfiguration issues have been approached from a optimization point of view. This means allocating a certain batch of work to specific machines/stations in an optimal schedule. Although in a an abstract way these solutions are elegant and sound sometimes the number and nature of their base assumptions are unrealistic. Approaching the problem from a self-organizing perspective offers the advantage of attaining a fair solution in a concrete environment and as a reaction of the current operational conditions. Even if optimality cannot be ensured the solutions attained and the online re-adjustments render the system generally robust. This works extends the IDEAS Agent Development Environment (IADE) developed in the FP7 Instantly Deployable Evolvable Assembly Systems (IDEAS) project which has demonstrated the basic concepts of the proposed approach. The main architectural changes are presented and justified and the prospects for the analysis and self-organizing control are presented.

This article describes the first version of a tool designed to infer the network characteristics of JADE-based multiagent systems. The rationale behind the tool is that systems in general and multiagent system in particular, often have some hidden dynamics that contribute to the emergence of desired and undesired characteristics. Traditional sniffing tools simply display the message exchange. The presented tool goes therefore beyond simple message sniffing and infers the agents’ network based on the ongoing interactions and codifies it a format suitable for further processing in specialized network analysis tools. In particular the prosped tool identifies the most frequently used communication links and the messages associated with them. To demonstrate the behavior of the tool an exploratory system based on the Evolvable Production System paradigm is discussed and analyzed.

The new market trends are very different, so it iscrucial to the companies improve the tools and capabilities thatallow themselves readjust rapidly and effectively to the newsmarket changes and to the new requirements. In order tofacilitate this process, it is proposed in this paper an agent basedimplementation that can provide to the existent systems thecapacity to quickly adapt and reconfigure using standardtechnology. The proposed framework provides an intelligent toolto autonomously help the configuration when a productionoperator pretends to introduce a new variant of product inruntime and consult important information provided by thesystem to monitor execution.

In recent years, the fields of reconfigurable manufacturing systems, holonic manufacturing systems, and multi-agent systems have made technological advances to support the ready reconfiguration of automated manufacturing systems.  While these technological advances have demonstrated robust operation and been qualitatively successful in achieving reconfigurability, their ultimate industrial adoption remains limited.  Amongst the barriers to adoption has been the relative absence of formal and quantitative multi-agent system design methodologies based upon reconfigurability measurement.  Hence, it is not clear 1.) the degree to which these designs have achieved their intended level of reconfigurability 2.) which systems are indeed quantitatively more reconfigurable and 3.) how these designs may overcome their design limitations to achieve greater reconfigurability in subsequent design iterations.  To our knowledge, this paper is the first multi-agent system reference architecture for reconfigurable manufacturing systems driven by a quantitative and formal design approach.  It is rooted in an established engineering design methodology called axiomatic design for large flexible engineering systems and draws upon design principles distilled from prior works on reconfigurability measurement.  The resulting architecture is written in terms of the mathematical description used in reconfigurability measurement which straightforwardly allows instantiation for system-specific application.

The increasing market fluctuations and customized products demandhave dramatically changed the focus of industry towards organizationalsustainability and supply chain agility. Such critical changes inevitably have adirect impact on the shop-floor operational requirements. In this sense, anumber of innovative production paradigms emerged, providing the necessarytheoretical background to such systems. Due to similarities between innovativemodular production floors and natural complex systems, modern paradigmstheoretically rely on bio-inspired concepts to attain the characteristics ofbiological systems. Nevertheless, during the implementation phase, bio-inspiredprinciples tend to be left behind in favor of more traditional approaches,resulting in simple distributed systems with considerable limitations regardingscalability, reconfigurable ability and distributed problem resolution.This paper analyzes and presents a brief critical review on how bio-inspiredconcepts are currently being explored in the manufacturing environment, in anattempt to formulate a number of challenges and properties that need to beconsidered in order to implement manufacturing systems that closely follow thebiological principles and consequently present overall characteristics ofcomplex natural systems.

Sustainability is currently one of the biggest challenges and driver of manufacturing industry. Nevertheless, with the decrease of product life cycles, the consumption of raw materials as well as the obsolescence of production systems increases. In this sense, agile shop-floors that enact companies with the ability to quickly reconfigure their shop-floors by deploying or removing modules are the key for sustainable industrial development. This paper attempts to characterize an innovative approach that relies on bio-inspired concepts as the main control mechanism, in order to foster sustainability by attaining the necessary shop-flooragility. Furthermore an experimental setup is presented and the results are analysed, in order to understand the influence and impact of the main properties that characterize the approach towards the system performance.

