Selected Publications

Nowadays, systems are becoming increasingly complex, mainly due to an exponential increase in the number of entities and their interconnections. Examples of these complex systems can be found in manufacturing, smart-grids, traffic control, logistics, economics and biology, among others. Due to this complexity, particularly in manufacturing, a lack of responsiveness in coping with demand for higher quality products, the drastic reduction in product lifecycles and the increasing need for product customization are being observed. Traditional solutions, based on central monolithic control structures, are becoming obsolete as they are not suitable for reacting and adapting to these perturbations. The decentralization of the complexity problem through simple, intelligent and autonomous entities, such as those found in multi-agent systems, is seen as a suitable methodology for tackling this challenge in industrial scenarios. Additionally, the use of biologically inspired self-organization concepts has proved to be suitable for being embedded in these approaches enabling better performances to be achieved. According to these principals, several approaches have been proposed but none can be truly embedded and extract all the potential of self-organization mechanisms. This paper proposes an evolution to the ADACOR holonic control architecture inspired by biological and evolutionary theories. In particular, a two-dimensional self-organization mechanism was designed taking the behavioural and structural vectors into consideration, thus allowing truly evolutionary and reconfigurable systems to be achieved that can cope with emergent requirements. The approach proposed is validated with two simulation use cases. © 2014 Elsevier B.V. All rights reserved.
Computers in Industry, 2015

Product intelligence is a new industrial manufacturing control paradigm aligned with the context of cyber-physical systems and addressing the current requirements of flexibility, reconfigurability and responsiveness. This paradigm introduces benefits in terms of improvement of the entire product[U+05F3]s life-cycle, and particularly the product quality and customization, aiming the customer satisfaction. This paper presents an implementation of a system of intelligent products, developed under the scope of the GRACE project, where an agent-based solution was deployed in a factory plant producing laundry washing machines. The achieved results show an increase of the production and energy efficiency, an increase of the product quality and customization, as well as a reduction of the scrap costs. © 2015 Elsevier Ltd.
Control Engineering Practice, 2015

Benchmarking 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. © 2013 Elsevier Ltd.
Control Engineering Practice, 2013

The current markets 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 natures insights and develop powerful adaptive systems that able to evolve, thus dealing with the current challenges imposed on manufacturing 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. © 2011 Elsevier Ltd. All rights reserved.
Engineering Applications of Artificial Intelligence, 2012

Recent Publications

More Publications

. Cross benefits from cyber-physical systems and intelligent products for future smart industries. IEEE International Conference on Industrial Informatics (INDIN), 2017.


. Instantiating the PERFORM system architecture for industrial case studies. Studies in Computational Intelligence, 2017.


. Integration and Deployment of a Distributed and Pluggable Industrial Architecture for the PERFoRM Project. Procedia Manufacturing, 2017.


. Building a robotic cyber-physical production component. Studies in Computational Intelligence, 2016.


. Engineering an ADACOR based solution into a small-scale production system. IEEE International Symposium on Industrial Electronics, 2016.


. Exploring the integration of the human as a flexibility factor in CPS enabled manufacturing environments: Methodology and results. IECON Proceedings (Industrial Electronics Conference), 2016.


. Selection of a data exchange format for industry 4.0 manufacturing systems. IECON Proceedings (Industrial Electronics Conference), 2016.


. Specification of the PERFoRM architecture for the seamless production system reconfiguration. IECON Proceedings (Industrial Electronics Conference), 2016.


. A multi-agent system tool for strategic planning in small-lot production environments. Cutter IT Journal, 2015.


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FROM THE FOURTH PARADIGM OF SCIENCE TO THE FUTURE OF WORK. Big Data and excellence in training, employment growth and jobs of the future. These are the issues addressed by Da.Re., the project that straddles the new paradigm of knowledge: in a world where the digital revolution walks side by side to the training and employment revolution, the future of the work is more and more knowledge and less and less factory.


The manufacturing assembly systems and the quality control stations of a production line will be treated as intelligent agents, and the whole production process will be supervised and controlled through the integrated and coordinated operation of a network of collaborative individual agents, each with its own objectives and behaviours, possessing its own perceptive and cognitive capabilities.


ARUM proposes to develop an intelligent Enterprise Service-Based platform (i-ESB). The platform will integrate a service-based architecture with a knowledge-based Multi-Agent System The i-ESB platform will gather information from sources such as sensors and resource management systems, giving decision makers and planners better insight into and control over the design-to-production process. Also, time-, cost- and risk-analysis will take place within the platform. The project has a double approach, making use of both prediction (in the pre-planning phase) and real-time control (in the production phase).


Traditional approaches for quality control treat the production system as a whole and lack the capability to discriminate among changes at different stages. This means that the large amount of data generated or used in a production system, is not efficiently processed. In this context, the strategy developed by the GO0D MAN project will be able to manage the production in an advanced way, involving Big Data as the key element for an efficient and zero defects manufacturing (ZDM) process. The ultimate goal is to develop a production strategy that can guarantee high quality of products without interfering, actually improving, the production efficiency of the entire system. GO0D MAN is an Innovation Action of Horizon 2020 programme and is based on the results achieved in other European Research Projects developed in recent years, such as GRACE, IDEAS and Self-Learning, which addressed the issue of zero-defect production at feasibility and prototyping level.

Maintenance 4.0

In industrial manufacturing environments, often characterized as being stochastic, dynamic and chaotic, maintenance is a crucial issue to ensure the production efficiency, since the occurrence of unexpected disturbances leads to a degradation of the system performance, causing the loss of productivity and business opportunities, which are crucial roles to achieve competitiveness. Maintenance 4.0 project constitutes a real world implementation of intelligent and predictive maintenance through the development of advanced data analytics applications. These applications will enable the reduction of the unplanned down times by predicting possible failures The ultimate goal is to develop industrial applications to perform real time data analysis in order to boost the productivity and, consequently, business opportunities.

PERFoRM - Production harmonizEd Reconfiguration of Flexible Robots and Machinery

The EU HORIZON2020 project PERFoRM (Production harmonizEd Reconfiguration of Flexible Robots and Machinery) is targeting the current need for increasing flexibility and reconfigurability in the manufacturing domain. This trend is caused by the increasing demand for more customised, but cheaper and higher quality products by the customer, as well as the necessity for the manufacturer to produce without delays or breakdowns to reduce production costs.

Promoção da Indústria 4.0 na Região de Trás-os-Montes e Alto Douro, I4.0@TMAD

Projecto resultante da “Carta de Compromissos para o desenvolvimento TMAD”, tem como objetivo a caracterização do estado atual da inovação tecnológica no sector industrial regional, com vista à promoção da adoção do paradigma “Indústria 4.0”: a quarta Revolução industrial.


Invited Professor, equivalent to Adjunct Professor, teaching electrical engineering related courses, namely:
  • Microcontroller/Microprocessor (Graduation in Electrical Engineering)
  • Automation (Graduation in Electrical Engineering and Graduation in Renewable Energies)
  • Electric Propulsion Systems (Master in Renewable Energies and Energy efficiency)
  • Physics (Graduation in Computer Science)
  • Electrical Power Systems (Graduation in Electrical Engineering)
  • Applied Electronics (TSC in Medical Electronics, TSC in Electrical and Automation Installations)
  • Electronics (Graduation in Electrical Engineering)
  • Physics 2 (Graduation in Biomedicine Engineering)
  • Automotive Electronics (TSC in Automotive Technology and Management)
  • Industrial Electronics (TSC in Renewable Energy)
  • Automation and Programmable Logic Controllers (TSC in Electrical and Automation Installations)