Abstract
Worldwide systems are increasingly growing into unprecedented complexity levels. This increase of system complexity has to be tackled with new control approaches, where decentralization is playing an important role and particularly the Multi-Agent Systems and Holonic Systems. Despite the benefits of this distribution, new problems arise such as the need for entities coordination. This paper proposes an innovative holonic multi-agent system architecture, named ADACOR2, which sets foundation on the already proved ADACOR architecture. This new control architecture is empowered by a two vector self-organization, called behavioural and structural self-organization. This paper describes the structural self-organization vector, particularizing the need for embedding in it learning techniques and nervousness stabilizer. A futuristic test bed, inspired in a real flexible-manufacturing system, is used to demonstrate the benefits of this vector in the architecture. © 2013 Springer-Verlag.

Abstract
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.

Abstract
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.

Abstract
The Industry 4.0 advent, advocating the digitalization and transformation of current production systems towards the factories of future, is introducing significant social and technological challenges. Cyber-physical systems (CPS) can be used to realize these Industry 4.0 compliant systems, integrating several emergent technologies, such as Internet of Things, big data, cloud computing and multi-agent systems. The paper analyses the advantages of using biological inspiration to empower CPS, and particularly those developed using distributed and intelligent paradigms such as multi-agent systems technology. For this purpose, the self-organization capability, as one of the main drivers in this industrial revolution is analysed, and the way to translate it to solve complex industrial engineering problems is discussed. Its applicability is illustrated by building a self-organized cyber-physical conveyor system composed by different individual modular and intelligent transfer modules.

Abstract
This paper describes the development of a multi-agent 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.

Abstract
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 methodologies towards the continuous system improvement. This paper introduces the main principles of a multi-agent CPS aiming the application of ZDM in multi-stage production systems, which is being developed under the EU H2020 GOOD MAN project. In particular, this paper describes the MAS architecture that allows the distributed data collection and the balancing of the data analysis for monitoring and adaptation among cloud and edge layers, to enable the earlier detection of process and product variability, and the generation of new optimized knowledge by correlating the aggregated data.