Computational and communication capabilities are increasingly being used in all devices. In the production context this leads to the generation of massive amounts of data that are rarely proficuously used. More particularly the application of data-mining techniques to infer knowledge from systems’ operation to improve its design decisions remains fairly unexplored. This article presents an approach to extract system design and configuration rules from Evolvable Production Systems. Furthermore it provides the empirical results from two test-cases that support the hypothesis that a simulation-data-mining approach can help reducing the complexity of the work carried by system designers and production managers.

Simulation has played an important role alongthe years to predict systems’ behaviour before theirdeployment. In the case of self-organising mechatronic systemssimulation tools can help researchers and practitionersunderstanding the full potential of the solution as well as itsunderlying limitations. Self-organising mechatronic systemshave passed a feasibility study and presented promisingresults. However they are rarely explored in industry in partdue to the lack of methods to support their design andconfiguration and the difficulty to predict the systems’behaviour before their deployment. Given the cost anddevelopment time associated with building self-organisingmechatronic systems this research problem has been left quiteunattended. In this article we present a tool that enables thecreation and simulation of Evolvable Production Systems andtheir self-organising behaviour. The generated operationalresults can posteriorly be used to analyse the suitability ofdifferent design and configuration alternatives for differentproduct types and volumes.

The research and development of self-organising mechatronic systems has been a hot topic in the past 10 years which conducted to very promising results in the close past. The proof of concept attained in IDEAS project that plug&produce can be achieved in these systems opens up new research horizons on the topics of system design, configuration and performance evaluation. These topics need to consider that the systems are no longer static prototypes but instead several distributed components that can be added and removed in runtime. The distribution of modules in the system and their inherent connections will then potentially affect the system’s global behaviour. Hence it is vital to understand the impact on performance as the system endures changes that affect its topology. This article presents an exploratory test case that shows that as a system evolves (and the nature of the network of its components changes) the performance of the system is necessarily affected in a specific direction. This performance landscape is necessarily complex and very likely nonlinear. Simulation plays therefore an important role in the study of these systems as a mean to generate data that can be later on used to generate macro level knowledge that may act as a guideline to improve both design and configuration.

JADE is a multiagent-based framework widely used for prototyping agent-oriented mechatronic architectures. JADE's messaging performance is normally rated unsuitable for real time operation in a mechatronic context and there are a few papers in the literature backing up this claim. The author's share this view however the main purpose of this paper is to highlight that distinct message exchange coding patters influence the round trip time (RTT) of the messages quite substantially. The paper presents a comparative performance analysis, based on the RTT, of these coding patterns. The majority of the published tests is focused in the evaluation of the RTT when pairs of communicating agents are added to the platform. The analysis presented in this work is focused on the efficiency of each method as the responder agent receives messages continuously.

The emergence of modern manufacturing paradigms together with the growing interest on distributed architectures has been increasing the use of biologically inspired solutions. However, somehow along the way, developed approaches have converged towards more traditional systems where the physical and logical decoupled nature of the system has been partially lost. In this context, the presented work aims to introduce and analyse a new fully physically and logically decoupled bio-inspired self-organising approach that tries to bring to the mechatronic-agent based manufacturing architectures the dynamics of biological systems. Furthermore, the manufacturing systems are approached from a bottom-up perspective in an attempt to reduce the specification of the production processes to the minimum.

With the emergence of new modern manufacturing paradigms new concepts, originally from the complexity sciences started to be introduced in the manufacturing systems, rendering traditional control approaches insufficient. Therefore, new approaches were developed, supported by the modern manufacturing paradigms bio-inspired background. However, somehow along the way the physical and logical nature of the system was partially lost, leading to the convergence of approaches towards more traditional systems, 'neglecting' their bio-inspired principles. With the present work the authors aim to introduce and analyse two new different self-organising approaches that try to bring the focus of manufacturing systems, again to the bio-inspired principles. For this purpose, in the context of this work, manufacturing systems are approached from a bottom-up perspective, in an attempt to reduce the specification of the production processes to the minimum and foster the production emergence. A test case is considered, to draw initial conclusions.

Multiagent system’s applications in industry have been widely investigated to support emerging production paradigms and models. One of the key concepts of these approaches is the utilization of a highly dynamic environment whereby intelligent agents are able to take autonomous decisions and self-organize to circumvent production disturbances. Deployment is a fundamental activity in these systems and entails significant technical and conceptual challenges. There are only a few documented deployment platforms for agents in a mechatronic context. This paper presents the deployment philosophy developed and tested under the framework of the FP7 IDEAS project.

This paper details the FP7 IDEAS project multiagent mechatronic architecture. In particular the technical details that support plug and produce of individual modules or stations are discussed. To highlight the generic approach of the core agent-based mechatronic platform an example where a virtual station is introduced and seamlessly interacts with the physical systems is highlighted. This example has, therefore, the dual purpose of showing the generic aspects of the reference architecture, and its implementation, and describing the technicalities that support the introduction of virtual components in the "loop" of the physical systems which goes far beyond mere simulation of assembly systems. This flexibility opens wide the door for improved system analysis where just specific components have to be simulated whereas the remaining dynamic is the one of the real system.

