DI-Artículos
URI permanente para esta colecciónhttps://hdl.handle.net/10953/218
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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 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 An optimal Best-Worst prioritization method under a 2-tuple linguistic environment in decision making(ELSEVIER, 2021-05) Labella, Álvaro; Dutta, Bapi; Martínez, LuisMulti-criteria group decision making (MCGDM) deals with decision makers who evaluate alternatives over several criteria. MCGDM problems evolve in tandem with the progress of our society. Such progress has given rise to the large-scale group decision making (LS-GDM) problems in which hundreds of decision makers may participate in the decision process and new challenges to face such as groups’ formation and polarization opinions. Most real world MCGDM problems present changing contexts with uncertainty that cannot be modeled by numerical values. Under these circumstances, the use of linguistic variables and computing with words (CW) processes have provided successfully results. Concretely, the 2-tuple linguistic computational model stands out because its precise linguistic computations and high interpretability. On the other hand, pairwise comparison is a widely used elicitation technique in MCGDM, but a large number of comparisons might lead inconsistent decision makers’ preferences. The Best-Worst method (BWM) reduces the number of pairwise comparisons and the inconsistency in decision makers’ opinions. Several BWM approaches have been proposed to manage linguistic information but none of them take advantage of the 2-tuple linguistic computational process based on the CW approach, which would allow to obtain precise and understandable results. This paper aims to present an extended 2-tuple BWM to reduce the number of pairwise comparisons in MCGDM problems and model the uncertainty associated with them to accomplish accuracy computations and obtaining interpretable results. Moreover, we apply our proposal to LS-GDM scenarios in which polarization opinions and sub-groups identification, ignored from any of BWM proposals, are considered. Finally, the new model is applied to several illustrative MCGDM problems.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 Consensual Group-AHPSort: Applying consensus to GAHPSort in sustainable development and industrial engineering(ELSEVIER, 2021-02) Labella, Álvaro; Ishizaka, Alessio; Martínez, LuisMulti-Criteria Group Decision Making (MCGDM) deals with the process of taking decisions among a number of decision makers who evaluate different alternatives on several criteria that may be conflicting. In such a type of problems conflicts may are not only related to the criteria but also with the decision makers that could result in unsatisfactory solutions because of disagreements or deadlocks to achieve a decision. To avoid these undesirable results, consensus processes have been applied to group decision. The application of MCGDM has been mainly focused on decision problems for ranking and choice alternatives but, similarly to multi-criteria decision making, other types of problems such as sorting and description can be formulated. Although, a few number of MCGDM-sorting models, comparing with ranking and choice ones, have been proposed their importance and impact is quite significant nowadays. Hence, our aim is to consider the recent proposal on Group-AHPSort that did not consider any disagreement measure across the sorting decision process and study how the use of consensus processes can avoid some misclassifications or disagreements among the group of decision makers taking part in the MCGDM problem. To illustrate our approach, a case study focusing on the sorting of five European Union countries according to their sustainable development performance regarding social issues will show the importance and utility of detecting and smoothing disagreements in Group-AHPSort.