2016
DOI: 10.1155/2016/8496812
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Linear Programming Problem with Interval Type 2 Fuzzy Coefficients and an Interpretation for Its Constraints

Abstract: Interval type 2 fuzzy numbers are a special kind of type 2 fuzzy numbers. These numbers can be described by triangular and trapezoidal shapes. In this paper, first, perfectly normal interval type 2 trapezoidal fuzzy numbers with their left-hand and righthand spreads and their core have been introduced, which are normal and convex; then a new type of fuzzy arithmetic operations for perfectly normal interval type 2 trapezoidal fuzzy numbers has been proposed based on the extension principle of normal type 1 trap… Show more

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Cited by 14 publications
(15 citation statements)
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References 27 publications
(33 reference statements)
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“…The interval trapezoidal fuzzy set is an important part of the concept in type-2 fuzzy sets, while the membership function of the perfectly normal interval type-2 fuzzy number (PnIT2TrFN) adopts the form of its interval trapezoid shape. The definition of PnIT2-TrFN is as follows (Srinivasan and Geetharamani, 2016): Let A be an interval type-2 fuzzy number (Figure 1) defined on the universe of discourse X, then its lower membership function and upper membership function can be expressed as and Geetharamani, 2016). and 2 3 ( , , , )…”
Section: Perfectly Interval Type-2 Fuzzy Numbermentioning
confidence: 99%
See 1 more Smart Citation
“…The interval trapezoidal fuzzy set is an important part of the concept in type-2 fuzzy sets, while the membership function of the perfectly normal interval type-2 fuzzy number (PnIT2TrFN) adopts the form of its interval trapezoid shape. The definition of PnIT2-TrFN is as follows (Srinivasan and Geetharamani, 2016): Let A be an interval type-2 fuzzy number (Figure 1) defined on the universe of discourse X, then its lower membership function and upper membership function can be expressed as and Geetharamani, 2016). and 2 3 ( , , , )…”
Section: Perfectly Interval Type-2 Fuzzy Numbermentioning
confidence: 99%
“…The preliminary data input fuzzy rules include Mamdani fuzzy rules and TSK fuzzy rules (Mamdani, 1974;Takagi and Sugeno, 1985); however, there are few researches on the ranking of fuzzy numbers. At present, there are direct ranking methods using centroids for interval type-2 fuzzy sets Mendel, 2009), andFigueroa et al (2018) proposed a ranking method based on Yager index and Srinivasan proposed a ranking method based on probability degree (Srinivasan and Geetharamani, 2016).…”
Section: Introductionmentioning
confidence: 99%
“…Pseudo-Interval T2 FSs play a central role in the models for their engineering applications [36]. They are a distinct type of FSs that can be described by a trapezoidal shape.…”
Section: Pseudo-interval T2 Fuzzy Sets Linear Programmentioning
confidence: 99%
“…Kundu et al [46] proposed a new parametric linear programming method for type-2 fuzzy intervals at different -cuts levels to solve fixed charge transportation problem. Srinivasan and Geetharamani [47] used -cuts levels with degree of satisfaction of the constraints where resources and technology coefficients are defined as type-2 fuzzy intervals. Yager [48] used a ranking function and -cuts to solve problems keeping them in linear form.…”
Section: Production Planning and Fuzzinessmentioning
confidence: 99%