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Dr. Leonilde Varela
University of Minho

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Research Keywords & Expertise

0 Collaboration
0 Decision Support
0 Industrial Engineering
0 Manufacturing
0 Manufacturing Planning

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Manufacturing
Scheduling
Decision Support
Industry 4.0
Collaboration
Web Services
production scheduling
Production Planning
manufacturing management
Web Technologies
Decision Support Systems
Web Systems
decision support tools
multicriteria decision making

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Short Biography

She received her PhD degree in Industrial Engineering and Management from the University of Minho, Portugal in 2007. She is Assistant Professor at Department of Production and Systems of University of Minho. Her main research interests are in Manufacturing Management, Production Planning and Control, Optimization, Artificial Intelligence, Meta-heuristics, Scheduling, Web based Systems, Services and technologies, mainly for supporting Engineering and Production Management, Collaborative Networks, Decision Making Models, Methods and Systems, and Virtual and Distributed Enterprises. She has published more than 150 refereed scientific papers in international conferences and in international scientific books and journals, indexed in the Web of Science and/or in the Scopus data bases. She coordinates R&D projects in the area of Production and Systems Engineering, namely concerning the development of web-based platforms and decision support models, methods and systems. She is a frequent paper reviewer for several journals, such as: Journal of Computer Integrated Manufacturing, Engineering Applications of Artificial Intelligence, IEEE Transactions on Neural Networks and Learning Systems, Journal of Decision Systems, Sustainability, among others. Moreover, she collaborates with more than twenty institutions worldwide, and is a member of several international networks, for instance: Machine Intelligence Research Labs, Scientific Network for Innovation and Research Excellence (MirLabs).

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Conference
Guimarães, Portugal
Date: 28-30 June 2021
Conference organizer :
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Leonilde Varela
Project

Project Goal: To provide an innovative higher education institution training toolbox to effectivelly address the European Industry 4.0 skills gap and mismatches

Starting Date:01 January 2020

Current Stage: Starting stage

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Journal article
Published: 08 July 2021 in Applied Sciences
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Rising energy prices, increasing maintenance costs, and strict environmental regimes have augmented the already existing pressure on the contemporary manufacturing environment. Although the decentralization of supply chain has led to rapid advancements in manufacturing systems, finding an efficient supplier simultaneously from the pool of available ones as per customer requirement and enhancing the process planning and scheduling functions are the predominant approaches still needed to be addressed. Therefore, this paper aims to address this issue by considering a set of gear manufacturing industries located across India as a case study. An integrated classifier-assisted evolutionary multi-objective evolutionary approach is proposed for solving the objectives of makespan, energy consumption, and increased service utilization rate, interoperability, and reliability. To execute the approach initially, text-mining-based supervised machine-learning models, namely Decision Tree, Naïve Bayes, Random Forest, and Support Vector Machines (SVM) were adopted for the classification of suppliers into task-specific suppliers. Following this, with the identified suppliers as input, the problem was formulated as a multi-objective Mixed-Integer Linear Programming (MILP) model. We then proposed a Hybrid Multi-Objective Moth Flame Optimization algorithm (HMFO) to optimize process planning and scheduling functions. Numerical experiments have been carried out with the formulated problem for 10 different instances, along with a comparison of the results with a Non-Dominated Sorting Genetic Algorithm (NSGA-II) to illustrate the feasibility of the approach.

ACS Style

Veera Ramakurthi; V. Manupati; José Machado; Leonilde Varela. A Hybrid Multi-Objective Evolutionary Algorithm-Based Semantic Foundation for Sustainable Distributed Manufacturing Systems. Applied Sciences 2021, 11, 6314 .

AMA Style

Veera Ramakurthi, V. Manupati, José Machado, Leonilde Varela. A Hybrid Multi-Objective Evolutionary Algorithm-Based Semantic Foundation for Sustainable Distributed Manufacturing Systems. Applied Sciences. 2021; 11 (14):6314.

Chicago/Turabian Style

Veera Ramakurthi; V. Manupati; José Machado; Leonilde Varela. 2021. "A Hybrid Multi-Objective Evolutionary Algorithm-Based Semantic Foundation for Sustainable Distributed Manufacturing Systems." Applied Sciences 11, no. 14: 6314.

