Abstract A fuzzy EOQ model with backorders is considered in which the costs like setup, holding and penalty price are assumed as triangular FN (fuzzy numbers). Graded mean integration approach more simple accurate employed to defuzzify cost functions. The illustrated numerical examples compute optimal values.

in this paper, we deal with fuzzy random variables for inputs andoutputs in data envelopment analysis (dea). these variables are considered as fuzzyrandom flat lr numbers with known distribution. the problem is to find a method forconverting the imprecise chance-constrained dea model into a crisp one. this can bedone by first, defuzzification of imprecise probability by constructing a suitablem...

One of the fundamental problems in wireless sensor networks (WSNs) is localization that forms the basis for many location aware applications. Localization in WSNs is to determine the physical position of sensor node based on the known positions of several nodes. In this paper, a range free, enhanced weighted centroid localization method using edge weights of adjacent nodes is proposed. In the p...

Hybrid algorithm is the hot issue in Computational Intelligence (CI) study. From in-depth discussion on Simulation Mechanism Based (SMB) classification method and composite patterns, this paper presents the Mamdani model based Adaptive Neural Fuzzy Inference System (M-ANFIS) and weight updating formula in consideration with qualitative representation of inference consequent parts in fuzzy neura...

Although simple additive weighting method (SAW) is the most popular approach for classical multiple attribute decision making (MADM), it is not practical any more if information is fuzzy. The existing methods of Fuzzy Simple Additive Weighting method (FSAW) apply defuzzification which distorts fuzzy numbers. Furthermore, most of the methods usually require lengthy and laborious manipulations. I...

Defuzzification is one of the fundamental steps in the development of fuzzy knowledge based systems. Given a fuzzy set μ over the reference set X, defuzzification applied to μ returns an element of X. While a large number of methods exists for the case of X being a numerical scale, only few methods are applicable when X corresponds to a categorical scale. Aggregation procedures have been extens...

recently, gasimov and yenilmez proposed an approach for solving two kinds of fuzzy linear programming (flp) problems. through the approach, each flp problem is first defuzzified into an equivalent crisp problem which is non-linear and even non-convex. then, the crisp problem is solved by the use of the modified subgradient method. in this paper we will have another look at the earlier defuzzifi...

AbstractIn general, expert rules expressed by imprecise (fuzzy) words of natural language like “small” lead to control recommendations. If we want design an automatic controller, need, based on these fuzzy recommendations, generate a single value. A procedure for such generation is known as defuzzification. The most widely used defuzzification centroid defuzzification, in which, the desired val...

In this paper, we concentrate on linear programming problems in which both the right-hand side and the technological coefficients are fuzzy numbers.We consider here only the case of fuzzy numbers with linear membership function. The determination of a crisp maximizing decision [2] is used for a defuzzification of these problems. The crisp problems obtained after the defuzzification are non-line...