Different optimum notions for fuzzy functions and optimality conditions associated

Fuzzy numbers have been applied on decision and optimization problems in uncertain or imprecise environments. In these problems, the necessity to define optimal notions for decision-maker’s preferences as well as to prove necessary and sufficient optimality conditions for these optima are essential steps in the resolution process of the problem. The theoretical developments are illustrated and motivated with several numerical examples.     

Optimality conditions of type KKT for optimization problem with interval-valued objective function via generalized derivative

This paper addresses the optimization problems with interval-valued objective function. For this we consider two types of order relation on the interval space. For each order relation, we obtain KKT conditions using of the concept of generalized Hukuhara derivative ( $gH$ -derivative) for interval-valued functions. The $gH$ -derivative is a concept more general of derivative for this class of functions than other concepts of derivative. We make some comparison with previous result given by other authors and we show some advantages of our result.     

The Karush–Kuhn–Tucker optimality conditions for fuzzy optimization problems

This paper considers optimization problems with fuzzy-valued objective functions. For this class of fuzzy optimization problems we obtain Karush–Kuhn–Tucker type optimality conditions considering the concept of generalized Hukuhara differentiable and pseudo-invex fuzzy-valued functions.     

On turbulent, erratic and other dynamical properties of Zadeh’s extensions

Let (X, d) be a compact metric space and f : X → X a continuous function. Consider the hyperspace (K(X),H) of all nonempty compact subsets of X endowed with the Hausdorff metric induced by d, and let (F(X),d_∞) be the metric space of all nonempty compact fuzzy set on X equipped with the supremum metric d_∞ which is calculated as the supremum of the Hausdorff distances of the corresponding level sets. If is the natural extension of f to (K(X),H) and is the Zadeh’s extension of f to (F(X),d_∞), then the aim of this paper is to study the dynamics of and when f is turbulent (erratic, respectively).     

On some characterizations of preinvex fuzzy mappings

Jeyakumar (Methods Oper. Res. 55:109–125, 1985) and Weir and Mond (J. Math. Anal. Appl. 136:29–38, 1988) introduced the concept of preinvex function. The preinvex functions have some interesting properties. For example, every local minimum of a preinvex function is a global minimum and nonnegative linear combinations of preinvex functions are preinvex. Invex functions were introduced by Hanson (J. Math. Anal. Appl. 80:545–550, 1981) as a generalization of differentiable convex functions. These functions are more general than the convex and pseudo convex ones. The type of invex function is equivalent to the type of function whose stationary points are global minima. Under some conditions, an invex function is also a preinvex function. Syau (Fuzzy Sets Syst. 115:455–461, 2000) introduced the concepts of pseudoconvexity, invexity, and pseudoinvexity for fuzzy mappings of one variable by using the notion of differentiability and the results proposed by Goestschel and Voxman (Fuzzy Sets Syst. 18:31–43, 1986). Wu and Xu (Fuzzy Sets Syst 159:2090–2103, 2008) introduced the concepts of fuzzy pseudoconvex, fuzzy invex, fuzzy pseudoinvex, and fuzzy preinvex mapping from \(\mathbb{R}^{n}\) to the set of fuzzy numbers based on the concept of differentiability of fuzzy mapping defined by Wang and Wu (Fuzzy Sets Syst. 138:559–591, 2003). In this paper, we present some characterizations of preinvex fuzzy mappings. The necessary and sufficient conditions for differentiable and twice differentiable preinvex fuzzy mapping are provided. These characterizations correct and improve previous results given by other authors. This fact is shown with examples. Moreover, we introduce additional conditions under which these results are valid.     

A new type of approximation for fuzzy intervals

This paper suggests a new method to approximate a fuzzy interval u by a sequence of differentiable fuzzy intervals. This new approximation method involves the construction of differentiable fuzzy intervals using the sup-min convolution of fuzzy sets. Numerical examples and an algorithm for computational implementation of the method proposed are also given.