Conference paper
Published: 24 June 2021 in Recent Advances in Computational Mechanics and Simulations
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A supply chain consists of facilities that are subjected to various types of disruption. When one or more facility in the supply chain is disrupted, its impact propagates and reduce the performance of the supply chain. This research paper takes into consideration the probability of a disruption scenario to design a robust network for a supply chain. A stochastic mathematical formulation has been presented as a mixed-integer linear programming model for maximizing the concerned profit. For measuring the supply chain’s robustness, the final quantity delivered after risk propagation in the disruption scenario as a robustness index (RI) is utilized. A numerical simulation is conducted to calculate the robustness index at different linking intensity and node threshold. The robustness index is compared under different disruption scenarios to obtain the optimal combination concerning linking intensity and node threshold.

ACS Style

V. K. Manupati; Shukalya Akash; K. Illaiah; E. Suresh Babu; M. L. R. Varela. Robust Supply Chain Network Design Under Facility Disruption by Consideration of Risk Propagation. Recent Advances in Computational Mechanics and Simulations 2021, 97 -107.

AMA Style

V. K. Manupati, Shukalya Akash, K. Illaiah, E. Suresh Babu, M. L. R. Varela. Robust Supply Chain Network Design Under Facility Disruption by Consideration of Risk Propagation. Recent Advances in Computational Mechanics and Simulations. 2021; ():97-107.

Chicago/Turabian Style

V. K. Manupati; Shukalya Akash; K. Illaiah; E. Suresh Babu; M. L. R. Varela. 2021. "Robust Supply Chain Network Design Under Facility Disruption by Consideration of Risk Propagation." Recent Advances in Computational Mechanics and Simulations , no. : 97-107.

Conference paper
Published: 24 June 2021 in Recent Advances in Computational Mechanics and Simulations
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There are Optimization Problems that are too complex to be solved efficiently by deterministic methods. For these problems, where deterministic methods have proven to be inefficient, if not completely unusable, it is common to use approximate methods, that is, optimization methods that solve the problems quickly, regardless of their size or complexity, even if they do not guarantee optimal solutions. In other words, methods that find “acceptable” solutions, efficiently. One particular type of approximate method, which is particularly effective in complex problems, are metaheuristics. Particle Swarm Optimization is a population-based metaheuristic, which has been particularly successful. In order to broaden the application and overcome the limitation of Particle Swarm Optimization, a discrete version of the metaheuristics is proposed. The Discrete Particle Swarm Optimization, DPSO, will change the PSO algorithm so it can be applied to discrete optimization problems. This alteration will focus on the velocity update equation. The DPSO was tested in an instance of the Traveling Salesman Problem, att48, 48 points problems proposed by Padberg and Rinaldi, which showed some promising results.

ACS Style

José A. Sequeiros; Rui Silva; André S. Santos; J. Bastos; M. L. R. Varela; A. M. Madureira. A Novel Discrete Particle Swarm Optimization Algorithm for the Travelling Salesman Problems. Recent Advances in Computational Mechanics and Simulations 2021, 48 -55.

AMA Style

José A. Sequeiros, Rui Silva, André S. Santos, J. Bastos, M. L. R. Varela, A. M. Madureira. A Novel Discrete Particle Swarm Optimization Algorithm for the Travelling Salesman Problems. Recent Advances in Computational Mechanics and Simulations. 2021; ():48-55.

Chicago/Turabian Style

José A. Sequeiros; Rui Silva; André S. Santos; J. Bastos; M. L. R. Varela; A. M. Madureira. 2021. "A Novel Discrete Particle Swarm Optimization Algorithm for the Travelling Salesman Problems." Recent Advances in Computational Mechanics and Simulations , no. : 48-55.

Conference paper
Published: 17 June 2021 in Recent Advances in Computational Mechanics and Simulations
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Distributed manufacturing systems have become consensus, particularly networked manufacturing systems (NMS) owing to its flexible and adaptable nature in response to customized requirements. Highlighting of functions in NMS and their integration with recent key enabling technologies i.e., Artificial intelligence, Machine learning, Internet of Things, Block chain technology, and Agent-based techniques, etc. are of utmost importance to identify problems and increase its efficiency. Hence, besides the above-mentioned approaches, this paper surveyed and analysed various articles systematically related to networked manufacturing in the context of knowledge creation and information, security, interoperability, and reliability. To identify the most related papers, the search has been conducted with Web of Science and Scopus databases. Subsequently, after evaluation 30 most related papers were selected and analyzed that further extended by identifying the issues and gaps in the existing empirical knowledge. Finally, the paper presents a roadmap for future research directions and developments.

