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21st International Conference on Production Research: Innovation in Product and Production, ICPR 2011

作者： Pinheiro De Lima, E. Gouvea Da Costa, S.E. Angelis, J.J. Munik, J. Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana Imaculada Conceicao St. 1155 Curitiba Parana Brazil Federal University of Technology Parana Curitiba Brazil Operations Management Group Warwick Business School University of Warwick Scarman Road Coventry United Kingdom

ISBN: (纸本)9783839602935

Contingency factors and globalization phenomena are forcing companies to revise the way they conceive and manage their operations network. Operations strategic management systems should develop specific design requirements to deal with a complex and changing environment, that is, operations strategy plans should be continuously assed and updated. This paper frame a discussion based on operations strategy and performance measurement framework. Performance measurement roles are revised in order to assess their alignment to new design requirements for operations strategic management. Based on a set of updated performance measurement systems roles, a case study protocol is developed and applied to manufacturing and service companies. Although classical roles as performance monitoring and control are still the main focus of operations performance measurement systems, evidences collected show a growing use of these management systems for continuous improvement and change management processes.

关键词： Strategic planning

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Diversity-Guided Generalized Extremal Optimization for Transformer Design Problem

Diversity-Guided Generalized Extremal Optimization for Trans...

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IEEE Symposium Series on Computational Intelligence

作者： Leandro dos S. Coelho Viviana C. Mariani Rafael B. Grebogi Emerson H. de Vasconcelos Segundo Mauricio V. Ferreira da Luz Jean V. Leite Roberto Z. Freire Industrial and Systems Engineering Graduate Program Electrical Engineering Federal University of Parana - UFPR Curitiba Brazil Mechanical Engineering Graduate Program Electrical Engineering Federal University of Parana - UFPR Curitiba Brazil Mechanical Engineering Graduate Program Federal Institute of Santa Catarina - IFSC Lages Brazil Mechanical Engineering Graduate Program Pontifical Catholic University of Parana - PUCPR Curitiba Brazil GRUCAD-EEL-CTC Federal University of Santa Catarina - UFSC Florianópolis Brazil Federal University of Santa Catarina - UFSC Florianópolis Brazil Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana - PUCPR Curitiba Brazil

Market competitiveness and pressure for rational use of resources led manufacturers to look for optimized electromagnetic devices. The large number of variables involving the design of this technology, as well as the compromise between performance and efficiency, have provide the opportunity to adopt optimization techniques in their design stage. Recently, a wide variety of both traditional and nontraditional mathematical algorithms have been introduced to solve challenging and practical optimization problems related to electromagnetic equipment. Evolutionary metaheuristics are general-purpose optimization algorithms inspired by natural self-organized systems and by biological evolution. An optimization method, called Extremal Optimization (EO), which is based on a simplified model of natural selection, was developed to show the emergence of self-organized criticality based on Bak-Sneppen model in ecosystems. Recently, a generalized version of EO method, called Generalized Extremal Optimization (GEO), was proposed. This technique can be applied to a broad class of design optimization problems. In order to enhance and to improve the search performance and efficiency of GEO, a novel Diversity-guided GEO (DGEO) metaheuristic algorithm, with focus in global continuous optimization, is proposed and investigated in this paper considering the design stage of an electromagnetic device. The analysis was carried for a single-phase transformer and revealed that DGEO provided considerable better performance in convergence speed and solution quality than standard GEO.

关键词： Metaheuristic optimization Transformer design Generalized extremal optimization Power-grid Power conversion

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Geometric modeling from the medical images in the CAD system to support prosthesis design

Geometric modeling from the medical images in the CAD system...

