scholarly journals ANÁLISE MATEMÁTICA PARA DIAGNÓSTICO DO GRAU DE IMPLEMENTAÇÃO DA CONSTRUÇÃO ENXUTA [ Mathematical analysis for the diagnosis of the Lean Construction implementation stage ]

2018 ◽  
Vol 14 (2) ◽  
Author(s):  
Tatiana Gondim do Amaral ◽  
Letícia Guimarães Oka ◽  
Carlos Augusto Bouhid de Camargo Filho
Keyword(s):  
Evaluation Method ◽  
Assessment Tool ◽  
Fuzzy Inference ◽  
Lean Construction ◽  
Inference System ◽  
Lean Implementation ◽  
Construction Companies ◽  
Initial Stage ◽  
Efficient Mechanisms ◽  
Implementation Stage

RESUMO:  A Lean Construction é uma filosofia que vem crescendo nos diversos meios de construção do Brasil e do mundo. Apesar de a maioria das empresas construtoras ainda estarem em processo inicial de implementação dessas práticas e ferramentas, é fundamental que sejam elaborados mecanismos eficazes na avaliação desse processo, como é o caso do Lean Construction Assessment Tool (LCAT). Esse método de avaliação é utilizado nesse trabalho para avaliar o grau de implementação enxuta em seis empresas construtoras em Goiânia. A partir dos dados obtidos, foi realizado o cálculo do grau de implementação Lean utilizando médias aritméticas, médias ponderadas por especialistas e médias baseadas no Sistema de Inferência Fuzzy. O trabalho tem como objetivo principal analisar os resultados advindos do diagnóstico Lean da aplicação da ferramenta, avaliando as potencialidades de cada média utilizada. Busca-se ainda avaliar o processo de gestão da produção das empresas pesquisadas, destacando falhas no sistema e como esse poderia ser melhorado. As médias obtidas apresentaram pequenas variações, como rapidez de obtenção, interferência de outras variáveis no processo e análise de especialistas, sendo possível concluir sobre as diferentes potencialidades no emprego de cada uma. A partir desse estudo foi possível estabelecer um diagnóstico a partir da interação entre meio acadêmica e ambiente profissional.ABSTRACT: The Lean Construction is a filosofy which is growing in the diverse communication’s means in Brazil and around the world. Although most of the construction companies are in the initial stage of these practices and tools implementation, the development of efficient mechanisms to evaluate this process is vital, such as the Lean Construction Assessment Tool (LCAT). This evaluation method is utilized in this research to evaluate the lean implementation stage in six construction companies in Goiânia. From the obtained data, the lean implementation stage was calculated using arithmetic averages, weighted averages and averages based in the Fuzzy Inference System. This work has as main goal to analyze the results from the Lean Diagnosis obtained using the tool and evaluate the potentialities that each average owns. It’s also a goal to evaluate the management process in the studied companies, highlighting failures in the system and how they can be improved. The averages obtained present small variations, such as speed of procurement, other variables interferences and the specialists’ analysis, being possible to conclude about different potentialities in each’s use. From this study, it was possible to establish a diagnosis based in the interaction between the academic and professional environment.

Equipment Support Training Level Evaluation Method based on ANFIS

2013 ◽  
Vol 706-708 ◽  
pp. 1950-1953
Author(s):  
Wu Kui Zhao ◽  
Cheng Zhang ◽  
Yi Bo Wang
Keyword(s):  
Evaluation Method ◽  
Fuzzy Inference ◽  
Learning Algorithm ◽  
Evaluation Model ◽  
Back Propagation ◽  
Training Data ◽  
Training Level ◽  
Inference System ◽  
Proposed Model ◽  
Training Efficiency

The evaluation of equipment support training is an effective way to improve training efficiency. The main influencing factors of equipment support training are analyzed. Adaptive neural fuzzy inference system (ANFIS) model structure is established and the hybrid-learning algorithm to solve the established model by applying back-propagation and least mean squares procedure is investigated. Then the evaluation model of equipment support training level based on ANFIS is constructed. The training level consistent with the actual training level is achieved by training the proposed model using training data samples, which verifies the correctness and effectiveness of the proposed method. Simulation comparing analysis using the proposed method and BP neutral network is conducted respectively. The superiority of the proposed method is verified by simulation results, which provides an effective method for equipment support training evaluation.

scholarly journals Smart Evaluation of Green Campus Sustainability Considering Energy Utilization