ACS Style

Veerababu Ramakurthi; Vijayakumar Manupati; M. L. R. Varela; Goran Putnik. A Novel Integrated Framework Approach for TEBC Technologies in Distributed Manufacturing Systems: A Systematic Review and Opportunities. Recent Advances in Computational Mechanics and Simulations 2021, 101 -112.

AMA Style

Veerababu Ramakurthi, Vijayakumar Manupati, M. L. R. Varela, Goran Putnik. A Novel Integrated Framework Approach for TEBC Technologies in Distributed Manufacturing Systems: A Systematic Review and Opportunities. Recent Advances in Computational Mechanics and Simulations. 2021; ():101-112.

Chicago/Turabian Style

Veerababu Ramakurthi; Vijayakumar Manupati; M. L. R. Varela; Goran Putnik. 2021. "A Novel Integrated Framework Approach for TEBC Technologies in Distributed Manufacturing Systems: A Systematic Review and Opportunities." Recent Advances in Computational Mechanics and Simulations , no. : 101-112.

Journal article
Published: 16 June 2021 in CIRP Annals
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The paper presents two original and innovative contributions: 1) the model of machine learning (ML) based approach for predictive maintenance in manufacturing system based on machine status indications only, and 2) semi-Double-loop machine learning based intelligent Cyber-Physical System (I-CPS) architecture as a higher-level environment for ML based predictive maintenance execution. Considering only the machine status information provides rapid and very low investment-based implementation of an advanced predictive maintenance paradigm, especially important for SMEs. The model is validated in real-life situations, exploring different learning algorithms and strategies for learning maintenance predictive models. The findings show very high level of prediction accuracy.

ACS Style

Goran D. Putnik; Vijaya Kumar Manupati; Sai Krishna Pabba; Leonilde Varela; Francisco Ferreira. Semi-Double-loop machine learning based CPS approach for predictive maintenance in manufacturing system based on machine status indications. CIRP Annals 2021, 1 .

AMA Style

Goran D. Putnik, Vijaya Kumar Manupati, Sai Krishna Pabba, Leonilde Varela, Francisco Ferreira. Semi-Double-loop machine learning based CPS approach for predictive maintenance in manufacturing system based on machine status indications. CIRP Annals. 2021; ():1.

Chicago/Turabian Style

Goran D. Putnik; Vijaya Kumar Manupati; Sai Krishna Pabba; Leonilde Varela; Francisco Ferreira. 2021. "Semi-Double-loop machine learning based CPS approach for predictive maintenance in manufacturing system based on machine status indications." CIRP Annals , no. : 1.

Conference paper
Published: 26 May 2021 in Recent Advances in Computational Mechanics and Simulations
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In this paper, an Industry 4.0 oriented architecture of a manufacturing scheduling decision support system is provided. This proposal is based on an analysis performed about some other decision support systems architectures found in the literature and based on another analysis that was carried out regarding the results obtained through a questionnaire distributed through a wide set of enterprises in the Iberian Peninsula. This analysis did enable us to realize that the main characteristics considered fundamental to be integrated into the prosed manufacturing scheduling decision support system were, in fact, of upmost importance, within the current Industry 4.0 requirements. Moreover, the main characteristics proposed for integrating the manufacturing scheduling decision support system’s architecture presented were also used to establish a comparative analysis between the proposed system’s architecture and the ones analyzed from the literature. Besides, through this analysis, it was possible to realize that none of the architectures analyzed from the literature covered the whole set of important I4.0 oriented characteristics proposed.

ACS Style

Leonilde Varela; Vaibhav Shah; Aurélio Lucamba; Adriana Araújo; José Machado. An Intelligent Scheduling System Architecture for Manufacturing Systems Based on I4.0 Requirements. Recent Advances in Computational Mechanics and Simulations 2021, 262 -274.

AMA Style

Leonilde Varela, Vaibhav Shah, Aurélio Lucamba, Adriana Araújo, José Machado. An Intelligent Scheduling System Architecture for Manufacturing Systems Based on I4.0 Requirements. Recent Advances in Computational Mechanics and Simulations. 2021; ():262-274.

