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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.

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

2021

A new Simulation-Based Approach in the Design of Manufacturing Systems and Real-Time Decision

Autores
Santos, R; Toscano, C; de Sousa, JP;

Publicação
IFAC PAPERSONLINE

Abstract
The principles and tools made available by the Industry 4.0, smart factories, or the Internet of Things (IoT), along with the adoption of more comprehensive simulation models, can significantly help the industry to face the current, huge external and internal challenges. This paper presents a new simulation-based approach to support decision making in the design and operational management of manufacturing systems. This approach is used to evaluate different layouts and resources allocation, and help managing operations, by integrating a simulation software with real-time data collected from the production assets through an IoT platform. The developed methodology uses a digital representation of the real production system (that may be viewed as a form of a digital twin) to assess different production scenarios. A set of key performance indicators (e.g. productivity) provided by the simulation can be used by the Manufacturing Execution System (MES) to generate production schedules. The developed approach was implemented and assessed in a real case study, showing its robustness and application potential. Its extension to other industrial contexts and sectors seems, therefore, quite promising. Copyright (C) 2021 The Authors.

2019

Industrial IoT integrated with simulation -A digital twin approach to support real-time decision making

Autores
Santos, R; Basto, J; Alcalá, SGS; Frazzon, E; Azevedo, A;

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

Abstract
The industry faces more and more the challenge of deploying and taking advantage of evidence-based strategic decisions to enhance profit gain. In this research, the possibility of having a fully integrated system composed by a simulator and an IoT platform with the capability of collecting real-time data from the shop floor and returning performance indicators to support decision making is evaluated. The suggested approach involves a Manufacturing Executing System (MES) producing a production schedule, an IoT Platform composed by a message broker and a real-time database, a Simulator including simulation software and a wrapper, and a user application serving as an interface between the user and the IoT Platform and Simulator integrated system. A detailed analysis of the functionalities and integration of the Simulator and the IoT Platform will also be explored. To evaluate the approach, one use case of a production line in the automotive industry is used. The application of the integrated IoT Simulation system permits its validation and consequent future work. © 2019, IEOM Society International.