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Determining Factors Affecting Perceived Quality among Shoe Manufacturing Workers towards Shoe Quality: A Structural Equation Modeling Approach

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Journal of Open Innovation: Technology, Market, and Complexity 2022年第2期8卷

作者： Chuenyindee, Thanatorn Torres, Robimarl B. Prasetyo, Yogi Tri Nadlifatin, Reny Persada, Satria Fadil Department of Industrial Engineering and Aviation Management Navaminda Kasatriyadhiraj Royal Air Force Academy Bangkok 10220 Thailand School of Industrial Engineering and Engineering Management Mapúa University Manila 1002 Philippines School of Graduate Studies Mapúa University Manila 1002 Philippines Department of Information Systems Institut Teknologi Sepuluh Nopember Kampus ITS Sukolilo Surabaya 60111 Indonesia Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Malang 65154 Indonesia

The shoe industry has been continuously growing in recent years. The goal of this study was to identify factors affecting perceived shoe quality among shoe manufacturing workers. A sample of 350 shoe manufacturing workers participated in answering a survey that was distributed using a purposive sampling approach. Structural Equation Modeling (SEM) indicated that quality training was the most significant factor on perceived shoe quality, indicating that quality training should be prioritized and included in company programs to sustain quality products. Interestingly, perceived tooling/machinery conditions, teamwork and cooperation, the operator’s technical skills, and the operator’s quality mindset, also had significant effects on perceived shoe quality. This study is one of the first studies to explore perceived shoe quality among shoe manufacturing workers. The results may convey information for future research on perceived product quality, particularly for the shoe industry. Finally, our framework can be utilized for the enhancement of perceived product quality in shoe manufacturing industries worldwide. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

关键词： perceived quality shoe quality Structural Equation Modeling

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Improved multiobjective differential evolution with spherical pruning algorithm for optimizing 3D printing technology parametrization process

Research Square

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Research Square 2021年

作者： Cruz, Luciano Ferreira Pinto, Flavia Bernardo Camilotti, Lucas Santanna, Angelo Marcio Oliveira Freire, Roberto Zanetti Coelho, Leandro dos Santos Volvo of Brazil Vehicles Juscelino Kubitschek de Oliveira 2600 Curitiba81260-900 Brazil Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana Imaculada Conceição 1155 Curitiba80215-901 Brazil Industrial Engineering Graduate Program Federal University of Bahia prof. Aristides Novis 2 Salvador41250-360 Brazil Department of Electrical Engineering Federal University of Paraná Cel. Francisco Heráclito dos Santos 100 Curitiba81531-980 Brazil

Multiobjective optimization approaches have allowed the improvement of technical features in industrial processes, focusing on more accurate approaches for solving complex engineering problems and support decision-making. This paper proposes a hybrid approach to optimize the 3D printing technology parameters, integrating the design of experiments and multiobjective optimization methods, as an alternative to classical parametrization design used in machining processes. Alongside the approach, a multiobjective differential evolution with uniform spherical pruning (usp-MODE) algorithm is proposed to serve as an optimization tool. The parametrization design problem considered in this research has the following three objectives: to minimize both surface roughness and dimensional accuracy while maximizing the mechanical resistance of the prototype. A benchmark with nondominated sorting genetic algorithm II (NSGA-II) and with the classical sp-MODE is used to evaluate the performance of the proposed algorithm. With the increasing complexity of engineering problems and advances in 3D printing technology, this study demonstrates the applicability of the proposed hybrid approach, finding optimal combinations for the machining process among conflicting objectives regardless of the number of decision variables and goals involved. To measure the performance and to compare the results of metaheuristics used in this study, three Pareto comparison metrics have been utilized to evaluate both the convergence and diversity of the obtained Pareto approximations for each algorithm: hyper-volume (H), g-Indicator (G), and inverted generational distance (IGD). To all of them, ups-MODE outperformed, with significant figures, the results reached by NSGA-II and sp-MODE algorithms. © 2021, CC BY.

关键词： Multiobjective optimization

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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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Control of a Refrigeration System Benchmark Problem: An Approach based on COR Metaheuristic Algorithm and TOPSIS Method

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IFAC-PapersOnLine 2019年第11期52卷 85-90页

作者： Ricardo Massao Kagami Gilberto Reynoso-Meza Eduardo A.P. Santos Roberto Zanetti Freire Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Paraná (PUCPR) Brazil

The Proportional-Integral-Derivative (PID) controller is widely used in industrial applications. In order to evaluate the performance of distinct types of controllers, several benchmark systems are available in the specialized literature. In this work, an alternative approach to tune PID controllers based on the Competition Over Resources (COR) metaheuristic optimization algorithm and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method is proposed to solve a vapor compression refrigeration system problem, which is a benchmark control challenge. The proposed technique considered the normalization of multiple cost functions to simplify the selection of two PIDs’ parameters, reducing the need of understanding the influences of cost functions variations in complex problems. The proposed controllers outperformed the base line controllers, providing a performance index 68.39% better than the original approach. Finally, the proposed method was compared with other tuning methods.