Chicago/Turabian Style

Leonilde Varela; Vaibhav Shah; Aurélio Lucamba; Adriana Araújo; José Machado. 2021. "An Intelligent Scheduling System Architecture for Manufacturing Systems Based on I4.0 Requirements." Recent Advances in Computational Mechanics and Simulations , no. : 262-274.

Journal article
Published: 10 May 2021 in Sustainability
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Complex systems consist of multiple machines that are designed with a certain extent of redundancy to control any unanticipated events. The productivity of complex systems is highly affected by unexpected simultaneous machine failures due to overrunning of machines, improper maintenance, and natural characteristics. We proposed realistic configurations with multiple machines having several flexibilities to handle the above issues. The objectives of the proposed model are to reduce simultaneous machine failures by slowing down the pace of degradation of machines, to improve the average occurrence of the first failure time of machines, and to decrease the loss of production. An approach has been developed using each machine’s degradation information to predict the machine’s residual life based on which the job adjustment strategy where machines with a lower health status will be given a high number of jobs to perform is proposed. This approach is validated by applying it in a fabric weaving industry as a real-world case study under different scenarios and the performance is compared with two other key benchmark strategies.

ACS Style

Thirupathi Samala; Vijaya Manupati; Bethalam Nikhilesh; Maria Varela; Goran Putnik. Job Adjustment Strategy for Predictive Maintenance in Semi-Fully Flexible Systems Based on Machine Health Status. Sustainability 2021, 13, 5295 .

AMA Style

Thirupathi Samala, Vijaya Manupati, Bethalam Nikhilesh, Maria Varela, Goran Putnik. Job Adjustment Strategy for Predictive Maintenance in Semi-Fully Flexible Systems Based on Machine Health Status. Sustainability. 2021; 13 (9):5295.

Chicago/Turabian Style

Thirupathi Samala; Vijaya Manupati; Bethalam Nikhilesh; Maria Varela; Goran Putnik. 2021. "Job Adjustment Strategy for Predictive Maintenance in Semi-Fully Flexible Systems Based on Machine Health Status." Sustainability 13, no. 9: 5295.

Review
Published: 25 February 2021 in Future Internet
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Research on flexible unit systems (FUS) with the context of descriptive, predictive, and prescriptive analysis have remarkably progressed in recent times, being now reinforced in the current Industry 4.0 era with the increased focus on integration of distributed and digitalized systems. In the existing literature, most of the work focused on the individual contributions of the above mentioned three analyses. Moreover, the current literature is unclear with respect to the integration of degradation and upgradation models for FUS. In this paper, a systematic literature review on degradation, residual life distribution, workload adjustment strategy, upgradation, and predictive maintenance as major performance measures to investigate the performance of the FUS has been considered. In order to identify the key issues and research gaps in the existing literature, the 59 most relevant papers from 2009 to 2020 have been sorted and analyzed. Finally, we identify promising research opportunities that could expand the scope and depth of FUS.

ACS Style

Thirupathi Samala; Vijaya Manupati; Maria Varela; Goran Putnik. Investigation of Degradation and Upgradation Models for Flexible Unit Systems: A Systematic Literature Review. Future Internet 2021, 13, 57 .

AMA Style

Thirupathi Samala, Vijaya Manupati, Maria Varela, Goran Putnik. Investigation of Degradation and Upgradation Models for Flexible Unit Systems: A Systematic Literature Review. Future Internet. 2021; 13 (3):57.

Chicago/Turabian Style

Thirupathi Samala; Vijaya Manupati; Maria Varela; Goran Putnik. 2021. "Investigation of Degradation and Upgradation Models for Flexible Unit Systems: A Systematic Literature Review." Future Internet 13, no. 3: 57.

Journal article
Published: 22 February 2021 in Procedia Computer Science
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Risk assessment is a theme of large spectrum applied in different fields. In the context of Virtual / Collaborative Enterprises there are several risks whose assessment should be aware to avoid undesirable consequences either for entire networked or for a partner in particular. The objective of this work is centered on the creation of a framework / guidelines to serve as a basis for the creation of a better risk assessment model for Virtual / Collaborative Enterprises. This work analyzed the few models available in the literature and identified some gaps that were used to purpose complementary guidelines for the design and / or improve the future risk assessment models. The pointed guidelines covered three important topics: risk factors; assessment methods; and the impact in different life cycle phases of a Virtual / Collaborative Enterprise. Considering the results of the work it is our conviction that there is space to improve the research in this field and a more robust and flexible risk assessment model should be developed.

