Examinando por Autor "Rodríguez, Rosa M."
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Ítem A Cohesion-driven Consensus Reaching Process for Large Scale Group Decision Making under a Hesitant Fuzzy Linguistic Term Sets Environment(Elsevier, 2021-05) Rodríguez, Rosa M.; Labella, Álvaro; Sesma-Sara, Mikel; Bustince, Humberto; Martínez, LuisLarge-scale group decision-making (LSGDM) under uncertainty modelled by comparative linguistic expressions based on a hesitant fuzzy linguistic term set (HFLTS) has recently attracted the interest of many researchers and research, due to the necessity of its function in LSGDM, and the challenges it faces such as the managing of the scalability problem, uncertainty of experts’ opinions and dealing with polarized conflicting opinions. To smooth out such discrepancies and obtain agreed solutions Consensus Reaching Processes (CRPs) for LSGDM have been applied, in which experts are grouped into sub-groups according to the closeness of their opinions to deal with scalability. However, most CRPs for LSGDM are driven by a majority rule, in which larger sub-groups, where there might be internal disagreements, lead the consensus. In such processes, the internal disagreements can produce unsatisfactory solutions. Consequently, the majority view should be complemented by additional mechanisms that also measure the strength of the sub-groups’ opinions. A good measurement of such strength is the cohesion among the sub-group members. Therefore, in this paper, a new cohesion measure for HFLTS based on restricted equivalence functions for measuring the experts’ sub-group cohesiveness is introduced to drive the consensus process together the majority and thus reduce the impact of internal disagreements risen in majority driven CRPs. It is then integrated in a new cohesion-driven CRP approach based on LSGDM to deal with comparative linguistic expressions based on HFLTS. An experimental analysis on different large scale scenarios will show the performance and importance of cohesion in consensus based LSGDM.Ítem A comprehensive minimum cost consensus model for large scale group decision making for circular economy measurement(ELSEVIER, 2022-02) Rodríguez, Rosa M.; Labella, Álvaro; Núñez-Cacho, Pedro; Molina-Moreno, Vicente; Martínez, LuisSince the first report on the Circular Economy (CE) appeared in 2013, there has been an explosion of interest in the subject by society and the business world. Thus, a base of academic literature has been developed, seeking the establishment of principles that serve as a theoretical foundation for the concept of CE. Governments demand to know how organizations are evolving in the transition towards the new production model. However, despite the efforts of researchers and companies to develop effective measurement systems, it is not easy to decide which aspects to measure, nor to determine the degree of intensity in which an organization implements the CE model. The measurement proposals combine different methodologies that are costly and time consuming procedures. We propose a comprehensive minimum cost consensus model for large scale group decision making, in which the initial experts’ preferences are automatically adjusted to obtain the measurement and cost of indicators, so that they might agree on the measurements implemented. The main aim of this research is not only to provide a quick, useful and correct method for measuring the CE, but also to show its correctness, advantages and usefulness by comparing its performance with a real case.Ítem A cost consensus metric for Consensus Reaching Processes based on a comprehensive minimum cost model(Elsevier, 2020-03) Labella, Álvaro; Liu, Hongbin; Rodríguez, Rosa M.; Martínez, LuisConsensus Reaching Processes (CRPs) have recently acquired much more importance within Group Decision Making real-world problems because of the demand of either agreed or consensual solutions in such decision problems. Hence, many CRP models have been proposed in the specialized literature, but so far there is not any clear objective to evaluate their performance in order to choose the best CRP model. Therefore, this research aims at developing an objective metric based on the cost of modifying experts’ opinions to evaluate CRPs in GDM problems. First, a new and comprehensive minimum cost consensus model that considers distance to global opinion and consensus degree is presented. This model obtains an optimal agreed solution with minimum cost but this solution is not dependent on experts’ opinion evolution. Therefore, this optimal solution will be used to evaluate CRPs in which experts’ opinion evolution is considered to achieve an agreed solution for the GDM. Eventually, a comparative performance analysis of different CRPs on a GDM problem will be provided to show the utility and validity of this cost metric.Ítem A Group Decision Making Model Dealing with Comparative Linguistic Expressions based on Hesitant Fuzzy Linguistic Term Sets(Elsevier, 2013-08) Rodríguez, Rosa M.; Martínez, Luis; Herrera, FranciscoThe complexity and impact of many real world decision making problems lead to the necessity of considering multiple points of view, building group decision making problems in which a group of experts provide their preferences to achieve a solution. In such complex problems uncertainty is often present and although the use of linguistic information has provided successful results in managing it, these are sometimes limited because the linguistic models use single-valued and predefined terms that restrict the richness of freely eliciting the preferences of the experts. Usually, experts may doubt between different linguistic terms and require richer expressions to express their knowledge more accurately. However, linguistic group decision making approaches do not provide any model to make more flexible the elicitation of linguistic preferences in such hesitant situations. In this paper is proposed a new linguistic group decision model that facilitates the elicitation of flexible and rich linguistic expressions, in particular through the use of comparative linguistic expressions, close to human beings’ cognitive models for expressing linguistic preferences based on hesitant fuzzy linguistic term sets and context-free grammars. This model defines the group decision process and the necessary operators and tools to manage such linguistic expressions.