2021 ◽  
Vol 13 (14) ◽  
pp. 7653
Author(s):  
Hongmei Zhao ◽  
Yang Xu ◽  
Wei-Chiang Hong ◽  
Yi Liang ◽  
Dandan Zou
Keyword(s):  
Bayesian Inference ◽  
Fuzzy Inference System ◽  
Evaluation Method ◽  
Fuzzy Inference ◽  
Evaluation Index System ◽  
Energy Utilization ◽  
Adaptive Network ◽  
Inference System ◽  
Campus Sustainability ◽  
Green Campus

With the change in energy utilization, a fast and accurate evaluation method is of great importance to promote green campus sustainability. In order to improve the feasibility and timeliness of evaluation, an intelligent evaluation model based on dynamic Bayesian inference and adaptive network fuzzy inference system (DBN-ANFIS) is proposed. Firstly, from the perspective of sustainability and considering the changes in energy utilization, a green campus evaluation index system is constructed from four levels: campus resource utilization, campus environment creation, campus usage management, and campus eco-efficiency. On this basis, the parameters of the adaptive network fuzzy inference system (ANFIS) are optimized based on dynamic Bayesian inference (DBN), so as to apply the modified model to the green campus evaluation work of the Spark big data operation platform. Finally, the scientificity of the model proposed in this paper is verified through example analysis, which is conducive to the real-time and effective evaluation of green campus sustainability and provides scientific and rational decision support to improve its management.

scholarly journals APLICAÇÃO DA FERRAMENTA DE AVALIAÇÃO DA IMPLEMENTAÇÃO DA CONSTRUÇÃO ENXUTA EM TRÊS EMPRESAS CONSTRUTORAS GOIANAS

2020 ◽  
Vol 15 (2) ◽  
pp. 137-150
Author(s):  
Tatiana Gondim do Amaral ◽  
Lucas Barros de Paiva ◽  
Rafael Figueiredo de Paula ◽  
Rodrigo Fernandes Nobre
Keyword(s):  
Supply Chain ◽  
Construction Industry ◽  
Assessment Tool ◽  
Low Cost ◽  
Lean Construction ◽  
Production Planning And Control ◽  
Chain Management ◽  
Construction Companies ◽  
Planning And Control ◽  
And Control

RESUMO: As práticas da construção enxuta vêm sendo difundidas de forma progressiva na indústria da construção civil e, com isso, surge a indispensabilidade de mecanismos que permitam a avaliação de sua implementação. As empresas podem aplicar os princípios da Lean Constuction possibilitando os meios para uma melhoria contínua de seus resultados em termos de prazos, custos e demanda ao cliente. O objetivo do trabalho é aplicar a ferramenta de avaliação da implementação da Construção Enxuta (LCAT) em três empresas construtoras goianas. A pesquisa é classificada como quantitativa e qualitativa, aplicada e de caráter exploratório. A partir da análise dos dados referentes à média de todas as quatro categorias (Gestão da Qualidade, Gestão da Cadeia de Suprimentos, Planejamento e Controle da Produção e Gestão de Projetos), as empresas B e C apresentaram percentuais próximos entre si, correspondendo a 61% e 62%, respectivamente. Esses resultados foram superiores ao da empresa A, equivalendo a 53%. A principal contribuição da pesquisa constituiu em evidenciar as práticas para a implementação da Lean Construction nas empresas pesquisadas e despertar a estas indicações de ferramentas que podem ser facilmente implementadas a um baixo custo.   ABSTRACT: The practices of lean construction have been progressively diffused in the civil construction industry, and with this, comes up the indispensability of mechanisms that allow the evaluation of their implementation. Companies can apply the principles of Lean Constuction by enabling them to continuously improve their results in terms of deadlines, costs and customer demand. The objective of this work is to apply the Lean Construction Assessment Tool (LCAT) in three construction companies in Goiás. The research is classified as quantitative and qualitative, applied and exploratory. Based on the analysis of the data for the average of all four categories (Quality Management, Supply Chain Management, Production Planning and Control and Project Management), companies B and C had close percentages, corresponding to 61 % And 62%, respectively. These results were superior to that of company A, equivalent to 53%. The main contribution of the research was to highlight the practices for the implementation of Lean Construction in the companies surveyed and to awaken to these indications of tools that can be easily implemented at a low cost.