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International Conference on Computers & industrial engineering (CIE)

作者： Osiris Canciglieri M. Rudek Tiago Francesconi Teófilo Miguel de Souza Pontificia Universidade Catolica do Parana Curitiba PR BR Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Paraná Curitiba Parana Brazil Department of Manufacturing Engineering of Pontifical Industrial and Systems Engineering Graduate Program (PPGEPS) Curitiba PR Brazil Department of Electrical Engineering São Paulo State University (UNESP) Guaratinguetá SP Brazil

This paper presents an individual designing prosthesis for surgical use and proposes a methodology for such design through mathematical extrapolation of data from digital images obtained via tomography of individual patient's bones. Individually tailored prosthesis designed to fit particular patient requirements as accurately as possible should result in more successful reconstruction, enable better planning before surgery and consequently fewer complications during surgery. Fast and accurate design and manufacture of personalized prosthesis for surgical use in bone replacement or reconstruction is potentially feasible through the application and integration of several different existing technologies, which are each at different stages of maturity. Initial case study experiments have been undertaken to validate the research concepts by making dimensional comparisons between a bone and a virtual model produced using the proposed methodology and a future research directions are discussed.

关键词： Solid modeling Design automation Bones Tomography Prosthetics Surgery Image reconstruction

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Designing Lead-Lag PSS Employing Backtracking Search Algorithm to Improve Power System Damping

Designing Lead-Lag PSS Employing Backtracking Search Algorit...

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IEEE GCC Conference & Exhibition

作者： Md Shafiullah Md Juel Rana L. S. Coelho M. A. Abido Ahmad Al-Subhi Department of Electrical Engineering King Fahd University of Petroleum & Minerals Dhahran KSA Industrial and Systems Engineering Graduate Program Federal University of Parana Curitiba PR Brazil

ISBN: (纸本)9781538627570

The power system is a complex network having highly nonlinear dynamics. Being electrically interconnected power systems are vulnerable to instability due to the disturbances in any one portion of the system. Even low-frequency oscillations (LFOs) originated from small disturbances in any part of the network may cause the outage of the whole system. Power System Stabilizer (PSS) with optimal parameter settings is an efficient tool for the suppression of these oscillations. In this paper, a novel approach is proposed to tune lead-lag controller based PSS for a single machine infinite bus (SMIB) network by using backtracking search algorithm (BSA) to suppress LFOs through improvement of power system damping. The efficacy of BSA tuned PSS has been investigated by comparing the simulation results with fixed gain conventional PSS. In addition, the damping ratio and eigenvalues of the same network have been compared with genetic algorithm (GA) based tuned PSS.

关键词： Power system stability Sociology Statistics Damping Oscillators Lead Eigenvalues and eigenfunctions

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Solution of Jiles-Atherton vector hysteresis parameters estimation by modified Differential Evolution approaches

Solution of Jiles-Atherton vector hysteresis parameters esti...

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作者： Coelho, Leandro Dos Santos Mariani, Viviana Cocco Leite, Jean Vianei Industrial and Systems Engineering Graduate Program PPGEPS PUCPR Imaculada Conceião 1155 80215-901 Curitiba Parana Brazil Department of Mechanical Engineering Pontifical Catholic University of Parana PUCPR Imaculada Conceião 1155 80215-901 Curitiba Parana Brazil Department of Electrical Engineering GRUCAD UFSC Postal Box 476 88040-970 Florianopolis Santa Catarina Brazil

Differential Evolution (DE) is a simple and efficient stochastic global optimization algorithm of evolutionary computation field, which involves the evolution of a population of solutions using operators such as mutation, crossover, and selection. The basic idea of DE is to adapt the search during the evolutionary process. At the start of the evolution, the perturbations are large since parent populations are far away from each other. As the evolutionary process matures, the population converges to a small region and the perturbations adaptively become small. DE approaches have been successfully applied to solve a wide range of optimization problems. In this paper, the parameters set of the Jiles-Atherton vector hysteresis model is obtained with an approach based on modified Differential Evolution (MDE) approaches using generation-varying control parameters based on generation of random numbers with uniform distribution. Several evaluated MDE approaches perform better than the classical DE methods and a genetic algorithm approach in terms of the quality and stability of the final solutions in optimization of vector Jiles-Atherton vector hysteresis model from a workbench containing a rotational single sheet tester. © 2011 Elsevier Ltd. All rights reserved.

