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Dr. Andre Dionisio Rocha
Department of Electrical and Computer Engineering (DEEC), NOVA University of Lisbon / UNINOVA-CTS, 1099-085 Lisbon, Portugal

Basic Info


Research Keywords & Expertise

1 Smart Manufacturing
1 digital twins
1 Distributed control systems
1 Cyber-Physical Production Systems
1 Industrial cyber-physical systems

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Smart Manufacturing
digital twins
Cyber-Physical Production Systems

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Short Biography

Andre Dionisio Rocha received both his M.Sc. and Ph.D. in electrical and computer engineering from the NOVA University of Lisbon. He is currently a professor at NOVA University of Lisbon and Senior Researcher at UNINOVA institute. He has participated in several research projects, most of them at the European level. His research interests include the design of cyber-physical production systems and smart manufacturing solutions. He has published several scientific contributions in scientific journals and international conferences and is highly involved and active in IEEE activities. Currently, he is member of two IEEE-IES Technical Committees (TC-ICPS and TC-IA).

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Short communication
Published: 17 July 2021 in Manufacturing Letters
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The advantages of using collaborative and distributed systems to control and optimise industrial environments have been explored in recent years. However, the self-organised and dynamic approaches that deliver these same advantages bring challenges associated with the long-term unpredictability of these approaches. The proposed work aims to present a framework that integrates a Digital Twin-based Optimiser within these systems to predict the system's evolution and reconfigure it if required. Digital Twins can be an essential advantage in adopting these systems due to the possibility of carrying out simulations at an accelerated speed and reconfiguring the self-organised control system.

ACS Style

Andre Dionisio Rocha; Jose Barata. Digital twin-based optimiser for self-organised collaborative cyber-physical production systems. Manufacturing Letters 2021, 29, 79 -83.

AMA Style

Andre Dionisio Rocha, Jose Barata. Digital twin-based optimiser for self-organised collaborative cyber-physical production systems. Manufacturing Letters. 2021; 29 ():79-83.

Chicago/Turabian Style

Andre Dionisio Rocha; Jose Barata. 2021. "Digital twin-based optimiser for self-organised collaborative cyber-physical production systems." Manufacturing Letters 29, no. : 79-83.

Journal article
Published: 17 June 2021 in Energies
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Industrial environments are heterogeneous systems that create challenges of interoperability limiting the development of systems capable of working collaboratively from the point of view of machines and software. Additionally, environmental issues related to manufacturing systems have emerged during the last decades, related to sustainability problems faced in the world. Thus, the proposed work aims to present an interoperable solution based on events to reduce the complexity of integration, while creating energetic profiles for the machines to allow the optimization of their energy consumption. A publish/subscribe-based architecture is proposed, where the instantiation is based on Apache Kafka. The proposed solution was implemented in two robotic cells in the automotive industry, constituted by different hardware, which allowed testing the integration of different components. The energy consumption data was then sent to a Postgres database where a graphical interface allowed the operator to monitor the performance of each cell regarding energy consumption. The results are promising due to the system’s ability to integrate tools from different vendors and different technologies. Furthermore, it allows the possibility to use these developments to deliver more sustainable systems using more advanced solutions, such as production scheduling, to reduce energy consumption.

ACS Style

Andre Rocha; Nelson Freitas; Duarte Alemão; Magno Guedes; Renato Martins; José Barata. Event-Driven Interoperable Manufacturing Ecosystem for Energy Consumption Monitoring. Energies 2021, 14, 3620 .

AMA Style

Andre Rocha, Nelson Freitas, Duarte Alemão, Magno Guedes, Renato Martins, José Barata. Event-Driven Interoperable Manufacturing Ecosystem for Energy Consumption Monitoring. Energies. 2021; 14 (12):3620.

Chicago/Turabian Style

Andre Rocha; Nelson Freitas; Duarte Alemão; Magno Guedes; Renato Martins; José Barata. 2021. "Event-Driven Interoperable Manufacturing Ecosystem for Energy Consumption Monitoring." Energies 14, no. 12: 3620.