ACS Style

Paulo Ávila; Alzira Mota; João Bastos; Leonel Patrício; António Pires; Hélio Castro; Maria Manuela Cruz-Cunha; Leonilde Varela. Framework for a risk assessment model to apply in Virtual / Collaborative Enterprises. Procedia Computer Science 2021, 181, 612 -618.

AMA Style

Paulo Ávila, Alzira Mota, João Bastos, Leonel Patrício, António Pires, Hélio Castro, Maria Manuela Cruz-Cunha, Leonilde Varela. Framework for a risk assessment model to apply in Virtual / Collaborative Enterprises. Procedia Computer Science. 2021; 181 ():612-618.

Chicago/Turabian Style

Paulo Ávila; Alzira Mota; João Bastos; Leonel Patrício; António Pires; Hélio Castro; Maria Manuela Cruz-Cunha; Leonilde Varela. 2021. "Framework for a risk assessment model to apply in Virtual / Collaborative Enterprises." Procedia Computer Science 181, no. : 612-618.

Conference paper
Published: 25 December 2020 in IOP Conference Series: Materials Science and Engineering
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Cyber physical systems (CPS) are known as one of the significant advancement in computer science and IT. Cyber physical manufacturing system is the emerging research area in the field of computer science as well as manufacturing science and technology which is promoting the 4th industrial revolution which is known as Industrie 4.0. CPS generally focuses on the integration of physical world with cyberspace. It is the integration of communication, computation, control and physical elements. At present time, CPS is the point of interest for academia, government and industries. However, a systematic literature review of cyber physical system for manufacturing system is not available. This paper aims to present the findings on cyber physical systems on manufacturing systems and development of cyber physical system for intelligent manufacturing. The CPS is explained with the concept of CPS and five level architecture system for manufacturing systems. Further, key enabling technologies for cyber physical manufacturing systems is also discussed. In this paper both WoS and Scopus databases (2000-2020) is taken in consideration for literature review. Top journals, top cited papers, top authors and top research categories have been found out.

ACS Style

Anbesh Jamwal; Rajeev Agrawal; Vijaya Kumar Manupati; Monica Sharma; Leonilde Varela; José Machado. Development of cyber physical system based manufacturing system design for process optimization. IOP Conference Series: Materials Science and Engineering 2020, 997, 012048 .

AMA Style

Anbesh Jamwal, Rajeev Agrawal, Vijaya Kumar Manupati, Monica Sharma, Leonilde Varela, José Machado. Development of cyber physical system based manufacturing system design for process optimization. IOP Conference Series: Materials Science and Engineering. 2020; 997 (1):012048.

Chicago/Turabian Style

Anbesh Jamwal; Rajeev Agrawal; Vijaya Kumar Manupati; Monica Sharma; Leonilde Varela; José Machado. 2020. "Development of cyber physical system based manufacturing system design for process optimization." IOP Conference Series: Materials Science and Engineering 997, no. 1: 012048.

Conference paper
Published: 15 August 2020 in Advances in Intelligent Systems and Computing
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Nowadays, interoperable decision support systems play a crucial role to improve production activity control in industrial Companies, and enable them to face the growing exigencies imposed by the arising Industry 4.0 era. In this paper an interoperable decision support system based on multivariate time series for setup data processing and visualization is put forward. The proposed system is described, in the context of a general architecture presented, and its application through an illustrative example from a stamping factory is analysed. Through the case study it is possible to realize about the importance of the proposed system, and its suitability of application to other companies, for instance in other industrial sectors and manufacturing environments.

ACS Style

M. L. R. Varela; Gabriela Amaral; Sofia Pereira; Diogo Machado; António Falcão; Rita Ribeiro; Emanuel Sousa; Jorge Santos; Alfredo F. Pereira. Interoperable Decision Support System Based on Multivariate Time Series for Setup Data Processing and Visualization. Advances in Intelligent Systems and Computing 2020, 550 -560.

AMA Style

M. L. R. Varela, Gabriela Amaral, Sofia Pereira, Diogo Machado, António Falcão, Rita Ribeiro, Emanuel Sousa, Jorge Santos, Alfredo F. Pereira. Interoperable Decision Support System Based on Multivariate Time Series for Setup Data Processing and Visualization. Advances in Intelligent Systems and Computing. 2020; ():550-560.

