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- Ribeiro, Luis, et al.
(författare)
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Industrial Agents for the Fast Deployment of Evolvable Assembly Systems
- 2015. - 1st ed.
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Ingår i: Industrial Agents. - Amsterdam, Netherlands : Elsevier. - 9780128003411 ; , s. 301-321, s. 301-322
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Bokkapitel (refereegranskat)abstract
- 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.
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| 2. |
- Ribeiro, Luis
(författare)
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The design, deployment, and assessment of industrial agent systems
- 2015. - 1st ed.
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Ingår i: Industrial Agents. - Amsterdam, Netherlands : Elsevier. - 9780128003411 ; , s. 45-63
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Bokkapitel (refereegranskat)abstract
- 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.
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