Managing Group Decision Making criteria values using Fuzzy Ontologies

Meeting: 8th International Conference on Information Technology and Quantitative Management (ITQM) - Developing Global Digital Economy after COVID-19Most of the available Multi-criteria Group Decision Making methods that deal with a high number of elements usually focus on managing scenarios that have high number of alternatives and/or experts. Nevertheless, there are also cases in which the number of criteria values is difficult for the experts to tackle. In this paper, a novel Group Decision Making method that employs Fuzzy Ontologies in order to deal with a high number of criteria values is presented. Our method allows the criteria values to be combined in order to generate a reduced set of criteria values that the experts can comfortably deal with. (C) 2021 The Authors. Published by Elsevier B.V.The authors would like to thank the Spanish State Research Agency through the project PID2019-103880RB-I00 / AEI / 10.13039/501100011033

Group Decision Making Based on a Framework of Granular Computing for Multi-Criteria and Linguistic Contexts

The usage of linguistic information involves computing with words, a methodology assuming linguistic values as computational elements, in group decision-making environments. In recent times, a new methodology founded on a framework of granular computing has been employed to manage linguistic information. An advantage of this methodology is that the distribution and the semantics of the linguistic values, in place of being initially established, are defined by the optimization of a certain criterion. In this paper, different from the existing approaches, we present a novel approach build on the basis of a granular computing framework that is able to cope with group decision-making problems defined in multi-criteria contexts, that is, those in which different criteria are considered to evaluate the possible alternatives for solving the problem. In particular, it models group decision-making problems in a more realistic way by taking into account that each criterion has an importance weight and by considering that each decision maker has a different importance weight for each criterion. This approach makes operational the linguistic values by associating them with intervals via the optimization of an optimization criterion composed of two important aspects that must be taken into account in this kind of decision problems, that is, the consensus at the level of group of decision makers and the consistency at the level of individual decision makers.This work was supported in part by the Spanish Ministry of Economy and Competitiveness under Project DPI2016-77677-P, in part by the RoboCity2030-DIH-CM Madrid Robotics Digital Innovation Hub (``Robótica aplicada a la mejora de la calidad de vida de los ciudadanos. Fase IV''; S2018/NMT-4331), funded by the ``Programas de Actividades I+D de la Comunidad de Madrid,'' and co-funded by the Structural Funds of the EU, and in part by the research grant from the Asociación Universitaria Iberoamericana de Postgrado (AUIP) and Consejería de Economía y Conocimiento de la Junta de Andalucía

Distributed Linguistic Representations in Decision Making: Taxonomy, Key Elements and Applications, and Challenges in Data Science and Explainable Artificial Intelligence

Distributed linguistic representations are powerful tools for modelling the uncertainty and complexity of preference information in linguistic decision making. To provide a comprehensive perspective on the development of distributed linguistic representations in decision making, we present the taxonomy of existing distributed linguistic representations. Then, we review the key elements and applications of distributed linguistic information processing in decision making, including the distance measurement, aggregation methods, distributed linguistic preference relations, and distributed linguistic multiple attribute decision making models. Next, we provide a discussion on ongoing challenges and future research directions from the perspective of data science and explainable artificial intelligence.National Natural Science Foundation of China (NSFC) 71971039 71421001,71910107002,71771037,71874023 71871149Sichuan University sksyl201705 2018hhs-5

Introducing disruption on stagnated Group Decision Making processes using Fuzzy Ontologies

In Group Decision Making processes, experts debate about how to rank a set of alternatives. It is usual that, at a certain point of the discussion, the debate gets stuck. In this paper, a novel Group Decision Making method for environments with a high number of alternatives is presented. Fuzzy Ontologies are used in order to represent the alternatives and their characteristics. Moreover, a novel stagnation analysis is used in order to determine if the debate gets stuck. If it does, the method modifies the alternatives set in order to introduce new options and remove the least popular ones. This way, the debate can revive since that the new alternatives provide different points of view. The presented method helps experts to conduct long and thorough debates in order for them to be able to make effective and reliable decisions.MCIN/AEI PID2019-103880RB-I00FEDER/Junta de Andalucia-Consejeria de Transformacion Economica, Industria, Conocimiento y Universidades B-TIC-590-UGR20Andalusian government P20_00673Ministry of Education and King Abdulaziz University, DSR, Jeddah, Saudi Arabia IFPHI-049-135-2020Universidad de Granada/CBU

Fuzzy sets and fuzzy logic in multi-criteria decision making. The 50th anniversary of Prof. Lotfi Zadeh’s theory: introduction

„Fuzzy sets and fuzzy logic in multi-criteria decision making. The 50th anniversary of Prof. Lotfi Zadeh’s theory: introduction" Technological and Economic Development of Economy, 21(5), pp. 677–68

A self-management mechanism for non-cooperative behaviors in large-scale group consensus reaching processes