Chicago/Turabian Style

M. L. R. Varela; Gabriela Amaral; Sofia Pereira; Diogo Machado; António Falcão; Rita Ribeiro; Emanuel Sousa; Jorge Santos; Alfredo F. Pereira. 2020. "Interoperable Decision Support System Based on Multivariate Time Series for Setup Data Processing and Visualization." Advances in Intelligent Systems and Computing , no. : 550-560.

Conference paper
Published: 18 July 2020 in Proceedings of International Conference on Big Data, Machine Learning and Applications
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Recent manufacturing systems did not just confine to optimal utilization of resources due to the global stance on strict environmental regimes. Collaborative effort to achieve sustainable practices in the decentralized manufacturing environment is a new complex problem. In this paper, with a networked manufacturing system we try to achieve both traditional as well as sustainable parameters by optimizing the performances such as makespan, machine utilization, and energy consumption. Thereafter, we formulate the problem as a mixed-integer non-linear programming (MINLP) model. To handle this NP-hard problem and to find the optimal solutions a Controlled elitist non-dominated sorting genetic algorithm (CE-NSGA-II) has been adopted. Finally, the results are analyzed with different scenarios to prove the proposed approach validation.

ACS Style

Veera Babu Ramakurthi; V. K. Manupati; Leonilde Varela; José Machado. Energy Efficient Network Manufacturing System Using Controlled Elitist Non-dominated Sorting Genetic Algorithm. Proceedings of International Conference on Big Data, Machine Learning and Applications 2020, 188 -206.

AMA Style

Veera Babu Ramakurthi, V. K. Manupati, Leonilde Varela, José Machado. Energy Efficient Network Manufacturing System Using Controlled Elitist Non-dominated Sorting Genetic Algorithm. Proceedings of International Conference on Big Data, Machine Learning and Applications. 2020; ():188-206.

Chicago/Turabian Style

Veera Babu Ramakurthi; V. K. Manupati; Leonilde Varela; José Machado. 2020. "Energy Efficient Network Manufacturing System Using Controlled Elitist Non-dominated Sorting Genetic Algorithm." Proceedings of International Conference on Big Data, Machine Learning and Applications , no. : 188-206.

Conference paper
Published: 29 June 2020 in 2020 7th International Conference on Control, Decision and Information Technologies (CoDIT)
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This paper presents an approach that through simulation, implements a solution for two of the fundamental pillars of Industry 4.0: Internet of Things and Vertical and Horizontal Integration of Cyber-Physical Systems, by always keeping in mind the concept of design and development of those systems. The objective of the approach is to present Cyber-Physical Systems designers a possible approach, with simple and easy to follow steps, even if they have superficial knowledge of programming. The approach is composed by two steps: In the first step the modelling of the physical part is performed, using a PLC simulator and the representation of a real system, and the second step consists on modelling the cyber part of the system, through web platforms, such as Node-RED and the use of some IBM Platform resources. The approach is validated through a case study in which data is collected in the system under study, and then processed, analyzed and visualized to give insights that help on decision making, whether in the context of market strategy and production and operation of the equipment.

ACS Style

Victor Lukoki; Leonilde Varela; Jose Machado. Simulation of Vertical and Horizontal Integration of Cyber-Physical Systems. 2020 7th International Conference on Control, Decision and Information Technologies (CoDIT) 2020, 1, 282 -287.

AMA Style

Victor Lukoki, Leonilde Varela, Jose Machado. Simulation of Vertical and Horizontal Integration of Cyber-Physical Systems. 2020 7th International Conference on Control, Decision and Information Technologies (CoDIT). 2020; 1 ():282-287.

Chicago/Turabian Style

Victor Lukoki; Leonilde Varela; Jose Machado. 2020. "Simulation of Vertical and Horizontal Integration of Cyber-Physical Systems." 2020 7th International Conference on Control, Decision and Information Technologies (CoDIT) 1, no. : 282-287.

