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28th ISTE International Conference on Transdisciplinary engineering, TE 2021

作者： De Oliveira, Michele Marcos Andreatta, Luisa Geraldi Stjepandić, Josip Junior, Osiris Canciglieri Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Paraná Curitiba - Paraná Brazil

ISBN: (纸本)9781643682082

The Fourth industrial Revolution and its disruptive technologies are emerging massively. With various motivations for its implementation such as elevation of speed, reducing costs, mitigating errors, and other different factors, more and more technology are being employed to solve business problems and increase profit. Through the years, product lifecycles have been shorted as a good reason to reduce production costs and give to consumers the experience of replacing goods in shorter periods. This linear cycle of 'produce-use-dispose' reached a massive success in the last decades, but have started to show lack of sustainability for consumer and industry. This work aims to conduct a Systematic Literature Review about the future challenges for Product Lifecycle Management and Sustainable Development, considering the context of Industry 4.0. The expected outcome for this study is to find elements that can support the use of 4.0 technologies in Product Lifecycle Management with a strong background for Sustainable Development. © 2021 The authors and IOS Press.

关键词： Industry 4.0

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Different approaches on how to improve braking performance in M3 vehicles to meet secondary brake requirements from ECE R13 regulation through low cost solutions 14

Different approaches on how to improve braking performance i...

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14th SAE Brasil International Brake and Motion Control Colloquium and Exhibit, BRASILBRAKE 2019

作者： Da Cruz, Luciano Ferreira Rudek, Claudemir Freire, Roberto Zanetti Volvo Bus Corporation Volvo Trucks Technology Industrial and Systems Engineering Graduate Program PUCPR Brazil

The braking system of any road vehicle is subject to extensive legislative standards and requirements in many regions around the world. In buses, which belong to M3 category, one of the most critical demands is occupancy rate of passengers that leads to affect Gross Vehicle Weight (GVW). More and more, customers are pushing the manufactures to increase the GVW of buses to allow more passengers inside and consequently increase profitability of the operation. By increasing GVW braking system has to present better performance to fulfill requirements. ECE R13 regulation establishes 2.5m/s2 as minimum Mean Fully Developed Deceleration (MFDD) and 64,4meters as maximum Stopping Distance (SD) acceptable to secondary brake performance. In a 4x2 vehicle with GVW of 19.5 tons fitted with disc brakes these requirements are actually fulfilled, but by adding 600 kg it became out of requirements. Based on the parameters which might affect braking performance, three different approaches were studied, tested and disposed in a decision matrix to choose most feasible in terms of performance, costs, manufacturing and time to implementation: a new protecting valve setup, increasing residual pressure at port 21 and 22;a new pneumatic scheme using a Double Check Valve (DCV) to share residual pressure from non- failed circuit to failed circuit;and finally an exchange of 24" service brake chambers by a 27" in both axles, being those solutions preselected as feasible solutions in terms of less impacts to brake system. Tests were performed considering failure at front (FA) and rear (RA) axles. By taking baseline results with MFDD 5.49% and 1.63%, and SD 3.09% and 4.72% out of requirements for FA and RA failures respectively, test results for proposed solutions showed in a ranking: DCV solution as the best performance, which had improved MFDD by 50.56% and 51.92%, and SD had decreased by 67.51% and 59.44%;27" service chambers as second best solution, which had improved MFDD by 17.80% and 16.23

关键词： Braking performance

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Towards Real-Time Machining Tool Failure Forecast Approach for Smart Manufacturing systems

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IFAC-PapersOnLine 2022年第2期55卷 548-553页

作者： Nicolas Nebelung Mario D.S. de Oliveira Santos Sofia T. Helena Athon F.C.S. de Moura Leite Matheus B. Canciglieri Anderson L. Szejka Control and Automation Engineering Department Pontifical Catholic University of Paraná (PUCPR) Curitiba Brazil Junior Scientific Initiation Program at the Nossa Senhora de Medianeira School and Pontifical Catholic University of Paraná Curitiba – Paraná Brazil Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Paraná (PUCPR) Curitiba Brazil

Industry 4.0 is characterized by a dynamic market that constantly looking for new methods to optimize and integrate manufacturing processes. In this context, Artificial Intelligence has gained prominence in problem-solving, such as failure prediction and decision making, thus improving product quality, and consequently bringing competitiveness to the company. Aiming to contribute to this scenario, this research develops a data treatment system that, from an intelligent tool and an interoperable ontological model, automates the prediction and detection of failures in machining machines lines. The system was developed for the prediction of faults in machining lines includes an artificial intelligence formed from prediction algorithms and inferences, it is possible to guarantee the correct treatment and communication of data at different stages of the process. For the experimental research, was used data collected from a machining line of a public dataset. The information is collected and classified by Artificial Intelligence that supports a decision system. The prediction of tool wear would enable the system to infer the type of problem that is causing this wear, a possible root cause, and the needed maintenance based on the ontological inference tool. By this classification of data, it is possible to achieve, through inferences, a reduction in the decision scope, bringing the possible problems caused by the incoming value. The semantic interoperability ensures correct data exchange and processing, which generates a more assertive view of production failures. The system may help companies to increase their productive process by helping them identify future failures in production if applied in a real scenario.