There is a substantial difference between traditional industrial systems and systems resultant from emerging production paradigms in terms of both conceptualization and implementation. Backwards compatibility is fundamental in assessing the potential transitional period whereby legacy technology will operate under the conceptual framework of modern approaches. However, before this point can be reached it is necessary to further investigate, assess and quantify the behavior of modern approaches that increasingly rely in self-organization. This article's goal is to give an overview of the behavioral assessment problematic, in the context of self-organizing mechatronic systems, and provide a discussion on opportunities to explore rule-extraction techniques to better understand the influence of design and configuration on the overall system behavior. Although the discussion widely applies to almost all emerging production paradigms the Evolvable Production System paradigm is used as case to bring context and clarity to the discussion.

This paper discusses the main problematic, misunderstandings and gaps associated with the development of self-organizing multiagent mechatronic systems. The paper reflects the authors' experience in designing and implementing Multiagent Mechatronic Systems. It also partly addresses the work developed under the FP7 IDEAS project (rated an EU FP7 success story) as a clarifying, but not unique, example. In this respect the paper presents a critical overview on how the existing technology is close to support the automation concepts envisioned by emerging production paradigms that rely on Emergence and Self-Organization as main constructs of the system and why technology alone should be understand as only a mean and not a end.

A cornerstone of modern production paradigms is the encapsulation of heterogeneity and system complexity. This functional encapsulation is supported by distinct architectural building blocks that once instantiated abstract, manage and control a specific system. Modern production approaches rely in semi hierarchical architectures to organize the interactions between these blocks. In runtime this specific structure promotes a self-organizing response resulting from concurrent interaction between the system's components. This paper simulates the execution of processes in a network of mechatronic agents comprising products, processes' abstractions and resources and analyses the impact of the typical network structure envisioned by modern production paradigms in respect to the time consumed in the production process.

Esta tese aborda a problemática da execução de diagnóstico em instalações industriais complexas que são compostas por um elevado número de componentes que interagem entre si no âmbito do processo de produção.

Apesar da abordagem apresentada ao problema ser geral e aplicável a várias classes de sistemas ligados em rede, o domínio dos Sistemas Evolutivos de Produção e os Sistemas Evolutions de Assembly (EPS/EAS) é utilizado como a base sobre a qual o sistema de diagnóstico proposto é implementado.

Os Sistemas Evolutivos de Produção são um paradigma de produção recente e multidisciplinar cujo sistema de controlo é baseado em arquitecturas multiagente que denotam um elevado grau de desacoplamento entre os módulos que constituem o sistema. Esta é uma característica fundamental deste tipo de sistemas que garante uma elevada robustez e tolerância a falhas. Contudo, a tolerância a falhas, por si só não é suficiente para garantir o correcto funcionamento dos sistemas. Especialmente nos modernos paradigmas de produção os aspectos de monitorização e diagnóstico exercem uma acção regulatória fundamental. Contudo no sentido de não corromper a lógica de controlo que é altamente distribuída e que, desta forma, permite a fácil reconfiguração do sistema sem limites de escalablidade, abordagens como a proposta nesta tese são fundamentais.

Neste contexto, o sistema proposto assume que a informação de diagnóstico proveniente dos métodos de diagnóstico existentes ao nível de cada um dos módulos do sistema deve ser harmonizada, transportada e considerada numa perspectiva de rede.

Desta forma torna-se possível efectuar uma análise holística do problema de diagnóstico nomeadamente do sub-problema da propagação de falhas.

Para não corromper a lógica de reconfiguração existente a abordagem proposta explora mecanismos de auto-organização e emergência ao nível de cada componente do sistema para fazer emergir o diagnóstico, focado nos aspectos da propagação de falhas, apenas explorando  informação e interações locais entre os componentes.

Assim garante-se a distribuição do sistema de diagnóstico, eliminando todo um conjunto de problemas associados a abordagem centralizadas (mais tradicionais) nomeadamente: escalabilidade, dependênia de um ponto único sujeito a falhas e os crescentes requisitos de performance computacional e complexidade algorítmica que acompanham a expansão do sistema.

Neste contexto, o presente trabalho expõe toda a problemática associada aos sistemas referidos, mostrando estatisticamente a relevância de considerar os aspectos da propagação de falhas e sugere, com validação, uma solução concreta para os desafios apresentados.

