X charts with variable sampling intervals
Technometrics
A probabilistic and statistical view of fuzzy methods
Technometrics
Fuzzy Systems for Management
&agr;-Cut fuzzy control charts for linguistic data
International Journal of Intelligent Systems
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
A hybrid fuzzy adaptive sampling - Run rules for Shewhart control charts
Information Sciences: an International Journal
Fuzzy individual and moving range control charts with α-cuts
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Fuzzy theory and technology with applications
Construction of p-charts using degree of nonconformity
Information Sciences: an International Journal
Development of fuzzy and control charts using α-cuts
Information Sciences: an International Journal
A genetic algorithm approach to determine the sample size for attribute control charts
Information Sciences: an International Journal
Information Sciences: an International Journal
Quality-based supplier selection and evaluation using fuzzy data
Computers and Industrial Engineering
A new monitoring design for uni-variate statistical quality control charts
Information Sciences: an International Journal
Supplier selection using fuzzy quality data and their applications to touch screen
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
Fuzzy logic based assignable cause diagnosis using control chart patterns
Information Sciences: an International Journal
An application of fuzzy random variables to control charts
Fuzzy Sets and Systems
Process capability analyses based on fuzzy measurements and fuzzy control charts
Expert Systems with Applications: An International Journal
Fuzzy and R control charts: Fuzzy dominance approach
Computers and Industrial Engineering
Expert Systems with Applications: An International Journal
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The major contribution of fuzzy set theory lies in its capability of representing vague data. Fuzzy logic offers a systematic base to deal with situations, which are ambiguous or not well defined. In the literature, there exist few papers on fuzzy control charts, which use defuzziffication methods in the early steps of their algorithms. The use of defuzziffication methods in the early steps of the algorithm makes it too similar to the classical analysis. Linguistic data in those works are transformed into numeric values before control limits are calculated. Thus both control limits as well as sample values become numeric. In this paper, some contributions to fuzzy control charts based on fuzzy transformation methods are made by the use of @a-cut to provide the ability of determining the tightness of the inspection: the higher the value of @a the tighter inspection. A new alternative approach ''Direct Fuzzy Approach (DFA)'' is also developed in this paper. In contrast to the existing fuzzy control charts, the proposed approach is quite different in the sense it does not require the use of the defuzziffication. This prevents the loss of information included by the samples. It directly compares the linguistic data in fuzzy space without making any transformation. We use some numeric examples to illustrate the performance of the method and interpret its results.