关键词： Industry 4.0 Smart System Artificial Intelligence Machine Learning Ontology Machine Failure Forecast

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Distributionally Robust Performative Optimization

arXiv

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arXiv 2024年

作者： Jia, Zhuangzhuang Wang, Yijie Dong, Roy Hanasusanto, Grani A. Department of Industrial and Enterprise Systems Engineering University of Illinois Urbana-Champaign United States Graduate Program in Operations Research and Industrial Engineering The University of Texas Austin United States Department of Industrial and Enterprise Systems Engineering University of Illinois Urbana-Champaign United States Department of Industrial and Enterprise Systems Engineering University of Illinois Urbana-Champaign United States

In this paper, we propose a general distributionally robust framework for performative optimization, where the selected decision can influence the probabilistic distribution of uncertain parameters. Our framework facilitates safe decision-making in scenarios with incomplete information about the underlying decision-dependent distributions, relying instead on accessible reference distributions. To tackle the challenge of decision-dependent uncertainty, we introduce an algorithm named repeated robust risk minimization. This algorithm decouples the decision variables associated with the ambiguity set from the expected loss, optimizing the latter at each iteration while keeping the former fixed to the previous decision. By leveraging the strong connection between distributionally robust optimization and regularization, we establish a linear convergence rate to a performatively stable point and provide a suboptimality performance guarantee for the proposed algorithm. Finally, we examine the performance of our proposed model through an experimental study in strategic classification. © 2024, CC BY.

关键词： Optimization

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A Framework of Risk Profile in Public Healthcare System Development: A Literature Review 2nd

A Framework of Risk Profile in Public Healthcare System Deve...

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2nd International Symposium on Disaster Resilience and Sustainable Development, 2021

作者： Krutphong, Kodchakorn Tang, Jing Leelawat, Natt Department of Industrial Engineering Faculty of Engineering Chulalongkorn University Bangkok Thailand Disaster and Risk Management Information Systems Research Unit Chulalongkorn University Bangkok Thailand Risk and Disaster Management Program Graduate School Chulalongkorn University Bangkok Thailand International School of Engineering Faculty of Engineering Chulalongkorn University Bangkok Thailand

ISBN: (纸本)9789811947148

Risk profile has been utilized in a variety of knowledge fields beyond disaster management or risk management it also plays as the tools in assessment process before the planning both in operation and *** public healthcare system is considered as one of the most critical stakeholders during crises such as disasters and *** risk profile in the public healthcare system requires experts to help developing it according to its *** to a limited number of reviews in the field, this review study focuses on the public healthcare system risk profile’s guideline and development from well-known research *** study also provides the SWOT analysis to realize the strengths, weaknesses, opportunities, and threats of each specific guideline for further *** research results found the ratio of the research in the keywords of the risk profile mostly in the field of medicine and rising number since 2011–*** order to develop the risk profile in such a specific area, it is necessary to use the strength part which suits with the appropriate area. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Risk management

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Virtual Wearable Sensor Data Generation with Generative Adversarial Networks 23

Virtual Wearable Sensor Data Generation with Generative Adve...

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Proceedings of the Winter Simulation Conference

作者： Yining Huang Hong Wan Xi Chen Operations Research Graduate Program North Carolina State University Raleigh USA Department of Industrial and Systems Engineering North Carolina State University Raleigh USA Grado Department of Industrial and Systems Engineering Virginia Tech Blacksburg USA

ISBN: (纸本)9798350369663

This study delves into the utilization of Generative Adversarial Networks (GANs) for generating subject-specific time series sensor data, offering an innovative alternative to traditional metamodel-based simulations. We undertake an in-depth analysis of DoppelGANger, a prominent GAN variant for time series data and metadata generation, evaluating its efficiency and efficacy. The sensor data for this investigation was sourced from the National Health and Nutrition Examination Survey, which served as the foundational training set. We scrutinized the synthesized sensor data corresponding to various physical attributes, focusing on the temporal and multi-dimensional statistical properties. Our empirical findings underscore the potential of GANs to adeptly capture the time-dependent correlations and the intricate statistical characteristics inherent in multi-dimensional data. This insight into GANs' capabilities is a crucial step towards more sophisticated synthetic data generation, with significant implications for future applications in wearable technology and personalized health monitoring systems.

