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24th International Conference on Production Research, ICPR 2017

作者： Okoshi, C.Y. de Lima, E. Pinheiro da Costa, S.E. Gouvêa Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana Rua Imaculada Conceição 1155 Prado Velho Curitiba Parana Brazil Industrial and Systems Engineering Graduate Program Federal University of Technology - Parana Via do Conhecimento km 1 Pato Branco Parana Brazil

ISBN: (纸本)9781605955070

The companies use the performance results to identify their main problems and to analyse the cause roots in their production systems, supplier network and process technology. The purpose of this paper is to analyze cause and effect relationships among the performance dimensions and decision areas in the framework of operations strategy. The explored dataset is a research project named 4th Round of High Performance Manufacturing Project. The dataset holds indicators from 304 manufacturing companies in 13 countries. The techniques used for the analysis were descriptive statistics, normality test, factor analysis, and Multiple Linear Regression. As main results, the following cause and effect correlations for each manufacture performance dimension were found: innovation, flexibility, quality, cost, speed, and reliability. The results support the building of cause and effect diagrams regarding performance dimensions and decision areas to be used on improvements to the companies. © 2017 DEStech Publications. All rights reserved.

关键词： Manufacture

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Heuristic Kalman Algorithm for Multiobjective Optimization.

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IFAC-PapersOnLine 2017年第1期50卷 4460-4465页

作者： Hultmann Ayala H.V. dos Santos Coelho L. Reynoso-Meza G. Industrial and Systems Engineering Graduate Program PPGEPS Polytechnic School Pontifical Catholic University of Paraná PUCPR Rua Imaculada Conceição 1155 Curitiba PR code 80215-901 Brazil Department of Electrical Engineering (PPGEE) Federal University of Parana (UFPR) Polytechnic Center Curitiba Parana 81531-970 Brazil

This papers presents a new heuristic multiobjective optimization procedure based on Kalman filtering. The novel approach is compared to the well-known Nondominated Sorting Genetic Algorithm version II with the Zitzler, Deb and Thiele test suite showing favorable results for the Kalman based solution. Further improvement on the proposed algorithm is made with respect to the learning factor, which is set to be time-dependent, improving the algorithm in most cases and diminishing the number of control parameters. The proposed multiobjective optimization algorithm is interesting due to its convergence and coverage quality, its computational complexity and few parameters to be adjusted. © 2017

关键词： loop pairing Multi-input/multi-output systems Multi-objective optimization Multiloop control

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Knowledge-Data Driven for Cyber-Physical Production systems in the Aerospace Industry: Current Issues and Emergent Technologies

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IFAC-PapersOnLine 2024年第19期58卷 1162-1167页

作者： Murillo Skrzek Anderson L. Szejka Fernando Mas Maria Jose Escalona Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Parana (PUCPR) Curitiba Brazil University of Seville (US) Seville Spain CT Engineering Group Spain

The aerospace manufacturing industry exhibits significant complexity in all its tasks. For instance, the aircraft’s main body comprises several components with multiple dimensions, geometries, and materials. This kind of manufacturing system is specialised in creating aerospace parts characterised by advanced technology, limited production quantities, and a high degree of customisation. The aerospace products and the corresponding manufacturing systems have extensive life cycles spanning decades and are repurposed to accommodate product variations. Any disturbance during the project progression has the potential to result in escalated expenses and time investments, leaving economic and environmental drivers. In this way, Cyber-Physical Production systems (CPPS) are emerging to reduce misinterpretation and mistakes across all stages of the manufacturing process. Therefore, the main aim of this paper is to discuss the current issues and emergent technologies across the literature review to address the following Research Issue 1 (RI1): What are the current issues and emergent technologies in CPPS and KDD for the Aerospace Industry? Research Issue 2 (RI2): What is the gap in CPPS and KDD for the aerospace industry? This initial literature review is concentrated on the Knowledge data-driven (KDD) to aid in developing CPPS for Aerospace Sheet Metal (ASM) parts manufacturing and examining the use of CPPS in the aerospace sector. Finally, this research contributes to the research community with an initial overview of research trends in the domain of KDD for CPPS in the aerospace industry and finds the main research gaps in this area.

