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- Migration strategy toward innovative, digitalized, and harmonized production systemsPublication . Cala, Ambra; Cachada, Ana; Pires, Flávia; Barbosa, José; Wermann, Jeffrey; Colombo, Armando W.Industry today is facing the fourth Industrial Revolution, also called Industry 4.0, pulled by the market demand of shorter delivery time and product life cycles, with increased product variety and smaller lot sizes, and pushed by innovative technologies, such as Cloud computing, big-data analysis, connected Cyber-Physical Systems (CPSs), Internet of Things, and related services, as well as autonomous robots and augmented reality. Industry 4.0 is an industrial paradigm that is proposing to transform the traditional factories into smart factories, which are more competitive, efficient, and productive. The goal is the rapid introduction of new tangible products and intangible products (services) into the market as soon as market and customer requirements change. In order to achieve this goal, the production process itself should be more agile and particularly correctly digitalized.
- Benchmarking flexible job-shop scheduling and control systemsPublication . Trentesaux, Damien; Pach, Cyrille; Bekrar, Abdelghani; Sallez, Yves; Berger, Thierry; Thérèse, Bonte; Leitão, Paulo; Barbosa, JoséBenchmarking is comparing the output of different systems for a given set of input data in order to improve the system’s performance. Faced with the lack of realistic and operational benchmarks that can be used for testing optimization methods and control systems in flexible systems, this paper proposes a benchmark system based on a real production cell. A three-step method is presented: data preparation, experimentation, and reporting. This benchmark allows the evaluation of static optimization performances using traditional operation research tools and the evaluation of control system's robustness faced with unexpected events.
- Solving flexible job shop scheduling using genetic algorithmPublication . Pereira, Ana I.; Curralo, Ana; Barbosa, José; Leitão, PauloThis work addresses a real assembly cell: the AIP-PRIMECA cell at the Université e de Valenciennes et du Hainaut-Cambrésis, in France. This system can be viewed as a Flexible Job Shop, leading to the formulation of a Flexible Job Shop Scheduling Problem (FJSSP). This FJSSP offers the possibility to create the products "AIP", "LATE" and "BELT" using by five workstations, each one being able to perform a set of operations, that are linked using a conveyor system. The transportation between stations is achieved using a shuttle which is able to transport one product at the time, being released after the product processing conclusion. The problem consists in finding a operations schedule on the machines, taking into account the precedence constraints minimizing the batch makespan, i.e., the finish time of the last operation completed in the schedule. To solve the flexible job shop the genetic algorithm (GA) was used to obtain the global solution.
- Big data: business, technology, education, and sciencePublication . Johnson, Jeffrey; Tesei, Luca; Piangerelli, Marco; Merelli, Emanuela; Paci, Riccardo; Stojanovic, Nedad; Leitão, Paulo; Barbosa, José; Amador, MarcoTransforming the latent value of big data into real value requires the great human intelligence and application of human-data scientists. Data scientists are expected to have a wide range of technical skills alongside being passionate self-directed people who are able to work easily with others and deliver high quality outputs under pressure. There are hundreds of university, commercial, and online courses in data science and related topics. Apart from people with breadth and depth of knowledge and experience in data science, we identify a new educational path to train “bridge persons” who combine knowledge of an organization’s business with sufficient knowledge and understanding of data science to “bridge” between non-technical people in the business with highly skilled data scientists who add value to the business. The increasing proliferation of big data and the great advances made in data science do not herald in an era where all problems can be solved by deep learning and artificial intelligence. Although data science opens up many commercial and social opportunities, data science must complement other science in the search for new theory and methods to understand and manage our complex world.
- Digitalized and harmonized industrial production systems: the PERFoRM approachPublication . Colombo, Armando W.; Gepp, Michael; Barata, José; Leitão, Paulo; Barbosa, José; Wermann, JeffreyOn the one side, Industrial competitiveness today means shorter product lifecycles, increased product variety, and shorter times to market and customized tangible products and services. To face these challenges, the manufacturing industry is forced to move from traditional management, control, and automation approaches towards industrial cyber-physical systems. On the other side, several emergent engineering approaches and related Information-Communication-Control-Technologies, such as Multi-Agent-Systems, Service-Oriented Architecture, Plug-and-Produce Systems, Cloud and Fog Technologies, Big Data and Analytics, among others, have been researched during the last years. The confluence of those results with the latest developments in Industrial Digitalization, Systems-of-Cyber-Physical-Systems Engineering, Internet-of-Things, Internet-of-Services, and Industry 4.0 is opening a new broad spectrum of innovation possibilities. The PERFoRM (Production-harmonizEd-Reconfiguration of Flexible Robots and Machinery) approach is one of them. It teaches the reader what it means when production machines and systems are digitalized and migrated into Industrial Cyber-Physical Systems and what happens when they are networked and start collaborating with each other and with the human, using the internet. After a Technology Trend Screening and beyond a comprehensive state-of-the-art analysis about Industrial Digitalization and Industry 4.0-compliant solutions, the book introduces methods, architectures, and technologies applicable in real industrial use cases, explained for a broad audience of researchers, practitioners, and industrialists
- Self-organized cyber-physical conveyor system using multi-agent systemsPublication . Leitão, Paulo; Barbosa, José; Funchal, Gustavo Silva; Melo, VictóriaThe adoption of industrial cyber-physical systems is facing several challenges, with artificial intelligence and self-organization techniques assuming critical aspects to be considered in the deployment of such solutions to support the dynamic evolution and adaptation to condition changes. This paper describes the implementation of a modular, flexible and self-organized cyber-physical conveyor system build up with different individual modular and intelligent transfer modules. For this purpose, multi-agent systems are used to distribute intelligence among transfer modules supporting pluggability and modularity, complemented with self-organization capabilities to achieve a truly self-reconfigurable system. Furthermore, Internet of Things and Artificial Intelligence technologies are used to enable the real-time monitoring of the system, aiming the detection and prevention of anomalies in advance, and to enable the protection from possible external threats.