Journal article
Published: 03 March 2021 in Applied Sciences
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During the last years, several research activities and studies have presented the possibility to perform manufacturing control using distributed approaches. Although these new approaches aim to deliver more flexibility and adaptability to the shop floor, they are not being readily adopted and utilised by the manufacturers. One of the main challenges is the unpredictability of the proposed solutions and the uncertainty associated with these approaches. Hence, the proposed research aims to explore the utilisation of Digital Twins (DTs) to predict and understand the execution of these systems in runtime. The Fourth Industrial Revolution is leading to the emergence of new concepts amongst which DT stand out. Given their early stage, however, the already existing implementations are far from standardised, meaning that each practical case has to be analysed on its own and solutions are often created from scratch. Taking the aforementioned into account, the authors suggest an architecture that enables the integration between a previously designed and developed agent-based distributed control system and its DT, whose implementation is also provided in detail. Furthermore, the digital model’s calibration is described jointly with the careful validation process carried out. Thanks to the latter, several conclusions and guidelines for future implementations were possible to derive as well.

ACS Style

Gonçalo Roque Rolo; Andre Dionisio Rocha; João Tripa; Jose Barata. Application of a Simulation-Based Digital Twin for Predicting Distributed Manufacturing Control System Performance. Applied Sciences 2021, 11, 2202 .

AMA Style

Gonçalo Roque Rolo, Andre Dionisio Rocha, João Tripa, Jose Barata. Application of a Simulation-Based Digital Twin for Predicting Distributed Manufacturing Control System Performance. Applied Sciences. 2021; 11 (5):2202.

Chicago/Turabian Style

Gonçalo Roque Rolo; Andre Dionisio Rocha; João Tripa; Jose Barata. 2021. "Application of a Simulation-Based Digital Twin for Predicting Distributed Manufacturing Control System Performance." Applied Sciences 11, no. 5: 2202.

Review
Published: 02 March 2021 in Applied Sciences
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The recent advances in technology and the demand for highly customized products have been forcing manufacturing companies to adapt and develop new solutions in order to become more dynamic and flexible to face the changing markets. Manufacturing scheduling plays a core role in this adaptation since it is crucial to ensure that all operations and processes are running on time in the factory. However, to develop robust scheduling solutions it is necessary to consider different requirements from the shopfloor, but it is not clear which constraints should be analyzed and most research studies end up considering very few of them. In this review article, several papers published in recent years were analyzed to understand how many and which requirements they consider when developing scheduling solutions for manufacturing systems. It is possible to understand that the majority of them are not able to be adapted to real systems since some core constraints are not even considered. Consequently, it is important to consider how manufacturing scheduling solutions can be structured to be adapted effortlessly for different manufacturing scenarios.

ACS Style

Duarte Alemão; André Rocha; José Barata. Smart Manufacturing Scheduling Approaches—Systematic Review and Future Directions. Applied Sciences 2021, 11, 2186 .

AMA Style

Duarte Alemão, André Rocha, José Barata. Smart Manufacturing Scheduling Approaches—Systematic Review and Future Directions. Applied Sciences. 2021; 11 (5):2186.

Chicago/Turabian Style

Duarte Alemão; André Rocha; José Barata. 2021. "Smart Manufacturing Scheduling Approaches—Systematic Review and Future Directions." Applied Sciences 11, no. 5: 2186.

Conference paper
Published: 16 April 2019 in Lecture Notes in Control and Information Sciences
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The production scheduling has attracted a lot of researchers for many years, however most of the approaches are not targeted to deal with real manufacturing environments, and those that are, are very particular for the case study. It is crucial to consider important features related with the factories, such as products and machines characteristics and unexpected disturbances, but also information such as when the parts arrive to the factory and when should be delivered. So, the purpose of this paper is to identify some important characteristics that have been considered independently in a lot of studies and that should be considered together to develop a generic scheduling framework to be used in a real manufacturing environment.

ACS Style

Duarte Alemão; André Dionísio Rocha; Jose Barata. Production Scheduling Requirements to Smart Manufacturing. Lecture Notes in Control and Information Sciences 2019, 227 -237.

AMA Style

Duarte Alemão, André Dionísio Rocha, Jose Barata. Production Scheduling Requirements to Smart Manufacturing. Lecture Notes in Control and Information Sciences. 2019; ():227-237.

