Fuzzy process capability indices based on imprecise observations induced from non-normal distributions

Fuzzy process capability indices based on imprecise observations induced from non-normal... This study has developed an approach to statistical process control indices for imprecise observations induced from an arbitrary population. For this purpose, most popular non-normal process capability index was extended based on a proposed triangular fuzzy distance between fuzzy numbers. The specific limits and targets are also considered as fuzzy numbers in this method. The order between the proposed fuzzy process capability indices was also investigated. A degree of belonging was proposed to verify the degree of process conditions of the proposed fuzzy process capability indices. The feasibility and effectiveness of the proposed method were also examined using a simulation study. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Computational and Applied Mathematics Springer Journals

Fuzzy process capability indices based on imprecise observations induced from non-normal distributions

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Publisher
Springer Journals
Copyright
Copyright © 2018 by SBMAC - Sociedade Brasileira de Matemática Aplicada e Computacional
Subject
Mathematics; Applications of Mathematics; Computational Mathematics and Numerical Analysis; Mathematical Applications in the Physical Sciences; Mathematical Applications in Computer Science
ISSN
0101-8205
eISSN
1807-0302
D.O.I.
10.1007/s40314-018-0657-8
Publisher site
See Article on Publisher Site

Abstract

This study has developed an approach to statistical process control indices for imprecise observations induced from an arbitrary population. For this purpose, most popular non-normal process capability index was extended based on a proposed triangular fuzzy distance between fuzzy numbers. The specific limits and targets are also considered as fuzzy numbers in this method. The order between the proposed fuzzy process capability indices was also investigated. A degree of belonging was proposed to verify the degree of process conditions of the proposed fuzzy process capability indices. The feasibility and effectiveness of the proposed method were also examined using a simulation study.

Journal

Computational and Applied MathematicsSpringer Journals

Published: Jun 5, 2018

References

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