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Sobre

Sobre

Américo Azevedo é coordenador do CESE - Centro de Engenharia de Sistemas Empresariais, do INESC TEC e Diretor Científico do FABTEC - Laboratório de Processos e Tecnologias para Sistemas Avançados de Produção.

Especialista em Gestão de Operações e em Organização e Gestão de Processos de Negócio, tem sido responsável por variados projetos empresariais (de consultoria e de I&D) de âmbito nacional e internacional. Professor Associado c/ Agregação da FEUP e docente na Porto Business School, onde também desenvolve projetos de consultoria empresarial. No Programa MIT Portugal, tem tido atividade na área EDAM (Engineering Design and Advanced Manufacturing) no âmbito da Gestão de Operações.

A sua atividade docente, desenvolvida em diversos cursos de mestrado e doutoramento da FEUP e de pós-graduação e de formação executiva na PBS (Porto Business School), está centrada fundamentalmente no domínio da Gestão de Operações, Sistemas Avançados de Produção e da Organização e Gestão de Processos de Negócio. 

Publica com regularidade em revistas científias, sendo autor/co-autor em mais de 180 publicações científicas.

Américo Azevedo é Licenciado em Engenharia Electrotécnia e de Computadores (1988), prestou provas de "Aptidão Pedagógica e Capacidade Científica" (1992), é Doutorado em Operações pela Universidade do Porto (2000) e Agregado pela Universidade do Porto (2017).

Tópicos
de interesse
Detalhes

Detalhes

018
Publicações

2023

Scalable Digital Twins for industry 4.0 digital services: a dataspaces approach

Autores
Moreno, T; Almeida, A; Toscano, C; Ferreira, F; Azevedo, A;

Publicação
PRODUCTION AND MANUFACTURING RESEARCH-AN OPEN ACCESS JOURNAL

Abstract

2023

A Digital Twin Platform-Based Approach to Product Lifecycle Management: Towards a Transformer 4.0

Autores
Silva H.; Moreno T.; Almeida A.; Soares A.L.; Azevedo A.;

Publicação
Lecture Notes in Mechanical Engineering

Abstract

2022

Self-adapting WIP parameter setting using deep reinforcement learning

Autores
Silva, MTDE; Azevedo, A;

Publicação
COMPUTERS & OPERATIONS RESEARCH

Abstract
This study investigates the potential of dynamically adjusting WIP cap levels to maximize the throughput (TH) performance and minimize work in process (WIP), according to real-time system state arising from process variability associated with low volume and high-variety production systems. Using an innovative approach based on state-of-the-art deep reinforcement learning (proximal policy optimization algorithm), we attain WIP reductions of up to 50% and 30%, with practically no losses in throughput, against pure-push systems and the statistical throughput control method (STC), respectively. An exploratory study based on simulation experiments was performed to provide support to our research. The reinforcement learning agent's performance was shown to be robust to variability changes within the production systems.

2022

Unveiling undergraduate production engineering students’ comprehension of process flow measures

Autores
Torres, N; Jr; de Azevedo, AL; Simões, AC; Ladeira, MB; de Sousa, PR; de Freitas, LS;

Publicação
Production

Abstract
Paper aims: This study analyzes the comprehension of production engineering students about the influence of some key variables on the process performance measures in a service process, Originality: This paper points out the need for educators to re-evaluate their approaches to teaching the Operations Management (OM) principles related to process flow measures, Research method: This study used scenario-based role-playing experiments with 2×2×2 between-subject factorial design with three independent variables (variability of activities, capacity utilization, and resource pooling) and four dependent variables related to key internal process performance measures (Flow Time, Overall Quality of service, Quality of service employees, and Queue Size), The sample was composed of 178 undergraduate production engineering students from a large university in Brazil from various institution units, Main findings: These results show that students perceived the use of resource pooling as an impactful practice, However, the students did not correctly identify the effects of increasing resource utilization and the variability on flow time and queue size when activities are pooled, Implications for theory and practice: The teaching of basic concepts of OM requires the support of computational tools, Undergraduate courses that contemplate subjects in the field of OM should work more intensely on simulation-based learning. © This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

2021

Grasp the Challenge of Digital Transition in SMEs—A Training Course Geared towards Decision-Makers

Autores
Azevedo, A; Almeida, AH;

Publicação
EDUCATION SCIENCES

Abstract
Small and medium-sized enterprises (SMEs) in Europe risk their competitiveness if they fail to embrace digitalization. Indeed, SMEs are aware of the need to digitalize—more than one in two SMEs are concerned that they may lose competitiveness if they do not adopt new digital technologies. However, a key obstacle is related with decision-makers’ lack of awareness concerning digital technologies potential and implications. Some decision-makers renounce digital transition simply because they do not understand how it can be incorporated into the business. Take into account this common reality, especially among SMEs, this research project intends to identify the skills and subjects that need to be addressed and suggests the educational methodology and implementation strategy capable of maximizing its success. Therefore, and supported by a focused group research methodology, an innovative training program, oriented to decision-makers, was designed and implemented. The program was conceived based on a self-directed learning methodology, combining both asynchronous lecture/expositive and active training methodologies, strongly based on state-of-the-art knowledge and supported by reference cases and real applications. It is intended that the trainees/participants become familiar with a comprehensive set of concepts, principles, methodologies, and tools, capable of significantly enhancing decision-making capability at both strategic and tactical level. The proposed programme with a multidisciplinary scope explores different thematic chapters (self-contained) as well as cross-cutting thematic disciplines, oriented to the Industry 4.0 and digital transformation paradigm. Topics related with Digital Maturity Assessment, Smart Factories and Flexible Production Systems, Big Data, and Artificial Intelligence for Smarter Decision-Making in Industry and Smart Materials and Products, as well as new production processes for new business models. Each thematic chapter in turn is structured around a variable set of elementary modules and includes examples and case studies to illustrate the selected topics. A teaching-learning methodology centered on an online platform is proposed, having as a central element, a collection of videos complemented by a set of handouts that organize the set of key messages and take-ways associated with each module. In this paper, we present the design and practice of this training course specifically oriented to decision-makers in SME.

Teses
supervisionadas

2022

Digital Twin for Manufacturing Equipment in Industry 4.0

Autor
Tomás Miguel Antero Moreno

Instituição
UP-FEUP

2022

A Value-Oriented Framework for Return Evaluation of Digital Business Transformation in SMEs

Autor
Alexander Dutra Tostes

Instituição
UP-FEUP

2022

Deep Reinforcement Learning for Production Flow Control

Autor
Manuel Tomé de Andrade e Silva

Instituição
UP-FEUP

2021

Deep Reinforcement Learning for Production Flow Control

Autor
Manuel Tomé de Andrade e Silva

Instituição
UP-FEUP

2021

Metodologias para melhoria de processos e crescimento numa empresa de marketing

Autor
André Filipe Nogueira Ribeiro

Instituição
UP-FEUP