Chicago/Turabian Style

Duarte Alemão; André Dionísio Rocha; Jose Barata. 2019. "Production Scheduling Requirements to Smart Manufacturing." Lecture Notes in Control and Information Sciences , no. : 227-237.

Conference paper
Published: 01 September 2018 in 2018 International Conference on Intelligent Systems (IS)
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The proliferation of Information and Communication Technologies allowed the development of new solutions to be applied at the shop-floor and all the tools which helps the manufacturers. Hence, new solutions such as cyber-physical production systems, data analytics and knowledge management were developed and proposed to solve the well-known issues, such as quality control in multistage manufacturing systems. However, those solutions can only have a small contribution in solving that issues compared to an optimized and fully integrated approach. To allow the development of a fully integrated environment, it is necessary to deliver a standard way to communicate and interact with the different functionalities. The proposed research aims to provide an integration layer, capable of translating the rules defined at the knowledge management level, structured as Decision Model and Notation rules, into an AutomationML based language. This allows the cyber-physical production system the ability to apply these rules near the shop-floor. This article presents the template defined to represent the rules in AutomationML as well as the infrastructure developed to receive the rules from the knowledge management, translate them and deliver to the cyber-physical production system. At the end of the article is presented a test bed where the solution is instantiated with rules focused on quality control.

ACS Style

Andre Dionisio Rocha; Ricardo Peres; Jose Barata; José Barbosa; Paulo Leitão. Improvement of Multistage Quality Control through the Integration of Decision Modeling and Cyber-Physical Production Systems. 2018 International Conference on Intelligent Systems (IS) 2018, 479 -484.

AMA Style

Andre Dionisio Rocha, Ricardo Peres, Jose Barata, José Barbosa, Paulo Leitão. Improvement of Multistage Quality Control through the Integration of Decision Modeling and Cyber-Physical Production Systems. 2018 International Conference on Intelligent Systems (IS). 2018; ():479-484.

Chicago/Turabian Style

Andre Dionisio Rocha; Ricardo Peres; Jose Barata; José Barbosa; Paulo Leitão. 2018. "Improvement of Multistage Quality Control through the Integration of Decision Modeling and Cyber-Physical Production Systems." 2018 International Conference on Intelligent Systems (IS) , no. : 479-484.

Journal article
Published: 24 July 2018 in Computers in Industry
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The manufacturing industry represents a data rich environment, in which larger and larger volumes of data are constantly being generated by its processes. However, only a relatively small portion of it is actually taken advantage of by manufacturers. As such, the proposed Intelligent Data Analysis and Real-Time Supervision (IDARTS) framework presents the guidelines for the implementation of scalable, flexible and pluggable data analysis and real-time supervision systems for manufacturing environments. IDARTS is aligned with the current Industry 4.0 trend, being aimed at allowing manufacturers to translate their data into a business advantage through the integration of a Cyber-Physical System at the edge with cloud computing. It combines distributed data acquisition, machine learning and run-time reasoning to assist in fields such as predictive maintenance and quality control, reducing the impact of disruptive events in production.

ACS Style

Ricardo Silva Peres; Andre Dionisio Rocha; Paulo Leitao; Jose Barata. IDARTS – Towards intelligent data analysis and real-time supervision for industry 4.0. Computers in Industry 2018, 101, 138 -146.

AMA Style

Ricardo Silva Peres, Andre Dionisio Rocha, Paulo Leitao, Jose Barata. IDARTS – Towards intelligent data analysis and real-time supervision for industry 4.0. Computers in Industry. 2018; 101 ():138-146.

Chicago/Turabian Style

Ricardo Silva Peres; Andre Dionisio Rocha; Paulo Leitao; Jose Barata. 2018. "IDARTS – Towards intelligent data analysis and real-time supervision for industry 4.0." Computers in Industry 101, no. : 138-146.