Journal article
Published: 24 June 2020 in Simulation Modelling Practice and Theory
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Manufacturing companies need to be effective in meeting customers’ delivery requirements. Due to customers’ expectations of shrinking delivery times, manufacturing lead times need to be short. This can be achieved through efficient Production Activity Control (PAC) methods. Currently PAC methods rely mostly on centralized decision-making and, seeming not to be adequate to deal with the increasing complexity and dynamics of manufacturing. Autonomous Production Control (APC) methods are a promising alternative to current methods, due to their rapid and flexible reaction to disturbances of the production systems’ operation. APC methods transfer the power of decision-making from a central unit to distributed logistic objects, such as machines, jobs and material handling devices. In this study, three APC methods, namely Pheromones (PHE), QLE (Queue Length Estimator) and a refined version of QLE (RQLE), are compared and analysed via simulation. The study was accomplished for two shop configurations, namely a flexible flow shop and a general flexible flow shop. Simulation results show a superior performance of RQLE in both configurations. Results also show that a new dispatching rule here proposed, the SPT-RTT rule, performs better than others with which it was compared. The study may have important implications for industrial practice and future research in PAC.

ACS Style

Luís Miguel Martins; Nuno Octávio Garcia Fernandes; Maria Leonilde Rocha Varela; Luís Miguel Silva Dias; Guilherme Augusto Borges Pereira; Sílvio Carmo Silva. Comparative study of autonomous production control methods using simulation. Simulation Modelling Practice and Theory 2020, 104, 102142 .

AMA Style

Luís Miguel Martins, Nuno Octávio Garcia Fernandes, Maria Leonilde Rocha Varela, Luís Miguel Silva Dias, Guilherme Augusto Borges Pereira, Sílvio Carmo Silva. Comparative study of autonomous production control methods using simulation. Simulation Modelling Practice and Theory. 2020; 104 ():102142.

Chicago/Turabian Style

Luís Miguel Martins; Nuno Octávio Garcia Fernandes; Maria Leonilde Rocha Varela; Luís Miguel Silva Dias; Guilherme Augusto Borges Pereira; Sílvio Carmo Silva. 2020. "Comparative study of autonomous production control methods using simulation." Simulation Modelling Practice and Theory 104, no. : 102142.

Conference paper
Published: 05 June 2020 in Recent Advances in Computational Mechanics and Simulations
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Production activity control approaches, methods, and mechanisms have been widely applied over the last decades, and continue to be of utmost importance nowadays, within the context of the currently fast-growing Industry 4.0 era. In this paper, a Simio-based simulation model is proposed and its application in a printing factory is illustrated. The main aim of this work consists of providing general production planning improvements in the considered factory, with a special focus on the reduction of setup time. The proposed model is based on several distinct production activity control mechanisms, for instance, the CONWIP and the Routing Group mechanisms from Simio, which did enable to reach good improvements regarding a set of performance measures considered, including machines’ setup time reduction, along with the maximization of the percentage of products delivered on time. Future work is also planned to be carried out to improve other kinds of performance measures, and by using other types of production activity control mechanisms, to be further applied in other industrial companies and sectors.

ACS Style

José Pedro Vaz; Leonilde Varela; Bruno Gonçalves; José Machado. Production Planning and Setup Time Optimization: An Industrial Case Study. Recent Advances in Computational Mechanics and Simulations 2020, 220 -230.

AMA Style

José Pedro Vaz, Leonilde Varela, Bruno Gonçalves, José Machado. Production Planning and Setup Time Optimization: An Industrial Case Study. Recent Advances in Computational Mechanics and Simulations. 2020; ():220-230.

Chicago/Turabian Style

José Pedro Vaz; Leonilde Varela; Bruno Gonçalves; José Machado. 2020. "Production Planning and Setup Time Optimization: An Industrial Case Study." Recent Advances in Computational Mechanics and Simulations , no. : 220-230.

Review
Published: 19 March 2020 in Enterprise Information Systems
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Production environments are becoming more complex and dynamics. This is influenced by external factors related with products’ characteristics and costumers’ requirements and internal factors related with processing times variability, machine failures, setup times, between others. To face this increasing complexity and dynamics, it is crucial to have effective production control methods, considering Interoperability Enablers for Cyber-Physical Systems. However, production control methods most in used today, are focused on centralised decision-making and planning, and considered inadequate to deal with the increasing dynamics of these systems. Autonomous Production Control (APC) may be an adequate alternative to face this complexity, allowing flexible and rapid reaction to possible disturbances that may occur in the production system. However, as APC is the relatively new concept, there are no existing surveys. Therefore, we review and discuss the literature on APC methods to bring more attention to this promising topic of research, highlighting future research directions.

