Get Fuzzy Applications in Industrial Engineering PDF

By Cengiz Kahraman

ISBN-10: 3540335161

ISBN-13: 9783540335160

ISBN-10: 354033517X

ISBN-13: 9783540335177

Industrial Engineering (IE) is worried with the layout, development, and install of built-in structures of individuals, fabric, gear, and effort. commercial engineers face many issues of incomplete and obscure details in those platforms because the features of those difficulties frequently require this sort of details. Fuzzy units methods are typically fantastic while human reviews and the modeling of human wisdom are wanted. IE brings an important variety of purposes of fuzzy set concept.

After an introductory bankruptcy explaining the hot prestige of fuzzy units in IE, this quantity consists of program chapters at the significant seven components of IE to which fuzzy set thought can give a contribution. those significant software components are keep watch over and Reliability, Engineering Economics and funding research, staff and Multi-criteria Decision-making, Human components Engineering and Ergonomics, production structures and know-how administration, Optimization innovations, and Statistical Decision-making. lower than those significant parts, each bankruptcy contains didactic numerical applications.

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Extra resources for Fuzzy Applications in Industrial Engineering

Example text

Limam, M. (2002), On fuzzy and probabilistic control charts, International Journal of Production Research, 40, 2849-2863. , Chen, C-H. (1995), Economic statistical np-control chart designs based on fuzzy optimization, International Journal of Quality and Reliability Management, 12, 88-92. , Raz, T. (1988), Applying fuzzy set theory in the development of quality control charts, International Industrial Engineering Conference proceedings, Orlando, FL, 30-35. 88 M. Gülbay and C. , Raz, T. (1990), On the construction of control charts using linguistic variables, International Journal of Production Research, 28, 477-487.

13. 0 (Crisp Case, VSS approach) This approach differs from the previous studies from the point of view of inspection tightness. The quality controller is able to define the tightness of the inspection depending on the nature of the products and manufacturing processes. This is possible by selecting the value of D-cut freely. Quality controller may decide using higher values of D-cut for products that require a tighter inspection. It simplifies computations and is similar to the crisp control charts.

1995), Fuzzy Systems for Management, IOS Press, Amsterdam. Bradshaw, C. W. (1983), A fuzzy set theoretic interpretation of economic control limits, European Journal of Operational Research, 13, 403-408. A. (1996), A neural fuzzy control chart for detecting and classifying process mean shifts, International Journal of Production Research, 34, 2265-2278. S. (2000), A fuzzy rule-based algorithm to improve the performance of statistical process control in quality systems, Journal of Intelligent & Fuzzy Systems, 9, 207-223.

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Fuzzy Applications in Industrial Engineering by Cengiz Kahraman


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