Conference paper
Published: 01 July 2018 in 2018 IEEE 16th International Conference on Industrial Informatics (INDIN)
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In the last decades, several research initiatives suggested new solutions regarding the interoperability and interconnectivity among heterogeneous production components and all the actors that somehow interact within the shop-floor. However, most of the proposed data representations are focused on the description of the production capabilities. In this paper, it is proposed a common data model focused not only in the production capabilities of the different components as well as the description of all the events, variables and resources that could indicate quality issues. Hence, the proposed data model describes all the information required by the GO0DMAN solution to reduce as much as possible, the defects, the respective causes and the strategies to avoid the propagation along the line. In order to increase the adoption of the proposed data model, it was developed using AutomationML. The proposed data model was designed and tested within the scope of the Horizon 2020 GO0DMAN project.

ACS Style

Ricardo Peres; Andre Dionisio Rocha; Joao Pedro Matos; Jose Barata. GO0DMAN Data Model - Interoperability in Multistage Zero Defect Manufacturing. 2018 IEEE 16th International Conference on Industrial Informatics (INDIN) 2018, 815 -821.

AMA Style

Ricardo Peres, Andre Dionisio Rocha, Joao Pedro Matos, Jose Barata. GO0DMAN Data Model - Interoperability in Multistage Zero Defect Manufacturing. 2018 IEEE 16th International Conference on Industrial Informatics (INDIN). 2018; ():815-821.

Chicago/Turabian Style

Ricardo Peres; Andre Dionisio Rocha; Joao Pedro Matos; Jose Barata. 2018. "GO0DMAN Data Model - Interoperability in Multistage Zero Defect Manufacturing." 2018 IEEE 16th International Conference on Industrial Informatics (INDIN) , no. : 815-821.

Article
Published: 01 November 2017 in Journal of Intelligent Manufacturing
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Cyber-physical systems (CPS) emerge as a new idea to implement new manufacturing paradigms. There paradigms aim at answering the socio-economic factors that characterise modern enterprises, such as mass customisation and new markets. The authors propose an architecture that performs distributed diagnosis. The proposed solution uses artificial immune systems (AIS) to perform evolutionary diagnose. Industrial approaches to machine diagnosis are centralised. The authors pretend to make a CPS capable of distributed diagnosis with learning capabilities. An architecture capable of machine diagnosis and learning is also presented. This is done by bio-inspired algorithms. These were rated by a fuzzy inference system. The algorithms were tested for situations a system may endure and for their learning capability. The results of the obtained research, study and development are hereby presented. These results constitute proof of the sustainability of the AIS paradigm as a solution to distributed diagnosis.

ACS Style

Andre Dionisio Rocha; Pedro Lima Monteiro; Mafalda Parreira-Rocha; Jose Barata. Artificial immune systems based multi-agent architecture to perform distributed diagnosis. Journal of Intelligent Manufacturing 2017, 30, 2025 -2037.

AMA Style

Andre Dionisio Rocha, Pedro Lima Monteiro, Mafalda Parreira-Rocha, Jose Barata. Artificial immune systems based multi-agent architecture to perform distributed diagnosis. Journal of Intelligent Manufacturing. 2017; 30 (4):2025-2037.

Chicago/Turabian Style

Andre Dionisio Rocha; Pedro Lima Monteiro; Mafalda Parreira-Rocha; Jose Barata. 2017. "Artificial immune systems based multi-agent architecture to perform distributed diagnosis." Journal of Intelligent Manufacturing 30, no. 4: 2025-2037.

Conference paper
Published: 01 July 2017 in 2017 IEEE 15th International Conference on Industrial Informatics (INDIN)
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Service-Oriented Architectures (SOAs) along with Web Services are now being considered as the de-facto standard implementation to connect several layers of a given business in a fast, secured and decentralized fashion. On the other hand, Internet of Things (IoT) devices are generating more and more data that can be analyzed and processed. Despite impossible to analyze locally, this data could be analyzed using a Service-Oriented Architecture. This work presents a solution, contemplating a SOA, that retrieves data from an unintelligent-turned-IoT device, using Representational State Transfer (REST) services. It then uses the ProSEco platform to process the data. Once processed, the results are presented using a web-based visualization tool.

ACS Style

Pedro Lima-Monteiro; Guilherme Brito; Andre Dionisio Rocha; Jose Barata; Paulo Ilheu; Joao Freire; Claudio Cenedese. ProSEco as a data processing platform: A service-oriented architecture for data analysis. 2017 IEEE 15th International Conference on Industrial Informatics (INDIN) 2017, 1205 -1210.