关键词： Hysteresis

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Data analytics in fleet operations: A systematic literature review and workflow proposal

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Procedia CIRP 2022年 107卷 1192-1197页

作者： Paulo Henrique Brunheroto André Luiz Gonçalves Pepino Fernando Deschamps Eduardo de Freitas Rocha Loures Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifícia Universidade Católica do Paraná Curitiba PR Brazil Volvo Group Connected Solutions Volvo do Brasil Veículos Curitiba PR Brazil Manufacturing Engineering Graduate Program (PPGEM) Federal University of Paraná Curitiba PR Brazil

Fleet operations have been one of the main concerns of a wide range of industries, particularly those industries in which transportation plays a pivotal role for operational continuity, such as logistics service providers and public transportation. In addition, accentuated by the social and economic globalization, new scenarios and higher requirements were introduced to the transportation sector and, therefore, the need for advanced techniques, such as optimization, traffic modeling, and machine learning, to improve fleet operations, is growing. As for machine learning, although an increasing number of data-driven methods are observed in manufacturing and mobility solutions, there is still a gap in terms of literature reviews related to machine learning applications in fleet operations. Based on this gap, a systematic literature review was conducted, and several applications related to machine learning in fleet operations were identified, most of them encompassing aspects related to predictive maintenance of mechanical systems and components. It was also identified that most of the studies were based on a single data source and did not approach aspects related to the deployment of the machine learning models and data analysis workflows. Therefore, based on the findings from this literature review, the present paper aims to present an overview of the main machine learning applications in fleet operations and propose a data analysis workflow for the connectivity monitoring of heavy vehicles based on multiple data sources.

关键词： fleet operations fleet management machine learning data analytics vehicular connectivity

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Evolutionary Multi-objective Optimization Design of a Butane Content Soft Sensor ⁎

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IFAC-PapersOnLine 2020年第2期53卷 7915-7920页

作者： Victor Henrique Alves Ribeiro Matheus Henrique Reis Marchioro Giberto Reynoso-Meza Industrial and Systems Engineering Graduate Program (PPGEPS) Pontificia Universidade Católica do Paraná (PUCPR) Rua Imaculada Conceição 1155 Zip code 80215-901 Curitiba PR Brazil

industrial processes must be well equipped with a variety of sensors to maintain a desired quality. However, some variables cannot be easily measured due to different causes, such as acquisition and/or maintenance costs and slow acquisition time. This situation leads to a lack of real-time information in the process, which could lead to lower quality in the final product. One of such processes is the debutanizer column, where butane content measurement is highly delayed. To enable online prediction of such variables, available information from the process can be used to estimate predictive models, known as soft sensors. To this end, data-driven techniques can be used, such as statistical and machine learning. However, such techniques usually take into account a single metric when estimating the models, and there are multiple factors that play an important role when designing a soft sensor, such as stability and accuracy. To cope with such a situation, this paper proposes a multi-objective optimization design procedure, where feature selection and ensemble member combination are performed. Therefore, the multi-objective differential evolution algorithm with spherical pruning (spMODE-II) is initially employed for building a pool of non-dominated linear support vector regression (SVR) models. Subsequently, the same evolutionary algorithm is applied for selecting the weights of the previously generated models in a weighted combination ensemble. In a final multi-criteria decision making stage, a preferred ensemble is selected using the preference ranking organization method for enrichment of evaluations (PROMETHEE). Results indicate that the proposed approach is able to produce a highly stable and accurate butane content soft sensor for the debutanizer column.

关键词： Soft sensor Machine learning Ensemble learning Evolutionary algorithms Multi-objective optimization Chemical industry Chemical sensors Monitoring

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Portfolio Selection Using Mean Variance Theory Based on the 30-year Historical Returns of Selected Subsectors of the Philippine industrial Sector

Portfolio Selection Using Mean Variance Theory Based on the ...