Ítem A large scale consensus reaching process managing group hesitation(Elsevier, 2018-11) Rodríguez, Rosa M.; Labella, Álvaro; De Tré, Guy; Martínez, LuisNowadays due to the social networks and the technological development, large-scale group decision making (LSGDM) problems are fairly common and decisions that may affect to lots of people or even the society are better accepted and more appreciated if they agreed. For this reason, consensus reaching processes (CRPs) have attracted researchers attention. Although, CRPs have been usually applied to GDM problems with a few experts, they are even more important for LS-GDM, because differences among a big number of experts are higher and achieving agreed solutions is much more complex. Therefore, it is necessary to face some challenges in LS-GDM. This paper presents a new adaptive CRP model to deal with LS-GDM which includes: (i) a clustering process to weight experts’ sub-groups taking into account their size and cohesion, (ii) it uses hesitant fuzzy sets to fuse expert’s sub-group preferences to keep as much information as possible and (iii) it defines an adaptive feedback process that generates advice depending on the consensus level achieved to reduce the time and supervision costs of the CRP. Additionally, the proposed model is implemented and integrated in an intelligent CRP support system, so-called AFRYCA 2.0 to carry out this new CRP on a case study and compare it with existing models.Ítem A Linguistic Metric for Consensus Reaching Processes Based on ELICIT Comprehensive Minimum Cost Consensus Models(IEEE, 2023-05) García-Zamora, Diego; Labella, Álvaro; Rodríguez, Rosa M.; Martínez, LuisLinguistic 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.Ítem A Linguistic Metric for Consensus Reaching Processes Based on ELICIT Comprehensive Minimum Cost Consensus Models(IEEE, 2023) García-Zamora, Diego; Labella, Álvaro; Rodríguez, Rosa M.; Martínez, LuisLinguistic 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.Ítem An Analysis of Symbolic Linguistic Computing Models in Decision Making(Taylor and Francis, 2012-07) Rodríguez, Rosa M.; Martínez, LuisIt is common that experts involved in complex real-world decision problems use natural language for expressing their knowledge in uncertain frameworks. The language is inherent vague, hence probabilistic decision models are not very suitable in such cases. Therefore, other tools such as fuzzy logic and fuzzy linguistic approaches have been successfully used to model and manage such vagueness. The use of linguistic information implies to operate with such a type of information, i.e. processes of computing with words (CWW). Different schemes have been proposed to deal with those processes, and diverse symbolic linguistic computing models have been introduced to accomplish the linguistic computations. In this paper, we overview the relationship between decision making and CWW, and focus on symbolic linguistic computing models that have been widely used in linguistic decision making to analyse if all of them can be considered inside of the CWW paradigm.Ítem Analyzing the performance of classical consensus models in large scale group decision making: A comparative study(ELSEVIER, 2018-06) Labella, Álvaro; Liu, Yaya; Rodríguez, Rosa M.; Martínez, LuisConsensus reaching processes (CRPs) in group decision making (GDM) attempt to reach a mutual agreement among a group of decision makers before making a common decision. Different consensus models have been proposed by different authors in the literature to facilitate CRPs. Classical CRP models focus on achieving an agreement on GDM problems in which few decision makers participate. However, nowadays, societal and technological trends that demand the management of larger scale of decision makers add new requirements to the solution of consensus-based GDM problems. This paper presents a comparative study of different classical CRPs applied to large-scale GDM in order to analyze their performance and find out which are the main challenges that these processes face in large-scale GDM. Such analyses will be developed in a java-based framework (AFRYCA 2.0) simulating different scenarios in large scale GDM.Ítem Comprehensive minimum cost models for large scale group decision making with consistent fuzzy preference relations(Elsevier, 2021-03) Rodríguez, Rosa M.; Labella, Álvaro; Dutta, Bapi; Martínez, LuisNowadays, society demands group decision making (GDM) problems that require the participation of a large number of experts, so-called large scale group decision making (LS-GDM) problems. Logically, the more experts are involved in the decision making process, the more common is the emergence of disagreements in the group. For this reason, consensus reaching processes (CRPs) are key in the resolution of these problems in order to smooth such disagreements in the group and reach consensual solutions. A CRP requires that experts are receptive to change their initial preferences, but demanding excessive changes could lead to deadlocks. The well-known minimum cost consensus (MCC) model allows to obtain an agreed solution by preserving experts’ preferences as much as possible. However, this MCC model only considers the distance among experts and collective opinion, which is not enough to guarantee a desired degree of consensus. To overcome this limitation, it was proposed comprehensive MCC models (CMCC) in which both consensus degree and distance are considered, and CMCC models deal with fuzzy preference relations (FPRs) for modeling experts’ opinions. However, these models are not efficient to deal with LS-GDM problems and the FPRs consistency is ignored in them. Therefore, this paper aims to propose new CMCC models focused on LS-GDM problems in which experts use FPRs whose consistency is taken into account in order to obtain reliable results. A case study is introduced to show the effectiveness of the proposed models.