AMA Style

Pedro Lima-Monteiro, Guilherme Brito, Andre Dionisio Rocha, Jose Barata, Paulo Ilheu, Joao Freire, Claudio Cenedese. ProSEco as a data processing platform: A service-oriented architecture for data analysis. 2017 IEEE 15th International Conference on Industrial Informatics (INDIN). 2017; ():1205-1210.

Chicago/Turabian Style

Pedro Lima-Monteiro; Guilherme Brito; Andre Dionisio Rocha; Jose Barata; Paulo Ilheu; Joao Freire; Claudio Cenedese. 2017. "ProSEco as a data processing platform: A service-oriented architecture for data analysis." 2017 IEEE 15th International Conference on Industrial Informatics (INDIN) , no. : 1205-1210.

Conference paper
Published: 31 March 2017 in Collaboration in a Hyperconnected World
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With the advent of the Industry 4.0 movement, smart multiagent-based cyber-physical systems (CPS) are being more and more often proposed as a possible solution to tackle the requirements of intelligence, pluggability, scalability and connectivity of this paradigm. CPS have been suggested for a wide array of applications, including control, monitoring and optimization of manufacturing systems. However, there are several associated challenges in terms of validating and testing these systems due to their innate characteristics, emergent behavior, as well as the availability and cost of physical resources. Therefore, a dynamic simulation model constructed in V-REP is proposed as a way to test, validate and improve such systems, being applied to a data acquisition and pre-processing scenario as one of the key aspects of the interaction between a CPS and the shop-floor.

ACS Style

Ricardo Silva Peres; Andre Dionisio Rocha; Jose Barata. Dynamic Simulation for MAS-Based Data Acquisition and Pre-processing in Manufacturing Using V-REP. Collaboration in a Hyperconnected World 2017, 125 -134.

AMA Style

Ricardo Silva Peres, Andre Dionisio Rocha, Jose Barata. Dynamic Simulation for MAS-Based Data Acquisition and Pre-processing in Manufacturing Using V-REP. Collaboration in a Hyperconnected World. 2017; ():125-134.

Chicago/Turabian Style

Ricardo Silva Peres; Andre Dionisio Rocha; Jose Barata. 2017. "Dynamic Simulation for MAS-Based Data Acquisition and Pre-processing in Manufacturing Using V-REP." Collaboration in a Hyperconnected World , no. : 125-134.

Conference paper
Published: 03 March 2017 in Econometrics for Financial Applications
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ACS Style

Andre Dionisio Rocha; Miguel Rodrigues; Jose Barata Oliveira. An Evolvable and Adaptable Agent Based Smart Grid Management—A Simulation Environment. Econometrics for Financial Applications 2017, 417 -426.

AMA Style

Andre Dionisio Rocha, Miguel Rodrigues, Jose Barata Oliveira. An Evolvable and Adaptable Agent Based Smart Grid Management—A Simulation Environment. Econometrics for Financial Applications. 2017; ():417-426.

Chicago/Turabian Style

Andre Dionisio Rocha; Miguel Rodrigues; Jose Barata Oliveira. 2017. "An Evolvable and Adaptable Agent Based Smart Grid Management—A Simulation Environment." Econometrics for Financial Applications , no. : 417-426.

Conference paper
Published: 03 March 2017 in Artificial Intelligence: Foundations, Theory, and Algorithms
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ACS Style

Andre Dionisio Rocha; Pedro Fernandes; Catiele Lima; Jose Barata Oliveira. Semantic Model to Perform Pluggability of Heterogeneous Smart Devices into Smart City Environment. Artificial Intelligence: Foundations, Theory, and Algorithms 2017, 327 -335.

AMA Style

Andre Dionisio Rocha, Pedro Fernandes, Catiele Lima, Jose Barata Oliveira. Semantic Model to Perform Pluggability of Heterogeneous Smart Devices into Smart City Environment. Artificial Intelligence: Foundations, Theory, and Algorithms. 2017; ():327-335.