关键词： Competition over resources algorithm controller tuning multi-objective problem multivariable control

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Electricity energy price forecasting based on hybrid multi-stage heterogeneous ensemble: Brazilian commercial and residential cases

Electricity energy price forecasting based on hybrid multi-s...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Matheus Henrique Dal Molin Ribeiro Ramon Gomes da Silva Cristiane Canton Naylene Fraccanabbia Viviana Cocco Mariani Leandro dos Santos Coelho Industrial & Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Parana (PUCPR) Curitiba Parana Brazil Department of Mathematics Federal Technological University of Parana (UTFPR) Pato Branco Parana Brazil Pato Branco University Center (UNIDEP) Pato Branco Parana Brazil Mechanical Engineering Graduate Program (PPGEM) Pontifical Catholic University of Parana (PUCPR) Curitiba Parana Brazil Department of Electrical Engineering Federal University of Parana (UFPR) Curitiba Parana Brazil

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

The development of accurate models to forecast electricity energy prices is a challenge due to the number of factors which can affect this commodity. In this paper, a hybrid multi-stage approach is proposed to forecast multi-stepahead (one, two and three-month-ahead) Brazilian commercial and residential electricity energy prices. The proposed data analysis combines the pre-processing named complementary ensemble empirical mode decomposition (CEEMD) in the first stage coupled with the coyote optimization algorithm (COA) to define the CEEMD's hyperparameters, aiming to deal with time series non-linearities and enhance the model's performance. On the next stage, four machine learning models named extreme learning machine, Gaussian process, gradient boosting machine, and relevance vector machine are employed to train and predict the CEEMD's components. Finally, in the final stage, the results of the previous step are directly integrated to compose a heterogeneous ensemble learning of components to obtain the final forecasts. In this case, a grid of models is obtained. The best model is one that has better generalization out-of-sample. Through developed comparisons, results showed that combining COA-CEEMD with a heterogeneous ensemble learning can develop accurate forecasts. The modeling developed in this paper is promising and can support decision making in electricity energy price forecasting.

关键词： Predictive models Biological system modeling Forecasting Data models Optimization Empirical mode decomposition

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The Evaluation of the Local Beer Industry during the COVID-19 Pandemic and Its Relationship with Open Innovation

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Journal of Open Innovation: Technology, Market, and Complexity 2022年第3期8卷

作者： Ong, Ardvin Kester S. Pequeña, Arianne R. Prasetyo, Yogi Tri Chuenyindee, Thanatorn Buaphiban, Thapanat Persada, Satria Fadil Nadlifatin, Reny School of Industrial Engineering and Engineering Management Mapua University 658 Muralla St. Intramuros Manila 1102 Philippines School of Graduate Studies Mapúa University 658 Muralla St. Intramuros Manila 1002 Philippines Department of Industrial Engineering and Management Yuan Ze University 135 Yuan-Tung Road Chung-Li 32003 Taiwan Department of Industrial Engineering and Aviation Management Navaminda Kasatriyadhiraj Royal Air Force Academy Bangkok 10220 Thailand BINUS Business School Undergraduate Program Entrepreneurship Department Bina Nusantara University Jakarta 11480 Indonesia Department of Information Systems Institut Teknologi Sepuluh Nopember Kampus ITS Sukolilo Surabaya 60111 Indonesia

The beer industry is one of the businesses affected during the COVID-19 pandemic. Despite the exponential growth of the beer industry throughout the years, this aspect of the beverage industry has gained limited attention and has been underexplored. This study aimed to provide a better and up-to-date understanding of Philippine-beer consumers to speed up its recovery. An online survey with 853 volunteer respondents was conducted to investigate Filipinos’ local beer consumption considering frequency, intake, expenses, and preference. A descriptive analysis of the consumers’ self-perceived evaluation of the changes in drinking showed a slight decrease in frequency, intake, and expenses and a minor change in preference. Somers’ d and the chi-squared test results indicated significant relationships between each demographic information (age, sex, and income) and frequency, intake, and expenses. In addition, a conjoint analysis with an orthogonal design indicated that price was the most important attribute (58.025%), followed by primary taste (12.452%), alcohol content (9.706%), mouthfeel (6.445%), aftertaste (6.355%), aroma (5.189%), and, lastly, color (1.827%). The findings of this study could be used as a baseline for improved product offerings, customized advertisements, and market segmentation. Moreover, the results of this study could be applied and extended by breweries to promote and create strategies. Lastly, this study could be extended and utilized by other beverage industries worldwide. © 2022 by the authors.