With the systematic implantation and acceptance of IT in the shop-floor a wide range of production paradigms, relying in open interoperable architectures, have been developed. Exploring these technological novelties, they promise to revolutionize the way current plant floors operate and react to emerging opportunities and disturbances. There is a high interest of module providers in the adoption of these open mechatronic architectures as they may provide a new business model where the automation solution can be easily tailored for each customer in due time and ships with a significant part of the control solution (high added value). Final customers on their side can contract operating hours rather than buying modules. Moreover, the automation solution can be swiftly modified to meet changing requirements.

The necessary increase in the number of distributed and autonomous components that interact in the execution of processes implies that new diagnostic approaches should be developed to tackle the network layer of these highly dynamic systems. In fact fault propagation events can be harder to understand and can affect the system in unpredictable and pervasive ways.

Following this rationale the paper presents a potential diagnostic solution that targets multiagent-based mechatronic systems where their components are highly decoupled from a control point of view. The diagnostic architecture presented tackles the problem of fault propagation while preserving the decoupled nature of the Mechatronic Agent concept. In this context the diagnostic system explores self-organization to enact an emergent response that denotes macro-level coherence. The system's response is the result of an individual probabilistic diagnostic inference based on Hidden Markov Models that capture the propagating nature of a failure.

The validation results of the proposed diagnostic approach are detailed for the system's response in simulation (highlighting the main variables that affect the performance of the system) and compared to the system applied to a pilot assembly cell. The simulation model and the performance metrics considered are detailed and discussed along with the main implementation details.

In recent years a set of production paradigms has emerged to circumvent pressing production challenges. In particular the ability of composing lower order components to form higher order entities with more complex functionalities and reuse them to build yet more complex system has been widely explored. However material handling aspects have been tackled independently of the control/reconfiguration architecture. Traditionally transport resources have been assumed passive and the organization of the production has been treated as a scheduling problems that often ignores disturbances in the transport system. This renders most of the scheduling approaches too theoretical to be applied in real production scenarios and has motivated researchers to pursuit alternative approaches to this sort of production organization problems. In this paper a technical agent-based architecture that integrates control/reconfiguration aspects with material handling is detailed. The architecture supports the simultaneous usage of conveyor and AGV-based material handling systems and the addition of production or material handling resources is handled in a transparent way through careful generic interaction design. Overall the proposed architecture can be applied to any mechatronic system favouring re-configuration and minimizing reprogramming.

There has been an increasing interest from industry in distributed architectures since they promote a plug-and-produce and robust environment, where adaptability and fault tolerance are native. Much research has been conducted in this field mainly supported by multi-agent and service oriented technologies. Nevertheless the retrieval and visualization of information in distributed systems is a relatively unexplored area. Although the dynamic nature of the multi-agent systems allows gathering information in a prompt manner, doing so might affect the performance of the mechatronic agents. In this sense, the present paper details the architecture of a visualization tool that introduces a reliable but non-invasive approach to retrieve data from distributed platforms as well as a new way to visualize and interpret the information gathered from mechatronic based systems.

The NEMO&CODED (NEMO) project targets the development of a standard-based software infrastructure, aiming to provide the appropriate support to manage energy-related devices considering an environment where energy is generated, stored, distributed, and consumed in a rational and environmentally correct way. This paper presents and discusses the results achieved so far by the project, with special emphasis on the implementation of a web services-based wrapper to seamlessly integrate new energy sources (renewable ones) into the NEMO network. Such a wrapper relies on the adoption of two standards, namely DPWS and the IEC 61850 series. The former handles service oriented related aspects whilst the latter helps to support the interoperability of Intelligent Electronic Devices (IEDs), through the use of the Abstract Communication Service Interface (ACSI) and the Substation Configuration Language (SCL). Main problems raised and solutions found are also included in the paper as well as the future steps to be performed.

Recent shop floor paradigms and approaches increasingly advocate the use of distributed systems and architectures. Plug-ability, Fault Tolerance, Robustness and Preparedness are characteristics believed to emerge by instantiation of these fundamentally new design approaches. However these features, when effectively present, often come at the cost of a greater system complexity. Enclosed in this complexity increase is a plethora on unforeseen interactions between the entities (modules) that compose the system. The purpose of this paper is, in this context, twofold: to validate a fault propagation model in random networks (that simulate the connectivity of modular shop floor systems) and assess the performance of two diagnostic approaches to expose the impact of relying in local or global information.

The present article describes the development of a standard-based software infrastructure, supported by the Service Oriented Architecture paradigm, for the management of complex distributed energy systems where efficient energy production, distribution and consumption are considered. The work has been developed on the context of the NEMO&CODED project (NEMO) and focuses on NEMO's architectural aim to enable seamless device integration, with plug and play features and vendor independent concepts, using the IEC 61850 ACSI model and services and adopting DPWS as Specific Communication Service Mapping.