This paper presents a hybrid socio-technical methodology for constructing fuzzy numbers from numerical data while incorporating expert knowledge through an interactive Deck of Cards (DoC) process. The approach extends the existing DoC membership function construction framework by introducing a data-driven pipeline based on a convex version of Fuzzy C -Means, in which each computational step produces intermediate outputs that are translated into card-based structures for expert validation and tuning. The proposed method ensures interpretability, adaptability, and consistency between empirical evidence and expert semantics.

d -Choquet integrals were introduced as a class of functions that generalize the classical Choquet integral by replacing the difference with a dissimilarity. This paper faces the problem of computing d-Choquet integrals when the information is imprecise/vague and, more specifically, when it is modeled as fuzzy numbers. We also study the main properties of such an extension and analyze how the features of the considered dissimilarity and fuzzy measure impact the extended d -Choquet integral.

This paper introduces a unified framework of type-(k1, k2) fuzzy subsethood measures (FSMs), where k1, k2 ∈ {0, 1, 2} with k1 ≥ k2. The proposed formulation generalizes classical subsethood measures by defining inclusion mappings between fuzzy sets of different types while preserving fundamental properties such as monotonicity, boundary conditions, and normality with respect to strong negations. We provide construction methods showing that well-known measures arise as special cases. Illustrative examples and an application to digital image processing demonstrate the applicability of the proposed FSMs in uncertain environments.

Ordered Weighted Averaging (OWA) operators are some of the most widely used aggregation functions in classic literature, but their application to fuzzy numbers has been limited due to the complexity of defining a total order in fuzzy contexts. However, the recent notion of admissible order for fuzzy numbers provides an effective method to totally order them by refining a given partial order. Therefore, this paper is devoted to defining OWA operators for fuzzy numbers with respect to admissible orders and investigating their properties. Firstly, we define the OWA operators associated with such admissible orders and then we show their main properties. Afterward, an example is presented to illustrate the applicability of these AOWA operators in linguistic decision-making. In this regard, we also develop an admissible order for trapezoidal fuzzy numbers that can be efficiently applied in practice.

Linguistic group decision making (LiGDM) aims at solving decision situations involving human decision makers (DMs) whose opinions are modeled by using linguistic information. To achieve agreed solutions that increase DMs' satisfaction toward the collective solution, linguistic consensus reaching processes (LiCRPs) have been developed. These LiCRPs aim at suggesting DMs to change their original opinions to increase the group consensus degree, computed by a certain consensus measure. In recent years, these LiCRPs have been a prolific research line, and consequently, numerous proposals have been introduced in the specialized literature. However, we have pointed out the nonexistence of objective metrics to compare these models and decide which one presents the best performance for each LiGDM problem. Therefore, this article aims at introducing a metric to evaluate the performance of LiCRPs that takes into account the resulting consensus degree and the cost of modifying DMs' initial opinions. Such a metric is based on a linguistic comprehensive minimum cost consensus (CMCC) model based on Extended Comparative Linguistic Expressions with Symbolic Translation information that models DMs' hesitancy and provides accurate Computing with Words processes. In addition, the linguistic CMCC optimization model is linearized to speed up the computational model and improve its accuracy.

As a key technology to perform sustainable development in transportation, electric vehicles have been largely welcomed due to their advantages in energy savings and low carbon emission. The main step in promoting these vehicles is the selection of the appropriate electric vehicle charging station (EVCS) site. EVCS site selection is a laborious task because it involves a series of conflicting quantitative and qualitative criteria from several dimensions. The quantitative criteria are usually expressed by numerical data, while qualitative criteria are commonly represented by linguistic terms. Furthermore, the linguistic terms generated by different decision makers are usually defined on multigranular linguistic term sets. In this article, we present a new decision-making framework to select sustainable EVCS sites within the context of heterogeneous information and multigranular linguistic terms. First, three information transformation mechanisms are defined to unify the heterogeneous information and multigranular linguistic terms into interval-valued belief structures. Afterward, shadowed sets theory is utilized to reflect the personalized individual semantics of linguistic terms. Then, with the aid of the evidential reasoning (ER) algorithm, a new information fusion method is proposed to generate the interval-valued expected utilities of alternatives. Subsequently, an improved minimax regret approach is developed to compare and rank the interval-valued expected utilities. The proposed decision-making framework is then implemented to solve a case study for EVCS site selection. Further analysis and comparisons with other methods are also conducted to show the applicability and feasibility of the current proposal.

Minimum cost consensus (MCC) models aim at obtaining a consensual solution that minimizes the cost of changing experts' preferences in the resolution of a Group Decision-Making (GDM) problem by assuring that the distance between each expert’s individual opinion and the collective one is lower than a certain threshold. In order to improve these models, Comprehensive MCC (CMCC) models were developed to include a consensus measure in the optimization model. These proposals were initially defined to deal with numerical assessments and the use of linguistic information was neglected because discrete changes across the linguistic scale imply either lost of information, deadlocks, or lack of interpretable results. In order to overcome these limitations, the Extended Comparative Linguistic Expressions with Symbolic Translation (ELICIT) framework was proposed to provide a flexible continuous linguistic representation and precise linguistic computations without any loss of under-standability or information. Therefore, this contribution aims at using the properties of the ELICIT information to define CMCC models for linguistic information which inherit the advantages of numeric CMCC in a Computing with Words environment.

One of the most widely adopted approaches to define weights for Ordered Weighting Averaging (OWA) operators consists of using biparametric linear increasing fuzzy linguistic quantifiers. However, several shortcomings appear when using these quantifiers because depending on the values of these parameters, the aggregations could be biased or the extreme values might be completely ignored. In this contribution, the use of Extreme Values Reductions (EVRs) as fuzzy linguistic quantifiers is proposed to define weights for OWA operators in order to provide more realistic aggregations. First, the impact of the parameters of these linear fuzzy linguistic quantifiers in the OWA aggregations is studied. After that, EVR-OWA operators are introduced as those OWA operators whose weights are computed by using an EVR as fuzzy linguistic quantifier. It will be shown that when using EVR-OWA operators to fuse information, the aggregations are non-biased, take into account more information and the intermediate values are prioritized before the extreme ones. After proposing several families of EVRs, the generalising potential of the EVR-OWA operators is shown by proving that every family of symmetric weights for OWA operators that prioritize the intermediate information are the weights obtained from a certain EVR. Finally, an illustrative example is provided.

Consensus Reaching Processes (CRPs) deal with those group decision-making situations in which conflicts among experts' opinions make difficult the reaching of an agreed solution.This situation, worsens in largescale group decision situations, in which opinions tend to be more polarized, because in problems with extreme opinions it is harder to reach an agreement.Several studies have shown that experts' preferences may not always follow a linear scale, as it has commonly been assumed in previous CRP.Therefore, the main aim of this paper is to study the effect of modeling this nonlinear behavior of experts' preferences (expressed by fuzzy preference relations) in CRPs.To do that, the experts' preferences will be remapped by using nonlinear deformations which amplify or reduce the distance between the extreme values.We introduce such automorphisms to remap the preferences as Extreme Values Amplifications (EVAs) and Extreme Values Reductions (EVRs), study their main properties and propose several families of these EVA and EVR functions.An analysis about the behavior of EVAs and EVRs when are implemented in a generic consensus