1. Introduction
One of the most fundamental results in the theory of convex functions is the
-inequality, originally introduced by Charles Hermite and Jacques Hadamard; see [
1,
2]. This inequality has attracted considerable attention due to its elegant structure, clear geometrical interpretation, and wide range of applications in mathematical analysis, optimization, and related fields. Specifically, let
be an interval and let
with
. If
is a convex function, then the
-inequality states that the value of the function at the midpoint of the interval is bounded above and below by the integral mean of the function and the arithmetic mean of its endpoint values, namely,
This inequality provides a useful estimate for the integral average of a convex function and serves as a powerful analytical tool. Owing to its significance, numerous generalizations and extensions have been developed, including variants for different classes of convexity, multidimensional settings, and integral operators. These developments continue to play an important role in advancing both theoretical results and practical applications. When the function is concave, the -inequality holds with the reverse ordering of inequalities. Over the years, this classical result has motivated extensive research, and numerous authors have established various forms and extensions of -type inequalities under different assumptions. It is worth noting that the -inequality can be derived directly from Jensen’s inequality, and thus may be viewed as a refined manifestation of the concept of convexity.
In recent decades, renewed interest in the
-inequality for convex functions has led to a broad spectrum of refinements, improvements, and generalizations. These include extensions to different classes of convexity, alternative integral operators, and multidimensional settings, as documented in a growing body of literature (see, for example, [
3,
4]). Such developments highlight the continuing relevance of this inequality in modern mathematical analysis.
A significant role in these investigations is played by interval analysis, which provides a systematic framework for handling uncertainty in mathematical modeling and computer-assisted computations. Although the origins of interval-based reasoning can be traced back [
5,
6] to early mathematical works, such as those of Archimedes on the quadrature of the circle, interval analysis as a formal and structured theory did not gain substantial attention until the mid-twentieth century. Since then, it has become an important tool in both theoretical and applied mathematics, particularly in the study of inequalities, numerical analysis, and uncertainty quantification.
In recent years, interval analysis has become an important mathematical tool for dealing with uncertainties and rounding errors in numerical computations. It provides a reliable framework for representing imprecise quantities by intervals and for performing rigorous numerical calculations. Over the past decades, significant theoretical developments and practical applications of interval analysis have been reported in the literature. Fundamental concepts and computational techniques of interval arithmetic and interval computations were systematically studied in classical works on interval methods and their applications [
7,
8]. Furthermore, interval analysis has been successfully applied to global optimization problems and the reliable solution of nonlinear systems of equations [
9]. Its applications have also been extended to various engineering and scientific problems, where interval methods provide guaranteed bounds for uncertain parameters and measurements [
10]. In addition, interval arithmetic plays a crucial role in computer arithmetic and reliable numerical computation, ensuring accurate error control in scientific computing [
11]. The development of extended interval spaces has further enriched the theoretical framework of interval analysis and opened new directions for mathematical modeling and analysis [
12]. These advances have naturally led to the broader acceptance of absolute inequalities and interval-based approaches in modern mathematical analysis. As a natural generalization of
-analysis, fuzzy-number theory provides a more flexible and expressive framework for describing uncertainty by incorporating degrees of membership rather than sharp bounds. Consequently, the study of inequalities for fuzzy-number-valued and set-valued functions has attracted growing interest. In this context, a
-type inequality for set-valued functions, which generalizes
-functions, was established by Sadowska, see [
13]. Subsequently, several authors extended these ideas to fuzzy-number-valued functions, thereby enriching the theory and widening its range of applications.
Moreover, the introduction of generalized fractional integral operators has played a crucial role in the advancement of
-type inequalities within the fuzzy setting. In particular, new
-type inequalities involving fractional integrals in relation to another function were obtained by Jleli and Samet [
14], providing a unified approach that encompasses several classical fractional operators. Furthermore, fractional integrals of one function in relation to another for
-functions were first presented by Tunç [
15], laying the groundwork for subsequent extensions to fuzzy-number-valued functions. These developments strongly motivate the present study, which focuses on establishing
-type inequalities for coordinated convex fuzzy-number-valued functions involving generalized fractional integrals.
