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Sobre

Sobre

Sou um investigador e Engineering Manager da equipa de Gestão de Operações e Apoio à Decisão no Centro de Engenharia de Sistemas Empresariais do INESC TEC. Possuo competências em programação e experiência em Gestão Industrial, especialmente em métodos de simulação e otimização para apoio à tomada de decisões, focando em sistemas de manufatura e logística interna. Tenho mestrado em Engenharia Eletrotécnica e de Computadores (ramo de Automação e especialização em Gestão Industrial) pela Faculdade de Engenharia da Universidade do Porto (FEUP). Participei em diversos projetos, abrangendo gestão de stock, balanceamento des linhas de produção, planeamento de produção, definição de layouts fabris, entre outros. Tenho experiência em várias indústrias, como calçado, mobiliário, embalagens metálicas, entre outras. As minhas principais áreas de interesse incluem Gestão de Operações, Sistemas de Apoio à Decisão, Machine Learning, com um interesse particular em Reinforcement Learning.

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    Romão Filipe Santos
  • Cargo

    Investigador Auxiliar
  • Desde

    17 janeiro 2018
016
Publicações

2024

Deep Reinforcement Learning-Based Approach to Dynamically Balance Multi-manned Assembly Lines

Autores
Santos, R; Marques, C; Toscano, C; Ferreira, M; Ribeiro, J;

Publicação
Lecture Notes in Mechanical Engineering

Abstract
Assembly lines are at the core of many manufacturing systems, and planning for a well-balanced flow is key to ensure long-term efficiency. However, in flexible configurations such as Multi-Manned Assembly Lines (MMAL), the balancing problem also becomes more challenging. Due to the increased relevance of these assembly lines, this work aims to investigate the MMAL balancing problem, to contribute for a more effective decision-making process. Therefore, a new approach is proposed based on Deep Reinforcement Learning (DRL) embedded in a Digital Twin architecture. The proposed approach provides a close-to-reality training environment for the agent, using Discrete Event Simulation to simulate the production system dynamics. This methodology was tested on a real-world instance with preliminary results showing that similar solutions to the ones obtained using optimization-based strategies are achieved. This research provides evidence of success in terms of dynamic resource assignment to tasks and workers as a basis for future developments. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

2024

Transitioning trends into action: A simulation-based Digital Twin architecture for enhanced strategic and operational decision-making

Autores
Santos, R; Piqueiro, H; Dias, R; Rocha, CD;

Publicação
COMPUTERS & INDUSTRIAL ENGINEERING

Abstract
In the dynamic realm of nowadays manufacturing, integrating digital technologies has become paramount for enhancing operational efficiency and decision-making processes. This article presents a novel system architecture that integrates a Simulation-based Digital Twin (DT) with emerging trends in manufacturing to enhance decision-making, accompanied by a detailed technical approach encompassing protocols and technologies for each component. The DT leverages advanced simulation techniques to model, monitor, and optimize production processes in real time, facilitating both strategic and operational decision-making. Complementing the DT, trending technologies such as artificial intelligence, additive manufacturing, collaborative robots, autonomous vehicles, and connectivity advancements are strategically integrated to enhance operational efficiency and facilitate the adoption of the Manufacturing as a Service (MaaS) paradigm. A case study within a MaaS supplier context, deployed in an industrial laboratory with advanced robotic systems, demonstrates the practical application of optimizing dynamic job-shop configurations using Simulation-based DT, showcasing strategies to improve operational efficiency and resource utilization. The results of the industrial experiment were highly encouraging, underscoring the potential for extension to more intricate industrial systems, with particular emphasis on incorporating sustainability and remanufacturing principles.

2024

Enhancing Smart Manufacturing Systems: A Digital Twin Approach Employing Simulation, Flexible Robots and Additive Manufacturing Technologies

Autores
Santos, R; Rocha, C; Dias, R; Quintas, J;

Publicação
Communications in Computer and Information Science

Abstract
A new generation of manufacturing systems is emerging through the adoption of new policies to overcome future crises highlighted by constant social, environmental, and economic concerns. The rise of so-called smart manufacturing is noticeable. However, new risks to humankind are being introduced, and, more than ever, science and technology are required to guarantee the future sustainability and resilience of our manufacturing systems. This research presents a Digital Twin approach resorting to simulation models with embedded intelligence to transform efficient manufacturing systems and react to complex and unpredictable circumstances. The methodology covers production scheduling incorporating flexible robots, internal logistics supervision contemplating planning and control of mobile robots, and capacity management. The method demonstrates the potential of integrating Additive Manufacturing technologies to quickly react to production needs. The developed strategy was enforced and assessed in an industrial experiment, exhibiting its robustness and promising application. The attained results were very encouraging, highlighting its potential extension to more complex industrial systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

2023

Managing Disruptions in a Biomass Supply Chain: A Decision Support System Based on Simulation/Optimisation

Autores
Piqueiro, H; Gomes, R; Santos, R; de Sousa, JP;

Publicação
SUSTAINABILITY

Abstract
To design and deploy their supply chains, companies must naturally take quite different decisions, some being strategic or tactical, and others of an operational nature. This work resulted in a decision support system for optimising a biomass supply chain in Portugal, allowing a more efficient operations management, and enhancing the design process. Uncertainty and variability in the biomass supply chain is a critical issue that needs to be considered in the production planning of bioenergy plants. A simulation/optimisation framework was developed to support decision-making, by combining plans generated by a resource allocation optimisation model with the simulation of disruptive wildfire scenarios in the forest biomass supply chain. Different scenarios have been generated to address uncertainty and variability in the quantity and quality of raw materials in the different supply nodes. Computational results show that this simulation/optimisation approach can have a significant impact in the operations efficiency, particularly when disruptions occur closer to the end of the planning horizon. The approach seems to be easily scalable and easy to extend to other sectors.

2022

Mitigating Biomass Supply Chain Uncertainty Through Discrete Event Simulation

Autores
Piqueiro, H; de Sousa, JP; Santos, R; Gomes, R;

Publicação
Proceedings of the International Conference on Industrial Engineering and Operations Management

Abstract