A Predictive Visual Analytics Evaluation Approach Based on Adaptive Neuro-Fuzzy Inference System

2018 ◽  
Vol 62 (7) ◽  
pp. 977-1000 ◽  
Author(s):  
Saber Amri ◽  
Hela Ltifi ◽  
Mounir Ben Ayed
Keyword(s):  
Data Analysis ◽  
Adaptive Learning ◽  
Fuzzy Inference System ◽  
Visual Analytics ◽  
Evaluation Method ◽  
Fuzzy Inference ◽  
Evaluation Methodology ◽  
Data Types ◽  
Inference System ◽  
Neuro Fuzzy

Abstract The evaluation of visual analytics (VA) is a challenging field enabling analysts to get insight into diverse data types and formats. It aims at understanding events described by data and supporting the knowledge discovery process by integrating different data analysis methods. Recently, the evolution of intelligent decision support systems has enabled the inductive and predictive approaches of data analysis to make important decisions faster with a higher level of confidence and lower uncertainty. This paper introduces a new and intelligent evaluation method of VA that understands the users’ work as well as the features of their environments including vagueness, uncertainty and ambiguity due to workload. To this end, we apply an adaptive neuro-fuzzy inference system (ANFIS) to get quantitative and qualitative measures and determine the lowest evaluation score with better approximation. By combining fuzzy logic, used to deal with the inaccuracies and uncertainty problems during the evaluation process, and neural network, used to solve the problem of continuous changes in assessment environments with the delivery of adaptive learning content. By using the ANFIS approach that allows accurate prediction of evaluation scores, the proposed method seems more efficient compared to the recent evaluation methodology.

Aplikasi Peramalan Kebutuhan Beban Listrik Menggunakan Metode Adaptive Neuro Fuzzy Inference System (ANFIS)

2017 ◽  
Vol 3 (1) ◽  
pp. 36-48
Author(s):  
Erwan Ahmad Ardiansyah ◽  
Rina Mardiati ◽  
Afaf Fadhil
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy

Prakiraan atau peramalan beban listrik dibutuhkan dalam menentukan jumlah listrik yang dihasilkan. Ini menentukan  agar tidak terjadi beban berlebih yang menyebabkan pemborosan atau kekurangan beban listrik yang mengakibatkan krisis listrik di konsumen. Oleh karena itu di butuhkan prakiraan atau peramalan yang tepat untuk menghasilkan energi listrik. Teknologi softcomputing dapat digunakan  sebagai metode alternatif untuk prediksi beban litrik jangka pendek salah satunya dengan metode  Adaptive Neuro Fuzzy Inference System pada penelitian tugas akhir ini. Data yang di dapat untuk mendukung penelitian ini adalah data dari APD PLN JAWA BARAT yang berisikan laporan data beban puncak bulanan penyulang area gardu induk majalaya dari januari 2011 sampai desember 2014 sebagai data acuan dan data aktual januari-desember 2015. Data kemudian dilatih menggunakan metode ANFIS pada software MATLAB versi b2010. Dari data hasil pelatihan data ANFIS kemudian dilakukan perbandingan dengan data aktual dan data metode regresi meliputi perbandingan anfis-aktual, regresi-aktual dan perbandingan anfis-regresi-aktual. Dari perbandingan disimpulkan bahwa data metode anfis lebih mendekati data aktual dengan rata-rata 1,4%, menunjukan prediksi ANFIS dapat menjadi referensi untuk peramalan beban listrik dimasa depan.

FACE RECOGNITION USING FUZZY INFERENCE SYSTEM

2009 ◽  
Vol 8 (3) ◽  
pp. 887-897
Author(s):  
Vishal Paika ◽  
Er. Pankaj Bhambri
Keyword(s):  
Neural Networks ◽  
Fuzzy Inference ◽  
Facial Features ◽  
Facial Appearance ◽  
Feature Points ◽  
Inference System ◽  
Detection Techniques ◽  
The Face ◽  
Hair Style ◽  
Facial Images

The face is the feature which distinguishes a person. Facial appearance is vital for human recognition. It has certain features like forehead, skin, eyes, ears, nose, cheeks, mouth, lip, teeth etc which helps us, humans, to recognize a particular face from millions of faces even after a large span of time and despite large changes in their appearance due to ageing, expression, viewing conditions and distractions such as disfigurement of face, scars, beard or hair style. A face is not merely a set of facial features but is rather but is rather something meaningful in its form.In this paper, depending on the various facial features, a system is designed to recognize them. To reveal the outline of the face, eyes, ears, nose, teeth etc different edge detection techniques have been used. These features are extracted in the term of distance between important feature points. The feature set obtained is then normalized and are feed to artificial neural networks so as to train them for reorganization of facial images.

Sign in / Sign up

Export Citation Format

Share Document