A unified framework that encompasses both the Riemann–Liouville and Hadamard fractional integrals was introduced through the novel fractional integral proposed by Katugampola. This generalization has proved to be highly effective in deriving new analytical results and has stimulated further developments in fractional inequality theory. Using generalized fractional integrals and classical integrals, Zhao et al. [
16] and Budak and Agarwal [
17] established
-type inequalities for coordinated convex functions, thereby unifying and extending several important fractional integral operators, including the Riemann–Liouville, Hadamard, and Katugampola fractional integrals. Subsequently,
-inequalities involving Riemann–Liouville fractional integrals [
18] were investigated for
-functions by Budak and coauthors [
19], further highlighting the applicability of fractional operators in interval analysis. In a related direction,
.
-left- and -right-sided generalized fractional double integrals were introduced by Kara and collaborators [
20], providing a broader setting for studying inequalities involving multivariable
-functions. The concept of
-convexity has also been extensively explored in the literature. In addition to classical
-convex functions, several generalized convexity notions—such as
-
-convex functions—have been examined to obtain sharper and more flexible inequality results. On the other hand, the theory of fuzzy sets was first introduced by Zadeh to deal with uncertainty and vagueness in mathematical models [
21]. Since then, fuzzy set theory has become an important tool in various branches of mathematics and applied sciences. In particular, the concept of metric spaces of fuzzy sets has been extensively studied, providing a fundamental framework for analyzing fuzzy-valued functions and their applications [
22]. Moreover, the study of fuzzy differential equations has attracted considerable attention due to its significant role in modeling dynamic systems under uncertainty. Several researchers have contributed to the development of this field, including the pioneering work on fuzzy differential equations and their solutions [
23,
24]. Furthermore, the concept of fuzzy quasilinear spaces and their applications has provided a useful structure for studying fuzzy-valued mappings and related mathematical problems [
25]. In addition, the theory of fuzzy integrals for set-valued and fuzzy mappings has been developed to extend classical integration concepts to fuzzy environments [
26]. These developments have laid the foundation for further research in fuzzy analysis and its applications in various mathematical models. These studies form an important foundation for extending
-type inequalities to fuzzy-number-valued functions—see [
27]—and coordinated convexity frameworks involving fuzzy fractional integrals [
28], which constitute the main focus of the present work. In recent years, several researchers have introduced and investigated different notions of
-convexity for
-functions [
29], thereby broadening the classical concept of
-convexity (see, for example, [
30,
31]). Within this generalized framework, a variety of
-type inequalities have been established for
-
-convex functions, demonstrating the flexibility and effectiveness of
-convexity in inequality analysis.
Following the extension of the
-inequality proposed by Zabandan [
32] in 2020, further developments were achieved for fractional and logarithmic integral operators. These results significantly enriched the theory by incorporating nonlocal operators and logarithmic kernels. In a related direction, important
-type inequalities for coordinated convex functions were derived by Sarıkaya and Kılıçer [
33], highlighting the role of coordinated convexity in multivariable settings. Moreover, several authors have extended
-type inequalities to the setting of Riemann–Liouville fractional integrals combined with logarithmic integral operators. These extensions have further deepened the understanding of fractional inequalities and have opened new avenues for research in
and fuzzy-number-valued analysis. Such advances provide strong motivation for the present study, which aims to develop new
-type inequalities for coordinated convex fuzzy-number-valued functions involving generalized fractional integrals. The following inequality is known as fuzzy-fractional
-type inequality, such that, as discussed in [
34],
where
is a fuzzy-number-valued convex function over
The principal aim of this paper is to establish several
-type inclusions for
-convex functions and
-coordinated convex functions by employing
-weighted functions. The results obtained in this study not only unify but also extend earlier findings reported in the literature, particularly those presented in [
27,
28].
The paper is organized as follows.