Chicago/Turabian Style

Andre Dionisio Rocha; Pedro Fernandes; Catiele Lima; Jose Barata Oliveira. 2017. "Semantic Model to Perform Pluggability of Heterogeneous Smart Devices into Smart City Environment." Artificial Intelligence: Foundations, Theory, and Algorithms , no. : 327-335.

Conference paper
Published: 03 March 2017 in Econometrics for Financial Applications
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ACS Style

Ricardo Silva Peres; Andre Dionisio Rocha; Andre Coelho; Jose Barata Oliveira. A Highly Flexible, Distributed Data Analysis Framework for Industry 4.0 Manufacturing Systems. Econometrics for Financial Applications 2017, 373 -381.

AMA Style

Ricardo Silva Peres, Andre Dionisio Rocha, Andre Coelho, Jose Barata Oliveira. A Highly Flexible, Distributed Data Analysis Framework for Industry 4.0 Manufacturing Systems. Econometrics for Financial Applications. 2017; ():373-381.

Chicago/Turabian Style

Ricardo Silva Peres; Andre Dionisio Rocha; Andre Coelho; Jose Barata Oliveira. 2017. "A Highly Flexible, Distributed Data Analysis Framework for Industry 4.0 Manufacturing Systems." Econometrics for Financial Applications , no. : 373-381.

Conference paper
Published: 03 March 2017 in Econometrics for Financial Applications
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ACS Style

Andre Dionisio Rocha; João Aires Tapadinhas; Luis Flores; Jose Barata Oliveira. Data Mining of Energy Consumption in Manufacturing Environment. Econometrics for Financial Applications 2017, 157 -166.

AMA Style

Andre Dionisio Rocha, João Aires Tapadinhas, Luis Flores, Jose Barata Oliveira. Data Mining of Energy Consumption in Manufacturing Environment. Econometrics for Financial Applications. 2017; ():157-166.

Chicago/Turabian Style

Andre Dionisio Rocha; João Aires Tapadinhas; Luis Flores; Jose Barata Oliveira. 2017. "Data Mining of Energy Consumption in Manufacturing Environment." Econometrics for Financial Applications , no. : 157-166.

Conference paper
Published: 03 March 2017 in Econometrics for Financial Applications
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With the increasing customization of products there was a need to create new manufacturing systems that were able to satisfy these needs of the market. The Reconfigurable Manufacturing System (RMS) emerges as a more recent approach allowing the reconfiguration of the line and all the manufacturing systems. The balancing line is a common problem in the system reconfiguration but may not be enough, being also important to reconfigure the material handling system itself. Genetic Algorithms (GA) are one of the most known and used alternatives in optimization problems being widely used in shortest path problems like the travelling salesman. In this paper a solution that allows reconfiguring an agent based material handling system using a genetic algorithm is presented.

ACS Style

Andre Dionisio Rocha; Pedro Caetano; Jose Barata Oliveira. A Generic Reconfigurable and Pluggable Material Handling System Based on Genetic Algorithm. Econometrics for Financial Applications 2017, 103 -113.

AMA Style

Andre Dionisio Rocha, Pedro Caetano, Jose Barata Oliveira. A Generic Reconfigurable and Pluggable Material Handling System Based on Genetic Algorithm. Econometrics for Financial Applications. 2017; ():103-113.

Chicago/Turabian Style

Andre Dionisio Rocha; Pedro Caetano; Jose Barata Oliveira. 2017. "A Generic Reconfigurable and Pluggable Material Handling System Based on Genetic Algorithm." Econometrics for Financial Applications , no. : 103-113.

Conference paper
Published: 03 March 2017 in Econometrics for Financial Applications
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ACS Style

Pedro Lima-Monteiro; Mafalda Parreira-Rocha; Andre Dionisio Rocha; Jose Barata Oliveira. Big Data Analysis to Ease Interconnectivity in Industry 4.0—A Smart Factory Perspective. Econometrics for Financial Applications 2017, 237 -245.

AMA Style

Pedro Lima-Monteiro, Mafalda Parreira-Rocha, Andre Dionisio Rocha, Jose Barata Oliveira. Big Data Analysis to Ease Interconnectivity in Industry 4.0—A Smart Factory Perspective. Econometrics for Financial Applications. 2017; ():237-245.

