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Ribeiro, L. & Linder, P. (2016). Hardware Abstraction Layer for JAVA-based agents. In: IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society: . Paper presented at IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society (pp. 4896-4901). Institute of Electrical and Electronics Engineers (IEEE).
Open this publication in new window or tab >>Hardware Abstraction Layer for JAVA-based agents
2016 (English)In: IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society, Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 4896-4901Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016
Keyword
Java;cyber-physical systems;hardware-software codesign;manufacturing systems;production engineering computing;software agents;HAL;Java-based agents;actuation;controlling layers;cyber-physical industrial systems;hardware abstraction layer;harmonized cyber interface;programming languages;sensing devices;software-hardware harmonization;system operability;Automation;Computer architecture;Context;Hardware;Java;Libraries;XML
National Category
Computer Systems
Identifiers
urn:nbn:se:liu:diva-135778 (URN)10.1109/IECON.2016.7793559 (DOI)000399031205027 ()978-1-5090-3475-8 (ISBN)978-1-5090-3474-1 (ISBN)
Conference
IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society
Available from: 2017-03-21 Created: 2017-03-21 Last updated: 2017-06-01Bibliographically approved
Leitao, P., Karnouskos, S., Ribeiro, L., Lee, J., Strasser, T. & Colombo, A. W. (2016). Smart Agents in Industrial Cyber-Physical Systems. Proceedings of the IEEE, 104(5), 1086-1101.
Open this publication in new window or tab >>Smart Agents in Industrial Cyber-Physical Systems
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2016 (English)In: Proceedings of the IEEE, ISSN 0018-9219, E-ISSN 1558-2256, Vol. 104, no 5, p. 1086-1101Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2016
Keyword
Artificial intelligence; cyber-physical systems (CPSs); internet of things; multiagent systems (MASs); service-oriented architectures
National Category
Mechanical Engineering
Identifiers
urn:nbn:se:liu:diva-128744 (URN)10.1109/JPROC.2016.2521931 (DOI)000374864600016 ()
Available from: 2016-05-31 Created: 2016-05-30 Last updated: 2016-06-22
Farid, A. M. & Ribeiro, L. (2015). An Axiomatic Design of a Multiagent Reconfigurable Mechatronic System Architecture. IEEE Transactions on Industrial Informatics, 11(5), 1142-1155.
Open this publication in new window or tab >>An Axiomatic Design of a Multiagent Reconfigurable Mechatronic System Architecture
2015 (English)In: IEEE Transactions on Industrial Informatics, ISSN 1551-3203, E-ISSN 1941-0050, Vol. 11, no 5, p. 1142-1155Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2015
Keyword
Axiomatic design; multiagent system; reconfigurability; reconfigurable manufacturing systems
National Category
Mechanical Engineering
Identifiers
urn:nbn:se:liu:diva-122206 (URN)10.1109/TII.2015.2470528 (DOI)000362356000015 ()
Available from: 2015-10-26 Created: 2015-10-23 Last updated: 2017-12-01
Ribeiro, L., Rocha, A., Veiga, A. & Barata, J. (2015). Collaborative routing of products using a self-organizing mechatronic agent framework: A simulation study. Computers in industry (Print), 68, 27-39.
Open this publication in new window or tab >>Collaborative routing of products using a self-organizing mechatronic agent framework: A simulation study
2015 (English)In: Computers in industry (Print), ISSN 0166-3615, E-ISSN 1872-6194, Vol. 68, p. 27-39Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Elsevier, 2015
Keyword
Multiagent systems; Material handling; Self-organization; Scheduling; Product oriented systems; Mechatronics
National Category
Mechanical Engineering
Identifiers
urn:nbn:se:liu:diva-117369 (URN)10.1016/j.compind.2014.12.003 (DOI)000352050300003 ()
Available from: 2015-04-24 Created: 2015-04-24 Last updated: 2017-12-04
Ribeiro, L., Barata, J., Onori, M. & Hoos, J. (2015). Industrial Agents for the Fast Deployment of Evolvable Assembly Systems (1st ed.ed.). In: Paulo Leitao and Stamatis Karnouskos (Ed.), Industrial Agents: Emerging Applications of Software Agents in Industry (pp. 301-321). Amsterdam, Netherlands: Elsevier.
Open this publication in new window or tab >>Industrial Agents for the Fast Deployment of Evolvable Assembly Systems
2015 (English)In: Industrial Agents: Emerging Applications of Software Agents in Industry / [ed] Paulo Leitao and Stamatis Karnouskos, Amsterdam, Netherlands: Elsevier, 2015, 1st ed., p. 301-321Chapter in book (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Amsterdam, Netherlands: Elsevier, 2015 Edition: 1st ed.
Keyword
Multiagent-based Industrial System, Plug and Produce, Intelligent Automation
National Category
Electrical Engineering, Electronic Engineering, Information Engineering Computer Systems Control Engineering Embedded Systems Other Engineering and Technologies not elsewhere specified Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:liu:diva-121688 (URN)10.1016/B978-0-12-800341-1.00017-6 (DOI)978-0-12-800341-1 (ISBN)
Available from: 2015-10-01 Created: 2015-10-01 Last updated: 2015-10-07Bibliographically approved
Ribeiro, L. (2015). The design, deployment, and assessment of industrial agent systems (1st ed.ed.). In: Paulo Leitao and Stamatis Karnouskos (Ed.), Industrial Agents: Emerging Applications of Software Agents in Industry (pp. 45-63). Amsterdam, Netherlands: Elsevier.
Open this publication in new window or tab >>The design, deployment, and assessment of industrial agent systems
2015 (English)In: Industrial Agents: Emerging Applications of Software Agents in Industry / [ed] Paulo Leitao and Stamatis Karnouskos, Amsterdam, Netherlands: Elsevier, 2015, 1st ed., p. 45-63Chapter in book (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Amsterdam, Netherlands: Elsevier, 2015 Edition: 1st ed.
Keyword
Multiagent-based Industrial System, Design, Assessment, Validation
National Category
Engineering and Technology Embedded Systems Computer Systems Other Electrical Engineering, Electronic Engineering, Information Engineering Other Mechanical Engineering Other Engineering and Technologies not elsewhere specified
Identifiers
urn:nbn:se:liu:diva-121685 (URN)10.1016/B978-0-12-800341-1.00003-6 (DOI)978-0-12-800341-1 (ISBN)
Available from: 2015-10-01 Created: 2015-10-01 Last updated: 2015-10-07Bibliographically approved
Di Orio, G., Rocha, A., Ribeiro, L. & Barata, J. (2015). The PRIME Semantic Language: Plug and Produce in Standard- based Manufacturing Production Systems. In: Proceedings of the Flexible Automation and Intelligent Manufacturing Conference: . Paper presented at The International Conference on Flexible Automation and Intelligent Manufacturing (FAIM2015), Wolverhampton, UK, 23 - 26 June 2015. .
Open this publication in new window or tab >>The PRIME Semantic Language: Plug and Produce in Standard- based Manufacturing Production Systems
2015 (English)In: Proceedings of the Flexible Automation and Intelligent Manufacturing Conference, 2015Conference paper, Published paper (Other academic)
Abstract [en]