关键词： beer conjoint analysis consumer preference COVID-19 pandemic expenditure

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A Normal Distribution Transform-Based Radar Odometry Designed For Scanning and Automotive Radars

arXiv

引用

arXiv 2021年

作者： Kung, Pou-Chun Wang, Chieh-Chih Lin, Wen-Chieh Graduate Degree Program of Robotics National Chiao Tung University Hsinchu Taiwan Department of Electrical and Computer Engineering National Chiao Tung University The Mechanical and Mechatronics Systems Research Laboratories Industrial Technology Research Institute Hsinchu Taiwan Department of Computer Science National Chiao Tung University Hsinchu Taiwan

Existing radar sensors can be classified into automotive and scanning radars. While most radar odometry (RO) methods are only designed for a specific type of radar, our RO method adapts to both scanning and automotive radars. Our RO is simple yet effective, where the pipeline consists of thresholding, probabilistic submap building, and an NDT-based radar scan matching. The proposed RO has been tested on two public radar datasets: the Oxford Radar RobotCar dataset and the nuScenes dataset, which provide scanning and automotive radar data respectively. The results show that our approach surpasses state-of-the-art RO using either automotive or scanning radar by reducing translational error by 51% and 30%, respectively, and rotational error by 17% and 29%, respectively. Besides, we show that our RO achieves centimeter-level accuracy as lidar odometry, and automotive and scanning RO have similar accuracy. Copyright © 2021, The Authors. All rights reserved.

关键词： Optical radar

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A Machine Learning Method for Vehicle Classification by Inductive Waveform Analysis ⁎

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IFAC-PapersOnLine 2020年第2期53卷 13928-13932页

作者： Bruno R. Vasconcellos Marcelo Rudek Marcelo de Souza Pontifícia Universidade Católica do Paraná - PUCPR Control and Automation Egineering Curitiba PR 80215-901 Pontifícia Universidade Católica do Paraná - PUCPR Industrial and Systems Engineering Graduate Program Curitiba PR 80215-901 Fiscaltech Curitiba PR 81290-270

The classification of vehicles is a matter of great importance for traffic control and management, helping with traffic surveillance as well as in statistical data collection. Among the several vehicular classification techniques, the most popular uses inductive loop sensors, because they achieve high accuracy rate at low cost. This paper proposes 5 different vehicle classification models by inductive waveform analysis: KNN, SVC, Decision Tree, Random Forest, and Voting Classifier. A brief introduction to the mathematical basis of these models and the main forms of vehicle detection are also presented. The obtained results reached an accuracy of 94% and showed how inductive waveform analysis is still a valid option for vehicle classification.

关键词： Machine learning Intelligent transportation systems Information processing Decision support

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Corrigendum to “Techno-economic model to appraise the use of cattle manure in biodigesters in the generation of electrical energy and biofertilizer” [Biomass Bioenergy 150 (2021) 1–11]

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Biomass and Bioenergy 2023年 179卷

作者： Simone Andreia Guares José Donizetti de Lima Gilson Adamczuk Oliveira Industrial and Systems Engineering Graduate Program (PPGEPS) UTFPR Pato Branco Parana Brazil Department of Mathematics Federal Technological University of Parana (UTFPR) Pato Branco Parana Brazil Department of Mechanical Engineering Federal Technological University of Parana (UTFPR) Pato Branco Parana Brazil

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Consumer behavior in clothing industry and its relationship with open innovation dynamics during the covid-19 pandemic

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Journal of Open Innovation: Technology, Market, and Complexity 2021年第4期7卷 211页

作者： Ong, Ardvin Kester S. Cleofas, Maria Arielle Prasetyo, Yogi Tri Chuenyindee, Thanatorn Young, Michael Nayat Diaz, John Francis T. Nadlifatin, Reny Redi, Anak Agung Ngurah Perwira School of Industrial Engineering and Engineering Management Mapúa University 658 Muralla St. Manila 1002 Philippines School of Graduate Studies Mapúa University 658 Muralla St. Intramuros Manila 1002 Philippines Department of Industrial Engineering Faculty of Engineering University of Santo Tomas Manila 1015 Philippines Navaminda Kasatriyadhiraj Royal Thai Air Force Academy Bangkok 10220 Thailand Department of Finance and Accounting Asian Institute of Management Manila 1229 Philippines Department of Information Systems Institut Teknologi Sepuluh Nopember Kampus ITS Sukolilo Surabaya 60111 Indonesia Industrial Engineering Department BINUS Graduate Program—Master of Industrial Engineering Bina Nusantara University Jakarta 11480 Indonesia

The fashion industry has been continuously growing over the years, yet it is an industry that was greatly affected by the COVID-19 pandemic. The purpose of the study was to determine the factors affecting buying behavior of Filipinos towards clothing apparel during the COVID-19 pandemic. There were 457 respondents who voluntarily participated and answered an online questionnaire. Structural equation modeling (SEM) indicated that marketing mix was found to have significant effects on attitude, subjective norm, and perceived behavioral control, which subsequently led to purchase intention. In addition, the COVID-19 latent variable was found to have significant effects on self-perceived severity and self-efficacy, which subsequently led to attitude and purchase intention. Interestingly, marketing mix was found to have the highest effect on actual purchase, which indicated that innovation dynamics are the keys for the buying behavior. The SEM construct can be applied to determine the clothing apparel buying behaviors of consumers in other countries, particularly during the COVID-19 pandemic. Finally, advertisements, promotions, sales, and health safety should be considered as innovation dynamics during the COVID-19 pandemic. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

关键词： Consumer behavior COVID-19 pandemic Fashion industry Innovation dynamic Open innovation Protection motivation theory Theory of planned behavior

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