Fuzzy Applications in Industrial Engineering

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In the proposed fuzzy differential equation for reliability, two types of fuzzy derivative: Hukuhara derivative and generalized differentiability are used. It is proved that the Hukuhara differentiability is not adequate for fuzzy reliability analysis. Finally, using the fuzzy integration, the concept of fuzzy mean time to failure FMTTF will be introduced. Some numerical simulations are presented to show the applicability and validity of generalized differentiability, in comparison with the Hukuhara differentiability results for fuzzy reliability analysis. Khastan, Rosana Rodrguez-Lpez, Amit Kumar, Sneh Lata, E Baloui Jamkhaneh, Harish Garg, Hsien-Chung Wu, Paper Title Pages.

Authors: Wang Lan Tian. Abstract: Fuzzy neural network, which can deal with complex data and prediction process that other algorithms can not accomplish, has become a focus in recent years in many fields. Data mining can extract such information and knowledge as data classification, spatial evolution and prediction and so on, and in the huge cadastral data find the implied information which is helpful for our urban construction.

Abstract: We report in this paper the control work of lateral displacement of a web that runs on a roll-to-roll system. The control system employs a displacement guide and a vision sensor.

Designing a model of intuitionistic fuzzy VIKOR in multi-attribute group decision-making problems

The vision sensor is used to detect the lateral displacement of a moving web and send a feedback signal to a controller. A fuzzy logic is developed and embedded in the controller.


Experimental results are presented to evaluate the control logic and system. Abstract: In this paper, an intelligent speed controller for DC motor is designed by combination of the fuzzy logic and genetic algorithms.

First, the speed controller is designed according to fuzzy rules such that the DC drive is fundamentally robust. Then, to improve the DC drive performance, parameters of the fuzzy speed controller are optimized by using the genetic algorithm.

Simulation works in MATLAB environment demonstrate that the genetic optimized fuzzy speed controller became very strong, gives very good results and possesses good robustness. The main feature is shown not only in reducing the input space by special inner structure of neurons, but also auto-extracting the rules by the structure self-organization and parameter self-learning. The equivalent is proved that the network structure and fuzzy inference. The whole structure of network is optimized by genetic algorithm to extract if-then rules.

  1. Fuzzy Applications in Industrial Engineering.
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Fuzzy set theory is now applied to problems in engineering, business, medical and related health sciences, and the natural sciences. Over the years there have been successful applications and implementations of fuzzy set theory in industrial engineering.


Journal of Industrial Engineering and Management Studies

Indus-trial engineering is one of the branches that fuzzy set theory found a wide application area. Industrial engineers face many problems with incomplete and vague information in these cases since the characteristics of these cases often require this kind of information. Fuzzy Sets Theory developed by L.

Zadeh is an excellent tool to solve these problems.

  1. Table of contents.
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  4. Journal of Intelligent and Fuzzy Systems.
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  6. Fuzzy Applications in Industrial Engineering.
  7. Fuzzy set applications in industrial engineering - Semantic Scholar.

Many industrial engineering curriculums of undergraduate and graduate pro-grams include many courses teaching how to use fuzzy sets when you face incomplete and vague information. This book presents some application examples of fuzzy sets in industrial engineering. It contains 24 original research and application chapters from different perspectives, and covering different areas of Industrial Engineer-ing.

FSDM International Conference on Fuzzy System and Data Mining

The book contains papers on the major seven areas of industrial engi-neering to which fuzzy set theory can contribute. These areas are fuzzy control and reliability, fuzzy engineering economics and investment analy-ses, fuzzy group and multi-criteria decision-making, human factors engi-neering and ergonomics, manufacturing systems and technology manage-ment, optimization, and statistical decision-making.

Many books and special issues of journals have been published on the fuzzy applications in the various topics of Industrial Engineering. This book aims at summarizing these works and presenting the future directions of the use of fuzzy sets in Industrial Engineering.


My special thanks go to Prof. Zimmermann, Prof. Waldemar Karwowski, Prof.

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