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.

National Category
Control Engineering Embedded Systems Other Electrical Engineering, Electronic Engineering, Information Engineering Other Engineering and Technologies not elsewhere specified Computer Systems
Identifiers
urn:nbn:se:liu:diva-121691 (URN)
Conference
The International Conference on Flexible Automation and Intelligent Manufacturing (FAIM2015), Wolverhampton, UK, 23 - 26 June 2015
Available from: 2015-10-01 Created: 2015-10-01 Last updated: 2015-10-07Bibliographically approved
Rocha, A., Ribeiro, L. & Barata, J. (2014). A Multi Agent Architecture to Support Self-organizing Material Handling. In: Luis M. Camarinha-Matos, Nuno S. Barrento, Ricardo Mendonça (Ed.), Technological Innovation for Collective Awareness Systems: 5th IFIP WG 5.5/SOCOLNET Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2014, Costa de Caparica, Portugal, April 7-9, 2014. Proceedings (pp. 93-100). Springer Berlin/Heidelberg, 423.
Open this publication in new window or tab >>A Multi Agent Architecture to Support Self-organizing Material Handling
2014 (English)In: Technological Innovation for Collective Awareness Systems: 5th IFIP WG 5.5/SOCOLNET Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2014, Costa de Caparica, Portugal, April 7-9, 2014. Proceedings / [ed] Luis M. Camarinha-Matos, Nuno S. Barrento, Ricardo Mendonça, Springer Berlin/Heidelberg, 2014, Vol. 423, p. 93-100Chapter in book (Other academic)
Abstract [en]

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.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2014
Series
IFIP Advances in Information and Communication Technology, ISSN 1868-4238 ; 423
Keyword
Multi Agent; Transport System; Material Handling; Self- Organization; Load Balancing; Architecture
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:liu:diva-109299 (URN)10.1007/978-3-642-54734-8_11 (DOI)978-3-642-54733-1 (ISBN)978-3-642-54734-8 (ISBN)
Available from: 2014-08-11 Created: 2014-08-11 Last updated: 2014-09-08Bibliographically approved
Ribeiro, L., Ferreira, J. D., Moura, C. & Barata, J. (2014). A network inference tool for JADE-based systems. In: : . Paper presented at 12th IEEE International Conference on Industrial Informatics (INDIN 2014), 27-30 July 2014, Porto Alegre, Brazil. IEEE conference proceedings.
Open this publication in new window or tab >>A network inference tool for JADE-based systems
2014 (English)Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2014
Series
IEEE International Conference on Industrial Informatics INDIN, ISSN 1935-4576
Keyword
agents, self-organization, analysis tool, evolvable production systems
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:liu:diva-109303 (URN)10.1109/INDIN.2014.6945546 (DOI)000349558700060 ()
Conference
12th IEEE International Conference on Industrial Informatics (INDIN 2014), 27-30 July 2014, Porto Alegre, Brazil
Available from: 2014-08-11 Created: 2014-08-11 Last updated: 2015-03-13
Rocha, A., Di Orio, G., Barata, J., Ribeiro, L., Antzoulatos, N., Castro, E., . . . Ratchev, S. (2014). An Agent Based Framework to Support Plug And Produce. In: Industrial Informatics (INDIN), 2014 12th IEEE International Conference on: . Paper presented at 12th IEEE International Conference on Industrial Informatics (INDIN 2014), 27-30 July 2014, Porto Alegre, Brazil (pp. 504-510). IEEE.
Open this publication in new window or tab >>An Agent Based Framework to Support Plug And Produce
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2014 (English)In: Industrial Informatics (INDIN), 2014 12th IEEE International Conference on, IEEE , 2014, p. 504-510Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
IEEE, 2014
Keyword
Plug and produce; Multiagent; Flexibility; Production, Standard Technology
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:liu:diva-109305 (URN)10.1109/INDIN.2014.6945565 (DOI)2-s2.0-84914145650 (Scopus ID)978-1-4799-4905-2 (ISBN)
Conference
12th IEEE International Conference on Industrial Informatics (INDIN 2014), 27-30 July 2014, Porto Alegre, Brazil
Projects
Plug and produce intelligent multi-agent environment based on standard technology (PRIME FP7)
Available from: 2014-08-11 Created: 2014-08-11 Last updated: 2015-04-21
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-0248-8180

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