Chicago/Turabian Style

Pedro Lima-Monteiro; Mafalda Parreira-Rocha; Andre Dionisio Rocha; Jose Barata Oliveira. 2017. "Big Data Analysis to Ease Interconnectivity in Industry 4.0—A Smart Factory Perspective." Econometrics for Financial Applications , no. : 237-245.

Conference paper
Published: 03 March 2017 in Artificial Intelligence: Foundations, Theory, and Algorithms
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ACS Style

Andre Dionisio Rocha; Pedro Barroca; Giovanni Dal Maso; Jose Barata Oliveira. Environment to Simulate Distributed Agent Based Manufacturing Systems. Artificial Intelligence: Foundations, Theory, and Algorithms 2017, 405 -416.

AMA Style

Andre Dionisio Rocha, Pedro Barroca, Giovanni Dal Maso, Jose Barata Oliveira. Environment to Simulate Distributed Agent Based Manufacturing Systems. Artificial Intelligence: Foundations, Theory, and Algorithms. 2017; ():405-416.

Chicago/Turabian Style

Andre Dionisio Rocha; Pedro Barroca; Giovanni Dal Maso; Jose Barata Oliveira. 2017. "Environment to Simulate Distributed Agent Based Manufacturing Systems." Artificial Intelligence: Foundations, Theory, and Algorithms , no. : 405-416.

Journal article
Published: 01 January 2017 in Procedia Manufacturing
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ACS Style

Giacomo Angione; José Barbosa; Frederik Gosewehr; Paulo Leitão; Daniele Massa; João Matos; Ricardo Silva Peres; André Dionisio Rocha; Jeffrey Wermann. Integration and Deployment of a Distributed and Pluggable Industrial Architecture for the PERFoRM Project. Procedia Manufacturing 2017, 11, 896 -904.

AMA Style

Giacomo Angione, José Barbosa, Frederik Gosewehr, Paulo Leitão, Daniele Massa, João Matos, Ricardo Silva Peres, André Dionisio Rocha, Jeffrey Wermann. Integration and Deployment of a Distributed and Pluggable Industrial Architecture for the PERFoRM Project. Procedia Manufacturing. 2017; 11 ():896-904.

Chicago/Turabian Style

Giacomo Angione; José Barbosa; Frederik Gosewehr; Paulo Leitão; Daniele Massa; João Matos; Ricardo Silva Peres; André Dionisio Rocha; Jeffrey Wermann. 2017. "Integration and Deployment of a Distributed and Pluggable Industrial Architecture for the PERFoRM Project." Procedia Manufacturing 11, no. : 896-904.

Conference paper
Published: 01 October 2016 in IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society
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With the emergence of the Industry 4.0 concept, or the fourth industrial revolution, the industry is bearing witness to the appearance of more and more complex systems, often requiring the integration of various new heterogeneous, modular and intelligent elements with pre-existing legacy devices. This challenge of interoperability is one of the main concerns taken into account when designing such systems-of-systems, commonly requiring the use of standard interfaces to ensure this seamless integration. To aid in tackling this challenge, a common format for data exchange should be adopted. Thus, a study to select the foundations for the development of such a format is hereby presented, taking into account the specific needs of four different use cases representing varied key European industry sectors.

ACS Style

Ricardo Peres; Mafalda Parreira-Rocha; Andre Dionisio Rocha; José Barbosa; Paulo Leitão; Jose Barata. Selection of a data exchange format for industry 4.0 manufacturing systems. IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society 2016, 5723 -5728.

AMA Style

Ricardo Peres, Mafalda Parreira-Rocha, Andre Dionisio Rocha, José Barbosa, Paulo Leitão, Jose Barata. Selection of a data exchange format for industry 4.0 manufacturing systems. IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society. 2016; ():5723-5728.

Chicago/Turabian Style

Ricardo Peres; Mafalda Parreira-Rocha; Andre Dionisio Rocha; José Barbosa; Paulo Leitão; Jose Barata. 2016. "Selection of a data exchange format for industry 4.0 manufacturing systems." IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society , no. : 5723-5728.