关键词： Knowledge data-driven Cyber-Physical Production System Aerospace Industry Content Analysis

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Developing a sustainable operations maturity model (SOMM) 22

Developing a sustainable operations maturity model (SOMM)

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22nd International Conference on Production Research, ICPR 2013

作者： Machado, C.G. Pinheiro De Lima, E. Gouvea Da Costa, S.E. Cestari, J.M.A.P. Kluska, R.A. Hundzinski, L.N. Industrial and Systems Engineering Graduate Program Pontifical Catholic Univeristy of Paraná-PUCPR R. Imaculada Conceição 1155 Curitiba Paraná Brazil Federal University of Technology-Paraná Av. Sete de Setembro 3165 Curitiba Paraná Brazil

The concepts associated with sustainability have been gradually incorporated into business models in recent years. Particularly in the field of Operations Management (OM), the adoption of new performance indicators and the revision of operations strategy content and process frameworks are changing the way companies manage their operations regarding sustainability, creating the right conditions for new operations management model development. Such models were created in order to assess the impact and to characterize the progress of new initiatives in sustainability in the economic, environmental and social spheres (Triple Bottom Line-TBL). This paper, based on academic literature and professional models, proposes a framework for verifying the level of maturity of sustainable operations. Its distinguishing aspect contemplates the integrated approach over TBL along five proposed maturity levels, followed by descriptions of context, structure, processes, value chain and functional recommendations on implementation. The main objective of the Sustainable Operations Maturity Model (SOMM) is to be applied as a managerial tool to support organizations when evaluating the integration of sustainability into their strategies, and especially to provide guidelines for the evolution of sustainable operation processes.

关键词： Sustainable development

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Vibration-based classification of road damages: Gyroscope data and a simple neural network model 2

Vibration-based classification of road damages: Gyroscope da...

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2nd International Conference on Eco engineering Development 2018, ICEED 2018

作者： Gunawan, Fergyanto E. Herriyandi Soewito, Benfano Mauritsius, Tuga Surantha, Nico Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia Information Systems Management Department BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Jakarta11480 Indonesia

A system for automatic detection of road damages is essential for logistics management. At the core of the system lies an algorithm for classification of damages. This work intends to establish such an algorithm. In the current proposal, the algorithm receives data of the probe vehicle rate of rotations, extracts statistics descriptive from the data to produce a feature vector, and finally, feeds the vector to a simple-three-layer neural network to classify the damages. The types of damages are limited to four cases: normal, pothole, speed bump, and expansion joint. For the development, a probe vehicle is used to produce 400 empirical data involving those four damage cases. By using Monte Carlo approach, the established neural network model is evaluated. The results show that the approach is able to classify the cases with 85% accuracy. The study also finds that the rates of pitch and roll to be the determining factors. © 2018 Institute of Physics Publishing. All rights reserved.

关键词： Neural network models

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Analysis Hierarchy Process In Real Problem Resolution In A Food Company 4

Analysis Hierarchy Process In Real Problem Resolution In A F...

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4th International Conference on Chemical engineering and Advanced Materials, CEAM 2014

作者： Mazia Rosa, Carla Regina Arnssteiner, Maria Teresinha Colmenero, JoãO Carlos PUCPR - Pontifical Catholic University of Paraná PPGEPS - Graduate Program in Production Engineeringand Systems R.ImaculadaConceição 1155 Curitiba Brazil PUCPR - Pontifical Catholic University of Paraná PPGEPS - Graduate Program in Production Engineeringand Systems R.ImaculadaConceição 1155 Curitiba Brazil UTFPR - Technological University Federal do Paraná - Campus Ponta Grossa PPGEP - Graduate Program in Production Engineering Av. Monteiro Lobato PontaGrossa Brazil

ISBN: (纸本)9783038352693

One of the most important aspects to be considered by Distribution Centers (DCs) is the improvement of the process in relation to the attributes of the service that is provided. The purpose of this article is to analyze various alternatives for the definition of a DC with the use of a method of multi-criteria support in decision making, more specifically, the Analytic Hierarchy Process (AHP) for the allocation and weighting of importance levels for structuring DCs. To further illustrate the process, a field study was conducted in a multinational company in the food sector. This enabled the identification of the best of three possible structures that were evaluated based on the criteria and sub-criteria targeted in the decision context. The results show that the cost and quality factors are primarily responsible for choosing the most appropriate alternative. © (2014) Trans Tech Publications, Switzerland.

关键词： Analytic hierarchy process

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Sustainable procedures audit structural model proposition 22

Sustainable procedures audit structural model proposition

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22nd International Conference on Production Research, ICPR 2013

作者： Pereira, Everaldo Antonio R. De Lima, E. Pinheiro Gouvea Da Costa, S.E. Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana Imaculada Conceicao St. 1155 Curitiba Parana Brazil

The paper proposes a model for expanded audit of operations strategy that considers the social, economic and environmental in the process of formulation and review of evidence to validate a sustainable operation. The proposed model calls for the operations strategy of an organization should be linked to 3 vertices: sustainability of operations, strategic indicators and auditing. A management audit aims to provide a thorough analysis of the company taking into account the effects of the changes caused by uncontrollable external variables and thus the challenge of being the first international standard for managing corporate responsibility, focusing on audit and social reporting, the AA1000, presents a strategic integration of sustainability indicators that, using the principles of management audit, planning determines an audit to validate sustainable operations. Thus, the main contribution of this work to the operations strategy is to propose an extensible model that allows auditing incorporate the new demands that are imposed on managers dynamically, but that meet the high performance sustainable.