Ítem Deriving the priority weights from incomplete hesitant fuzzy preference relations in group decision making(Elsevier, 2016-05) Xu, Yejun; Chen, Lei; Rodríguez, Rosa M.; Herrera, Francisco; Wang, HuiminThe concept of hesitant fuzzy preference relation (HFPR) has been recently introduced to allow the de- cision makers (DMs) to provide several possible preference values over two alternatives. This paper in- troduces a new type of fuzzy preference structure, called incomplete HFPRs, to describe hesitant and incomplete evaluation information in the group decision making (GDM) process. Furthermore, we define the concept of multiplicative consistency incomplete HFPR and additive consistency incomplete HFPR, and then propose two goal programming models to derive the priority weights from an incomplete HFPR based on multiplicative consistency and additive consistency respectively. These two goal programming models are also extended to obtain the collective priority vector of several incomplete HFPRs. Finally, a numerical example and a practical application in strategy initiatives are provided to illustrate the validity and applicability of the proposed models.Ítem Hesitant Fuzzy Sets: State of the Art and Future Directions(Wiley, 2014-04) Rodríguez, Rosa M.; Martínez, Luis; Torra, Vicenç; Xu, Zeshui; Herrera, FranciscoThe necessity of dealing with uncertainty in real world problems has been a long-term research challenge that has originated different methodologies and theories. Fuzzy sets along with their extensions, such as type-2 fuzzy sets, interval-valued fuzzy sets, and Atanassov’s intuitionistic fuzzy sets, have provided a wide range of tools that are able to deal with uncertainty in different types of problems. Recently, a new extension of fuzzy sets so-called hesitant fuzzy sets has been introduced to deal with hesitant situations, which were not well managed by the previous tools. Hesitant fuzzy sets have attracted very quickly the attention of many researchers that have proposed diverse extensions, several types of operators to compute with such types of information, and eventually some applications have been developed. Because of such a growth, this paper presents an overview on hesitant fuzzy sets with the aim of providing a clear perspective on the different concepts, tools and trends related to this extension of fuzzy sets.Ítem Optimization algorithm for learning consistent belief rule-base from examples(Springer Link, 2011-10) Liu, Jun; Martínez, Luis; Ruan, Da; Rodríguez, Rosa M.; Calzada, AlbertoA belief rule-based inference approach and its corresponding optimization algorithm deal with a rule-base with a belief structure called a belief rule base (BRB) that forms a basis in the inference mechanism. In this paper, a new learning method is proposed based on the given sample data for optimally generating a consistent BRB. The focus is given on the consistency of BRB knowing that the consistency conditions are often violated if the system is generated from real world data. The measurement of BRB inconsistency is incorporated in the objective function of the optimization algorithm. This process is formulated as a non-linear constraint optimization problem and solved using the optimization tool provided in MATLAB. A numerical example is demonstrated the effectiveness of the proposed algorithm.Ítem Using Linguistic Incomplete Preference Relations to Cold start Recommendations(Emerald, 2010-06) Rodríguez, Rosa M.; Espinilla, Macarena; Sánchez, Pedro J.; Martínez, LuisPurpose – Analyzing current recommender systems, it is observed that the cold start problem is still too far away to be satisfactorily solved. This paper aims to present a hybrid recommender system which uses a knowledge-based recommendation model to provide good cold start recommendations. Design/methodology/approach – Hybridizing a collaborative system and a knowledge-based system, which uses incomplete preference relations means that the cold start problem is solved. The management of customers’ preferences, necessities and perceptions implies uncertainty. To manage such an uncertainty, this information has been modeled by means of the fuzzy linguistic approach. Findings – The use of linguistic information provides flexibility, usability and facilitates the management of uncertainty in the computation of recommendations, and the use of incomplete preference relations in knowledge-based recommender systems improves the performance in those situations when collaborative models do not work properly. Research limitations/implications – Collaborative recommender systems have been successfully applied in many situations, but when the information is scarce such systems do not provide good recommendations. Practical implications – A linguistic hybrid recommendation model to solve the cold start problem and provide good recommendations in any situation is presented and then applied to a recommender system for restaurants. Originality/value – Current recommender systems have limitations in providing successful recommendations mainly related to information scarcity, such as the cold start. The use of incomplete preference relations can improve these limitations, providing successful results in such situations.