- Bio-inspired multi-agent systems for reconfigurable manufacturing systemsPublication . Leitão, Paulo; Barbosa, José; Trentesaux, DamienThe current market’s demand for customization and responsiveness is a major challenge for producing intelligent, adaptive manufacturing systems. The Multi-Agent System (MAS) paradigm offers an alternative way to design this kind of system based on decentralized control using distributed, autonomous agents, thus replacing the traditional centralized control approach. The MAS solutions provide modularity, flexibility and robustness, thus addressing the responsiveness property, but usually do not consider true adaptation and re-configuration. Understanding how, in nature, complex things are performed in a simple and effective way allows us to mimic nature’s insights and develop powerful adaptive systems that able to evolve, thus dealing with the current challenges imposed on manufactur- ing systems. The paper provides an overview of some of the principles found in nature and biology and analyses the effectiveness of bio-inspired methods, which are used to enhance multi-agent systems to solve complex engineering problems, especially in the manufacturing field. An industrial automation case study is used to illustrate a bio-inspired method based on potential fields to dynamically route pallets.
- Agent-based distributed data analysis in industrial cyber-physical systemsPublication . Queiroz, Jonas; Leitão, Paulo; Barbosa, José; Oliveira, Eugenio; Garcia, GiselaCyber-Physical System (CPS) is a key concept in the fourth industrial revolution, acting as the backbone infrastructure to develop Industry 4.0 compliant solutions based on decentralized networks of cyber-physical entities. In such systems, the distribution of intelligence and data analytics capabilities by different computational layers assumes a crucial relevance to support monitoring, diagnosis, prediction, and optimization tasks. Multiagent Systems (MAS) is a suitable approach to support this distribution through vertical and horizontal dimensions. In this context, this article introduces a modular agent-based architecture to balance the distribution of data analysis capabilities in industrial CPS. The proposed MAS solution was implemented in an industrial automotive factory plant, which applicability, novelty, and benefits were recognized, e.g., through its inclusion in the Innovation Radar of the Great EU-funded Innovations due to its contribution for the implementation of Zero Defects Manufacturing strategies in multistage production systems.
- A impressão 3D no FabLab IPB, um caso solidárioPublication . Rocha, João; Santos, Jorge; Barbosa, JoséA primeira vez que tivemos contacto com a tecnologia 3D foi em 1998 no INEGI, no âmbito de um mestrado em Engenharia Mecânica da Faculdade de Engenharia da Universidade do Porto, sob orientação do professor J. Lino Alves. Nesta época iniciava-se o uso destas tecnologias, a primeira impressora fora lançada para o mercado cerca de 10 anos antes pela 3D Systems. Não se falava em fabrico aditivo. O grande objetivo era o fabrico rápido de protótipos, preferencialmente funcionais, falava-se de prototipagem rápida. A outra grande área era o fabrico rápido de ferramentas, área onde se inseriu esta tese de mestrado. As tecnologias disponíveis nos finais dos anos 90 de seculo XX no INEGI eram o LOM (laminated object manufacturing - fabrico de objetos por camadas) e um equipamento de Estereolitografia.
- Genetic algorithm for flexible job shop scheduling problem - a case studyPublication . Ferreira, Adriano; Guevara, Gabriela; Pereira, Ana I.; Barbosa, José; Leitão, PauloThis work proposes the impact assessment of the workers in the optimal time of operations in a Flexible Job Shop Scheduling Problem. In this work, a real enterprise was studied. The problem consists in finding the workers operations schedule, taking into account the precedence constraints. The scheduling of operations is a complex problem consisting in determining the optimal allocation of tasks to resources under a set of constraints, which in enterprise business assumes a critical issue. Solving this issue requires the use of optimization techniques that guarantees the achievement of acceptable solutions as optimized as possible. In industrial environments, characterized by the frequent occurrence of unplanned disturbances and changes, the optimal plan becomes inapplicable and obsolete very fast. This introduces a new requirement to the use of optimization techniques, where besides the quality of the calculated solution, it is also crucial to consider the time to compute the solution. This paper studies the application of a genetic algorithm approach to determine the scheduling in an industrial factory plant organized as flexible job shop problem. Flexible Job Shop Scheduling Problem (FJSSP) is an extension of the traditional Job Shop Scheduling Problem, differing from this in the sense that some workers may be capable of performing more than one type of tasks. Additionally, for each task there is at least, one worker that is capable of performing the operation. The main objective is to minimize the finish time of the last task completed in the schedule.