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2024 IEEE International Conference on industrial engineering and engineering Management, IEEM 2024

作者： Yamanaka, A.D. Young, M.N. Chuahay, T.N. Pratseyo, Y. Persada, S. Nadlifatin, R. School of Graduate Studies Mapua University Manila Philippines School of Industrial Engineering and Engineering Management Mapua University Manila Philippines Department of Finance Chung Yuan University Taoyuan City Taiwan Department of Industrial Engineering and Management Yuan Ze University Taoyuan City Taiwan International Bachelor Program in Engineering Yuan Ze University Taoyuan City Taiwan Entrepreneurship Business Creation Business School Binus University West Jakarta Indonesia Department of Information Systems Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (纸本)9798350386097

Portfolio selection is an important part of investment management that aims to select the right combination of assets to achieve an optimal risk-return ratio. This study integrates the Mean-Variance Theory utilizing the 30-year historical returns of the Philippine Stock Exchange. Three subsectors of the industrial Sector were used as the investment pool. The back-test results showed that the Mean-Variance portfolios of selected subsectors can outperform the industrial sector and not the market. Stakeholders must be cautious when investing in these three subsectors, considering the values obtained from the simulations. There are recommended portfolio selections wherein companies have a specific allocation that investors and financial managers can use whenever they decide to invest in these subsectors. Overall, this study offers a framework for investors to use as a guide in diversifying their stakes in the Philippine Stock Exchange. © 2024 IEEE.

关键词： Decentralized finance

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Real engineering Problems in an Undergraduate Course: The learning methodologies and assessment tools

Real Engineering Problems in an Undergraduate Course: The le...

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World engineering Education Forum - Global engineering Deans Council (WEEF-GEDC)

作者： Ricardo A. Diogo André L.A.C. Venâncio Maria Angela M. Roveredo Santos Eduardo F.R. Loures Neri dos Santos Knowledge Engineering&Management Graduate Program Federal University of Santa Catarina Florianopolis Brazil Industrial&Systems Engineering Graduate Program Pontifical Catholic University of Parana Curitiba Brazil Control and Automation Engineering/Mechatronics Engineering Pontifical Catholic University of Parana Curitiba Brazil

ISBN: (纸本)9781665424899

This paper presents the context of the Ubiquitous Computing course carried out in an industrial engineering undergraduate program throughout 2020 and the first semester of 2021. This course took advantage of the Institutional Modernization program led by the Control and Automation engineering Undergraduate program, which consists in the modernization of undergraduate engineering programs at the Pontifical Catholic University of Paraná. The course aimed to help develop competencies, i.e., a set of knowledge, skills, and attributes aligned with Student Outcomes suggested by ABET. Also, the course proposal brought the productive sector very close to the academic environment, proposing real engineering problems as challenges. However, what are the methods and assessment tools for practical learning in a course with modern elements? This paper proposed the flipped learning and the Challenge-Based Learning Framework with the support of the CDIO Framework as learning methodologies to answer such question. Additionally, quizzes, tests, presentations, rubrics, and peer evaluation were applied as assessment tools to measure the student's progress. Finally, students were listened to about their perceptions during the course. The results suggested that the learning methodologies and assessment tools are suitable for the course context, although some improvements are expected for the following course offers. Moreover, students approved the initiative to bring in real challenges proposed by companies, the mentoring hours, and the feedback about the projects.

关键词： Automation Companies industrial engineering Ubiquitous computing Proposals Mentoring engineering education

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Multi-objective control engineering benchmark ⁎

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IFAC-PapersOnLine 2020年第2期53卷 7927-7932页

作者： Gilberto Reynoso-Meza Jesús Carrillo-Ahumada Victor Henrique Alves Ribeiro Tyene Zoraski Zanella Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Paraná (PUCPR) Instituto de Química Aplicada. Universidad del Papaloapan Circuito Central 200 Colonia Parque Industrial Tuxtepec Oaxaca 68301 México

In this paper we are presenting the statement and evaluation guidelines of a control engineering benchmark, oriented for multi-objective optimisation design techniques. This is done with the aim of promoting new research on this field, by defining a benchmark to have reproducibility and comparability of the three steps involved in the multi-objective process: problem statement, optimisation process and multi-criteria decision making. The proposed benchmark is a single-input single-output process based on the Peltier effect. Rules and guidelines, merged with common practices in control systems engineering, are highlighted and disclosed in the multi-objective open invited track 2020.

关键词： Multi-objective optimisation Parametric optimisation Evolutionary Algorithms Intelligent Control Process Control

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