2 How to Create Instances of Fuzzy Numbers 3 2.1 Arbitrary Fuzzy Numbers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.1.1 Definition by Side Functions . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.1.2 Definition by α-cut Bounds . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.1.3 Definition with Generating Functions Omitted: Shadowed Sets . . . . . . 6 2.2 Us...

This paper addresses the design of control charts for both variable ( x chart) andattribute (u and c charts) quality characteristics, when there is uncertainty about the processparameters or sample data. Derived control charts are more flexible than the strict crisp case, dueto the ability of encompassing the effects of vagueness in form of the degree of expert’spresumption. We extend the use o...

the classical control charts by using precise and specified data divide the production process into two groups: in-control or out of control, while the fuzzy sets with the definition continuous and cohesive membership functions and also using ambiguous and vague fuzzy numbers (triangular and trapezoid), it introduces different levels of decisions for the decision makers. in this paper, using th...

Nonparametric control charts are presented in order to figure out the problem of detecting changes in the process median (or mean)‎, ‎or changes in the variability process where there is limited knowledge regarding the underlying process‎. ‎When observations are reported imprecise‎, ‎then it is impossible to use classical nonparametric control charts‎. ‎This paper is devoted to the problem of c...

Many problems in scientific investigation generate nonprecise data incorporating nonstatistical uncertainty. A nonprecise observation of a quantitative variable can be described by a special type of membership function defined on the set of all real numbers called a fuzzy number or a fuzzy interval. A methodology for constructing control charts is proposed when the quality characteristics are v...

We present new tools for fuzzy arithmetic with fuzzy numbers, based on the parametric representation of fuzzy numbers and new fuzzy operations, the generalized difference and the generalized division of fuzzy numbers. The new operations are described in terms of the parametric LR and LU representations of fuzzy numbers and the corresponding algorithms are described. An application to the soluti...

Abstract: Two approaches for constructing control charts to monitor multivariate attribute processes when data set is presented in linguistic form are suggested. Two monitoring statistics 2 f T and are developed based on fuzzy and probability theories. The first is similar to the Hotelling’s statistic and is based on representative values of fuzzy sets. The distribution of statistic, being a li...

Fuzzy control charts are proposed to solve the problem that traditional cannot be applied fuzzy quality characteristics. First, characteristics converted representative statistics, which mode transformation, level midrange transformation and median transformation. Control designed based on Poisson distribution. Second, effects of different statistics analysed. Direct Charts avoid some informati...