关键词： Sustainable development

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Correlation process in content analysis for a BPM modeling project 22

Correlation process in content analysis for a BPM modeling p...

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22nd International Conference on Production Research, ICPR 2013

作者： Kluska, R.A. Pinheiro De Lima, E. Gouvea Da Costa, S.E. Machado, C.G. Cestari, J.M.A.P. Hundzinski, L.N. Industrial and Systems Engineering Graduate Program Pontifical Catholic Univeristy of Paraná-PUCPR R. Imaculada Conceição 1155 Curitiba Paraná Brazil Federal University of Technology-Paraná Av. Sete de Setembro 3165 Curitiba Paraná Brazil

In the field of content analysis, there are several methods that aim to compare texts and analyze them to determine whether they are equivalent. Within the content analysis, we found techniques used to compare and correlate texts. Some of them are present in computational tools such as the Sphinx and Atlati. In General, these tools are closed solutions of difficult integration with other systems. In this context, it is not possible to set up an automated process including these tools. This difficulty became evident during operation activities of a sustainability indicators formulation model proposed by Machado et al. 2012. In this paper, the aim is to develop a content correlation methodology and make it operational in the form of an automated process of similar correlation texts and or equivalents. The research was driven by a methodological organization process. This process is the combination of methodological tools: action research, BPM Cycle-Business Process Management, Project Management and Process Approach. The correlation process developed by this research makes it possible to correlate texts that are grouped so that assignments are made that determine a degree of correlation between texts. It is known that with this model it is not possible to determine which texts are equivalent between themselves exactly, however, there is a qualifying list of correlation between the group texts.

关键词： Project management

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Economic dispatch optimization using hybrid chaotic particle swarm optimizer

Economic dispatch optimization using hybrid chaotic particle...

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IEEE International Conference on systems, Man and Cybernetics

作者： Leandro dos Santos Coelho Viviana Cocco Mariani Industrial and Systems Engineering Graduate Program-PPGEPS Automation and Systems Laboratory PUCPR/PPGEPS Pontifical Catholic University of Paraná Curitiba Parana Brazil Mechanical Engineering Graduate Program PUCPR/PPGEM Pontifical Catholic University of Paraná Curitiba Parana Brazil

Particle swarm optimization (PSO) is a population-based stochastic optimization technique, originally developed by Eberhart and Kennedy, inspired by simulation of a social psychological metaphor instead of the survival of the fittest individual. In PSO, the system (swarm) is initialized with a population of random solutions (particles) and searches for optima using cognitive and social factors by updating generations. PSO has been successfully applied to a wide range of applications, mainly in solving continuous nonlinear optimization problems. Based on the PSO and chaos theories, this paper discusses the use of a chaotic PSO approach hybridized with an implicit filtering (IF) technique to optimize performance of economic dispatch problems. The chaotic PSO with chaos sequences is the global optimizer and the IF is used to fine-tune the chaotic PSO run in sequential manner. The hybrid methodology is validated for a test system consisting of 13 thermal units whose incremental fuel cost function takes into account the valve-point loading effects.

关键词： Chaos Particle swarm optimization Fuel economy Stochastic processes Psychology Social factors Filtering theory System testing Thermal loading Cost function

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A Machine Learning-Based Digital Twin Model for Pressure Prediction in the Fuel Injection System 48

A Machine Learning-Based Digital Twin Model for Pressure Pre...

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48th Annual Conference of the IEEE industrial Electronics Society, IECON 2022

作者： Duarte, Edwin P. Viegas, Eduardo K. Santin, Altair O. Graduate Program in Computer Science PPGIa Pontificia Universidade Catolica Do Parana-Pucpr Brazil Robert Bosch GmbH Engineering Systems Curitiba Brazil Secure Systems Research Center Abu Dhabi United Arab Emirates

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

Over the last years, the engine calibration task has mostly been conducted based on the engineers' knowledge. As a result, considering the complexity of modern engines, finding the most suitable configuration for each situation has become an impractical and expensive task. Apart from causing engines to be produced with inadequate calibration configuration, it can also decrease the lifespan of their components, degrading their efficiency. This paper proposes a machine learning-based digital twin model for pressure prediction in a fuel injection system, split into two steps. First, we extract statistical engine features based on a predefined time window to represent the engine behavior over time. Second, a digital twin implemented through a machine learning model is used to predict pressure levels in the fuel injection system. As a result, the predicted values can be used to assist the engine common rail system module in avoiding undesired engine states. Experiments performed on a new dataset, built over a real diesel-based engine, consisting of 208 features and over 1.3 million instances, have shown the feasibility of our proposal. The proposed scheme can predict in an advance time of 0.1 seconds the pressure levels for a fuel injection system with only 0.057 RMSE. Moreover, it increases its error rate by only 10.6% if a 0.5-second time advance is required. © 2022 IEEE.

关键词： Machine learning

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