In large-scale group decision making (GDM), non-cooperative behavior in the consensus reaching process (CRP) is not unusual. For example, some individuals might form a small alliance with the aim to refuse attempts to modify their preferences or even to move them against consensus to foster the alliance’s own interests. In this paper, we propose a novel framework based on a self-management mechanism for non-cooperative behaviors in large-scale consensus reaching processes (LCRPs). In the proposed consensus reaching framework, experts are classified into different subgroups using a clustering method, and experts provide their evaluation information, i.e., the multi-criteria mutual evaluation matrices (MCMEMs), regarding the subgroups based on subgroups’ performance (e.g., professional skills, cooperation, and fairness). The subgroups’ weights are dynamically generated from the MCMEMs, which are in turn employed to update the individual experts’ weights. This self-management mechanism in the LCRP allows penalizing the weights of the experts with non-cooperative behaviors. Detailed simulation experiments and comparison analysis are presented to verify the validity of the proposed framework for managing non-cooperative behaviors in the LCRP

On Dynamic Consensus Processes in Group Decision Making Problems

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Consensus in group decision making requires discussion and deliberation between the group members with the aim to reach a decision that reflects the opinions of every group member in order for it to be acceptable by everyone. Traditionally, the consensus reaching problem is theoretically modelled as a multi stage negotiation process, i.e. an iterative process with a number of negotiation rounds, which ends when the consensus level achieved reaches a minimum required threshold value. In real world decision situations, both the consensus process environment and specific parameters of the theoretical model can change during the negotiation period. Consequently, there is a need for developing dynamic consensus process models to represent effectively and realistically the dynamic nature of the group decision making problem. Indeed, over the past few years, static consensus models have given way to new dynamic approaches in order to manage parameter variability or to adapt to environment changes. This paper presents a systematic literature review on the recent evolution of consensus reaching models under dynamic environments and critically analyse their advantages and limitations

A METHODOLOGY FOR THE BIDDERS EVALUATION AND SELECTION IN THE PUBLIC PROCUREMENT PROCESS BASED ON HETEROGENEOUS INFORMATION AND ADAPTIVE CONSENSUS APPROACHES

The public procurement problem is a special problem of supplier selection that requires strict adherence to the principles of non-discrimination, free competition, and transparency in the contract awarding procedures. It is a very complex multi-criteria problem, which requires the engagement of several decision-makers (experts). The public procurement problem requires the usage of different types of conflicting criteria, the combination of different models (methods and techniques) of decision-making, as well as the modeling of different forms of uncertainty, inaccuracy, and subjectivity of decision-makers, which can represent a rather complex, difficult, and lengthy decision-making process. Therefore, the paper proposes a methodology for improving the tender process that focuses on heterogeneous preference structures of information (preference ordering, utility values, fuzzy (additive) preference relations, multiplicative preference relations, and linguistic preference relations) and an adaptive consensus approach for subjectively determining the weight of criteria and evaluation and selection of alternative bids. The Simple Additive Weighting (SAW) method is used for the final ranking of bidders. The proposed methodology enables obtaining a more objective and measurable value during subjective decision-making as well as minimizing the risk of unscrupulous, incompetent, and irresponsible decision-making, which is shown in the given example

A systematic review on multi-criteria group decision-making methods based on weights: analysis and classification scheme

Interest in group decision-making (GDM) has been increasing prominently over the last decade. Access to global databases, sophisticated sensors which can obtain multiple inputs or complex problems requiring opinions from several experts have driven interest in data aggregation. Consequently, the field has been widely studied from several viewpoints and multiple approaches have been proposed. Nevertheless, there is a lack of general framework. Moreover, this problem is exacerbated in the case of experts’ weighting methods, one of the most widely-used techniques to deal with multiple source aggregation. This lack of general classification scheme, or a guide to assist expert knowledge, leads to ambiguity or misreading for readers, who may be overwhelmed by the large amount of unclassified information currently available. To invert this situation, a general GDM framework is presented which divides and classifies all data aggregation techniques, focusing on and expanding the classification of experts’ weighting methods in terms of analysis type by carrying out an in-depth literature review. Results are not only classified but analysed and discussed regarding multiple characteristics, such as MCDMs in which they are applied, type of data used, ideal solutions considered or when they are applied. Furthermore, general requirements supplement this analysis such as initial influence, or component division considerations. As a result, this paper provides not only a general classification scheme and a detailed analysis of experts’ weighting methods but also a road map for researchers working on GDM topics or a guide for experts who use these methods. Furthermore, six significant contributions for future research pathways are provided in the conclusions.The first author acknowledges support from the Spanish Ministry of Universities [grant number FPU18/01471]. The second and third author wish to recognize their support from the Serra Hunter program. Finally, this work was supported by the Catalan agency AGAUR through its research group support program (2017SGR00227). This research is part of the R&D project IAQ4EDU, reference no. PID2020-117366RB-I00, funded by MCIN/AEI/10.13039/ 501100011033.Peer ReviewedPostprint (published version