Section 2 recalls the fundamental definitions, properties, and preliminary results related to
-functions that are essential for the subsequent analysis. In
Section 3, we present a concise overview of fractional integrals for
-functions involving one and two variables.
Section 4 is devoted to the derivation of weighted
-type inclusions for
-convex functions, where several known results are recovered as special cases. Finally, in the context of coordinated convex functions, we establish new and significant
-type inclusions, thereby further enriching the existing theory.
2. Preliminary Concepts
Let
be a fuzzy set in a nonempty set
with the membership function
For any
, the
-cut (or
-level set) of
is defined as the crisp set
Additionally, the 0-cut of
is defined as
where the bar denotes the closure of the set.
The family of -cuts provides a complete characterization of the fuzzy set and plays a fundamental role in fuzzy analysis, particularly in the definition of fuzzy numbers, fuzzy arithmetic, and fuzzy-valued integrals.
Note that the following four conditions are standard and essential for characterizing a fuzzy number. Normality ensures the existence of at least one element with full membership, convexity guarantees that intermediate values are represented consistently, upper semi-continuity ensures well-behaved membership functions, and compact support guarantees boundedness. Together, these conditions ensure that a fuzzy number is mathematically well-defined and suitable for analysis and applications.
Definition 1 ([
21])
. A fuzzy number (·) is a normal, convex, upper semi-continuous fuzzy set on with compact support, whose μ-cuts are nonempty closed bounded intervals for all . The set of all ·s is denoted by .
Theorem 1 ([
22])
. A fuzzy set is if is a · if, and only if, there exist two real-valued functions.such that, for every
, the -cut of is given by Moreover, the endpoint functions satisfy the following conditions:
is bounded, non-decreasing, and left-continuous on ;
is bounded, non-increasing, and left-continuous on ;
for all .
Conversely, any pair of functions satisfying conditions (1)–(3) uniquely determines a · ““.
Let represented parametrically and , respectively. We say that if for all , , and or , where is a partial-ordered relation between the intervals. If then there exists such that or . The fuzzy numbers and are said to be comparable in relation to the partial order if either or holds. Otherwise, they are termed non-comparable. The set forms a partially ordered set under the relation . In what follows, we may equivalently write instead of whenever convenient.
Let
. If there exists a fuzzy number
such that
where
denotes fuzzy addition, then the generalized Hukuhara (
) difference between
and õ is said to exist. In that sense,
is called the
-difference of
and
, and it is denoted by
as defined in ref. [
25].
If the
-difference exists, then for each
the endpoint functions satisfy
and
Having established these fundamental properties of fuzzy numbers under addition and scalar multiplication, we now proceed to the case where
and
; then,
and
are defined as
Remark 1. It is evident that is closed under “” scalar addition, and the features of that were previously defined match those that result from the traditional extension concept. Furthermore, we obtainfor every scalar number . A full metric space is generally understood to be the space equipped with the supremum metric, which is written as ) (see, e.g., [
26]
). Definition 2 ([
26])
. Let be a ·-mapping. Then, -continuouity of at a point over fuzzy supremum metric if Equivalently,
is D-continuous at
if, and only if, for every
, the endpoint functions
are continuous at
, uniformly in relation to
.
The mapping is called -continuous on if it is -continuous at every point of
Remark 2. -continuity in relation to the fuzzy supremum metric guarantees uniform level-wise continuity of the μ-cut endpoints and is particularly suitable for studying ·-integrals, fractional operators, and -type inequalities.
Definition 3 ([
27])
. A mapping is called ·-mapping if, for every , is a ·. Equivalently, for every and the -cut of is a closed and bounded interval given bywhere Definition 4 ([
27])
. Let be a · mapping such that, for each the endpoints mappingare integrable on The
·
Aumann integral of
over
is defined as the
·
:
whose
-cut is given by
where
and
represent
·
-Aumann and interval Aumann integrals, respectively.
The ·-Aumann integral preserves the ·-structure and plays a fundamental role in fuzzy analysis, particularly in the study of f ·-inequalities, ·-fractional integrals, and · -type results.