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Artificial Intelligence-Based Recommendation System for Detecting and Diagnosing Broken Bars in Induction Motors Under Transient Operation

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IFAC-PapersOnLine 2024年第19期58卷 1156-1161页

作者： Narco A.R. Maciejewski Roberto Z. Freire Anderson L. Szejka Thiago P.M. Bazzo Sofia M.A. Lopes Rogério A. Flauzino Department of Electrical Engineering - DAELT-CT Universidade Tecnológica Federal do Paraná - UTFPR Curitiba PR 80230-90 Brazil Production and Systems Engineering - PPGEPS-PB Energy Systems - PPGSE-CT Industrial and Systems Engineering Graduate Program - PPGEPS Pontifical Catholic University of Parana - PUCPR Curitiba PR 80215-901 Brazil Electrical Engineering - PPGEE-SC São Carlos School of Engineering - EESC Universidade de São Paulo - USP São Carlos SP 13566-590 Brazil

Three-phase induction motors are the main elements for converting electrical energy into mechanical energy and are extensively used in industry. Reducing maintenance costs becomes an incentive for developing systems capable of identifying defects. This research proposes a framework for recommending machine learning algorithms that diagnose and detect broken bar defects in three-phase induction motors under transient operation based on artificial intelligence. Employing experimental data, features were extracted and selected based on current, voltage, and vibration. A protocol of insertion of white noise showed that the proposed framework admitted 80% of noise without losing the predictive capacity based on a multicriteria performance measure.

关键词： condition monitoring fault detection fault classification machine learning maintenance engineering smart systems three-phase induction motor

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A Mixed Integer Linear programming for COVID-19 Related Medical Waste Reverse Logistics Network Design 22

A Mixed Integer Linear Programming for COVID-19 Related Medi...

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4th International Conference on Management Science and industrial engineering, MSIE 2022

作者： Santos, Aira Ronalie P. Maghfiroh, Meilinda Fitriani Nur Sapiter, John Rayam Prasetyo, Yogi Tri Redi, Anak Agung Ngurah Perwira Persada, Satria Fadil Young, Michael Nayat Nadlifatin, Reny Ardiansyahmiraja, Bobby School of Industrial Engineering and Engineering Management Mapua University Manila Philippines Department of Industrial Engineering Universitas Islam Indonesia Yogyakarta Indonesia Industrial Engineering Department Binus Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia Department of Information Systems Institut Teknologi Sepuluh Nopember Surabaya Indonesia Faculty of Business and Economics Universitas Surabaya Surabaya Indonesia

ISBN: (纸本)9781450395816

Medical wastes bring probable hazards and public risks if not handled correctly, especially during the covid-19 pandemic. Waste management costs are rising due to continues devastation of covid-19 virus. Increased care and handling processes have been implemented to avoid further spreading the infection while ensuring the proper disposal. Given this, medical waste related to covid-19, including other wastes generated by medical facilities, requires delicate integration into the waste management process thru a reverse logistics network for cost-efficient collection, processing, and transport style. Using AMPL software as solving tool, this paper was designed to utilize a mixed-integer linear programming model of a reverse logistics network to improve medical waste management by identifying the total minimum cost of the waste disposal cycle from Waste generating facilities, treatment facilities, and to the disposal factory. © 2022 ACM.

关键词： COVID-19

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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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Selection of industry 4.0 Technologies: A research agenda

Selection of industry 4.0 Technologies: A research agenda

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IISE Annual Conference and Expo 2021

作者： Santos, Reginaldo Carreiro de Lima, Edson Pinheiro da Costa, Sérgio Eduardo Gouvêa Deschamps, Fernando Industrial and Systems Engineering Graduate Program Pontifícia Universidade Católica do Paraná PR Curitiba Brazil

ISBN: (纸本)9781713838470

The technological paradigm shift is supporting the digital transformation of manufacturing, characterized by emerging industry 4.0 technologies, and the inherent complexity in selecting appropriate technologies. This increasing complexity of technologies, the multiple and conflicting decision criteria, and the abundance of technology alternatives demand a structured process for managers to select technologies to be acquired and implemented. This work derived from the observation of a real problem in a recognized world class automaker company. The identified problem consists in a lack of a consolidated process (procedure) to support managers and specialists in selecting industry 4.0 technologies. This paper aims at extending theory concerning technology management and selection, by proposing a research agenda regarding the solution for the defined problem. A systematic literature review (SLR) is performed to support it, comprising the analysis and synthesis of a review portfolio. The construction of a structured procedure for industry 4.0 technology selection would serve two purposes: (i) to identify the most suitable technology to solve a specific problem, (ii) to support the creation of a technology roadmap to ground a long-term investment plan. The procedure should be able to deal with both purposes to support firms in a digital transformation journey, enabling an objective, rational and traceable decision-making process. © 2021 IISE Annual Conference and Expo 2021. All rights reserved.

关键词： Industry 4.0

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