ACS Style

Luis Martins; Maria L. R. Varela; Nuno O. Fernandes; Sílvio Carmo–Silva; José Machado. Literature review on autonomous production control methods. Enterprise Information Systems 2020, 14, 1219 -1231.

AMA Style

Luis Martins, Maria L. R. Varela, Nuno O. Fernandes, Sílvio Carmo–Silva, José Machado. Literature review on autonomous production control methods. Enterprise Information Systems. 2020; 14 (8):1219-1231.

Chicago/Turabian Style

Luis Martins; Maria L. R. Varela; Nuno O. Fernandes; Sílvio Carmo–Silva; José Machado. 2020. "Literature review on autonomous production control methods." Enterprise Information Systems 14, no. 8: 1219-1231.

Journal article
Published: 01 January 2020 in International Journal of Industrial and Systems Engineering
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Social network analysis (SNA) is a widely studied research topic, which has been increasingly applied for solving different kinds of problems, including industrial manufacturing ones. This paper focuses on the application of SNA to an industrial plant layout problem. The study aims at analysing the importance of using SNA techniques to study the important relations between entities in a manufacturing environment, such as jobs and resources in the context of industrial plant layout analysis. Here, performance measures such as maximum completion time of jobs (makespan), resource utilisation, and throughput time have been considered to evaluate the system performance. Later, with the simulation analysis, the relationships between entities and their impact on the system performance are evaluated. The experimental results revealed that the proposed SNA approach supports to find the key machines of the systems that ultimately lead to the effective performance of the whole system. Finally, the identification of relations among these entities supported the establishment of an appropriate plant layout for producing the jobs in the context of industry 4.0.

ACS Style

Maria Leonilde Varela; Vijay Kumar Manupati; Suraj Panigrahi; Eric Costa; Goran D. Putnik. Using social network analysis for industrial plant layout analysis in the context of industry 4.0. International Journal of Industrial and Systems Engineering 2020, 34, 1 .

AMA Style

Maria Leonilde Varela, Vijay Kumar Manupati, Suraj Panigrahi, Eric Costa, Goran D. Putnik. Using social network analysis for industrial plant layout analysis in the context of industry 4.0. International Journal of Industrial and Systems Engineering. 2020; 34 (1):1.

Chicago/Turabian Style

Maria Leonilde Varela; Vijay Kumar Manupati; Suraj Panigrahi; Eric Costa; Goran D. Putnik. 2020. "Using social network analysis for industrial plant layout analysis in the context of industry 4.0." International Journal of Industrial and Systems Engineering 34, no. 1: 1.

Journal article
Published: 25 November 2019 in International Journal for Quality Research
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ACS Style

Leonilde Varela; Gabriela Amaral; Sofia Pereira; Diogo Machado; António Falcão; Rita A. Ribeiro; Emanuel Sousa; Jorge A. Santos; Alfredo F. Pereira; Goran D. Putnik; Luís Ferreira; Cátia Alves. DECISION SUPPORT VISUALIZATION APPROACH IN TEXTILE MANUFACTURING A CASE STUDY FROM OPERATIONAL CONTROL IN TEXTILE INDUSTRY. International Journal for Quality Research 2019, 13, 987 -1004.

AMA Style

Leonilde Varela, Gabriela Amaral, Sofia Pereira, Diogo Machado, António Falcão, Rita A. Ribeiro, Emanuel Sousa, Jorge A. Santos, Alfredo F. Pereira, Goran D. Putnik, Luís Ferreira, Cátia Alves. DECISION SUPPORT VISUALIZATION APPROACH IN TEXTILE MANUFACTURING A CASE STUDY FROM OPERATIONAL CONTROL IN TEXTILE INDUSTRY. International Journal for Quality Research. 2019; 13 (4):987-1004.

Chicago/Turabian Style

Leonilde Varela; Gabriela Amaral; Sofia Pereira; Diogo Machado; António Falcão; Rita A. Ribeiro; Emanuel Sousa; Jorge A. Santos; Alfredo F. Pereira; Goran D. Putnik; Luís Ferreira; Cátia Alves. 2019. "DECISION SUPPORT VISUALIZATION APPROACH IN TEXTILE MANUFACTURING A CASE STUDY FROM OPERATIONAL CONTROL IN TEXTILE INDUSTRY." International Journal for Quality Research 13, no. 4: 987-1004.