Criteria for evaluating fuzzy ranking methods
Fuzzy Sets and Systems
A probabilistic and statistical view of fuzzy methods
Technometrics
&agr;-Cut fuzzy control charts for linguistic data
International Journal of Intelligent Systems
An alternative approach to fuzzy control charts: Direct fuzzy approach
Information Sciences: an International Journal
Testing statistical hypotheses with vague data
Fuzzy Sets and Systems
Fuzzy process control: construction of control charts with fuzzy numbers
Fuzzy Sets and Systems
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The statistical process control (SPC), an internationally recognized technique for improving products quality and productivity, has been widely employed throughout various industries. The SPC relies on the use of control charts to monitor a manufacturing process for identifying special causes in the process variation and signaling the necessity of a certain corrective action for the process. Since fuzzy data ubiquitously exist in the modern manufacturing process, for monitoring its sample average and variance, we propose the fuzzy X@? and R control charts, whose fuzzy control limits are obtained on the basis of the results of the resolution identity, a well-known theory in the fuzzy set field. By using the fuzzy dominance approach, which compares the fuzzy averages and variances to their respective fuzzy control limits, we are capable of determining whether the manufacturing process is needed to be adjusted or not. Finally, a practical manufacturing process illustrates the proposed methodologies to show the potential application in monitoring its average and variability while its fuzzy sample data are taken into consideration.