383 research outputs found
Using clustering methods to deal with high number of alternatives on Group Decision Making
Novel Group Decision Making methods and Web 2.0 have augmented the quantity of data that experts have to discuss about. Nevertheless, experts are only capable of dealing with a reduced set of information. In this paper, a novel method for dealing with decision environments that include a large set of alternatives is presented. By the use of clustering methods, the available alternatives are combined into clusters according to their similarity. Afterwards, one Group Decision Making process is employed for choosing a cluster and another one for selecting the final alternative.The authors would like to thank the FEDER financial support for the Project TIN2016-75850-P by the Spanish
Ministry of Science, Innovation and Universities
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
A Group Decision Making Approach Considering Self-Confidence Behaviors and Its Application in Environmental Pollution Emergency Management
Self-confidence as one of the human psychological behaviors has important influence on
emergency management decision making, which has been ignored in existing methods. To fill this
gap, we dedicate to design a group decision making approach considering self-confidence behaviors
and apply it to the environmental pollution emergency management. In the proposed method, the
self-confident fuzzy preference relations are utilized to express expertsâ evaluations. This new type of
preference relations allow experts to express multiple self-confidence levels when providing their
evaluations, which can deal with the self-confidence of them well. To apply the proposed group
decision making method to environmental pollution emergency management, a novel determination
of the decision weights of experts is given combining the subjective and objective weights. The
subjective weight can be directly assigned by organizer, while the objective weight is determined
by the self-confidence degree of experts on their evaluations. Afterwards, by utilizing the weighted
averaging operator, the individualsâ evaluations can be aggregated into a collective one. To do
that, some operational laws for self-confident fuzzy preference relations are introduced. And then,
a self-confidence score function is designed to get the best solution for environmental pollution
emergency management. Finally, some analyses and discussions show that the proposed method is
feasible and effective.The work was supported by National Key R&D Program of China (Grant No.
2017YFC0404600), National Natural Science Foundation of China (NSFC) under Grants (71871085, 71471056),
Qing Lan Project of Jiangsu Province. Additionally, Xia Liu andWeike Zhang gratefully acknowledge the financial
support of the China Scholarship Council (Nos. 201706710084, 201806240231)
Multi-criteria group decision making with a partialranking-based ordinal consensus reaching process for automotive development management
The consensus reaching process (CRP) aims at reconciling the
conflicts between individual preferences when eliciting collective
preferences. The ordinal CRP based on the positional orders of
alternatives in linear rankings is straightforward and robust; however, for partial rankings involving preference, indifference and
incomparability relations, there is no explicit positional order but
are binary relations. This study focuses on partial rankings that
may occur when using the ORESTE (organısation, rangement et
Synthese de donnees relarionnelles, in French) method for making
decisions, and designs an ordinal CRP pertaining to the binary
relations of alternatives. Concretely, we propose an enhanced
ordinal consensus measure with two hierarchies to measure the
agreement levels between individual partial rankings. Consensus
degrees are calculated based on the frequency distribution of binary relation types, which can avoid subjective axiomatic assumptions on the relations themselves. Besides, a consensus threshold
determination method close to cognitive expression is developed.
A feedback mechanism is designed to aid experts to modify preferences towards group consensus. An example about the evaluation of automotive design schemes is presented to validate the
proposed ordinal CRP. A ranking result that allows the incomparability relations of design schemes is obtained after the information exchange among experts
Large-Scale Green Supplier Selection Approach under a Q-Rung Interval-Valued Orthopair Fuzzy Environment
As enterprises pay more and more attention to environmental issues, the green supply chain management (GSCM) mode has been extensively utilized to guarantee proïŹt and sustainable development. Greensupplierselection(GSS),whichisakeysegmentofGSCM,hasbeeninvestigated to put forward plenty of GSS approaches
Outlier identification and group satisfaction of rating experts: density-based spatial clustering of applications with noise based on multi-objective large-scale group decision-making evaluation
Group satisfaction is a trending issue in large-scale group decision-
making (LSGDM) but most existing studies maximize the
group satisfaction of LSGDM from the perspective of consensus.
However, the clustering algorithm in LSGDM also has an impact
on group satisfaction. Hence, this paper proposes a density-based
spatial clustering of applications with noise (DBSCAN)-based
LSGDM approach in an intuitionistic fuzzy set (IFS) environment.
The DBSCAN algorithm is used to identify experts with outlier ratings
that can reduce the time consumption and iterations of the
LSGDM process and maximize the satisfaction of the group decision.
An easy-to-use function is then provided to estimate group
satisfaction. Finally, a numerical example of data centre supplier
evaluation and comparative analysis is constructed to validate the
rationality and feasibility of the proposed DBSCAN-based LSGDM
approach in an IFS environment. The results demonstrate that the
proposed method can effectively identify outliers in expert ratings
and improve group satisfaction in the LSGDM process
An Influence-Driven Feedback System for Preference Similarity Network Clustering Based Consensus Group Decision Making Model
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 group decision making (CGDM) allows the integration within this area of study of other advanced frameworks such as Social Network Analysis (SNA), Social Influence Network (SIN), clustering and trust-based concepts, among others. These complementary frameworks help to bridge the gap between their corresponding theories in such a way that important elements are not overlooked and are appropriately taken into consideration. In this paper, a new influence-driven feedback mechanism procedure is introduced for a preference similarity network clustering based consensus reaching process. The proposed influence-driven feedback mechanism aims at identifying the network influencer for the generation of advices. This procedure ensures that valuable recommendations are coming from the expert with most similar preferences with the other experts in the group. This is achieved by adapting, from the SIN theory into the CGDM context, an eigenvector-like measure of centrality for the purpose of: (i) measuring the influence score of experts, and (ii) determining the network influencer. Based on the initial evaluations on a set of alternatives provide by the experts in a group, the proposed influence score measure, which is named the sigma-centrality, is used to define the similarity social influence network (SSIN) matrix. The sigma-centrality is obtained by taking into account both the endogenous (internal network connections) and exogenous (external) factors, which means that SSIN connections as well as the opinion contribution from third parties are permitted in the nomination of the network influencer. The influence-driven feedback mechanism process is designed based on the satisfying of two important conditions to ensure that (1) the revised consensus degree is above the consensus threshold and that (2) the clustering solution is improved
Hesitant fuzzy linguistic DNMA method with cardinal consensus reaching process for shopping mall location selection
The hesitant fuzzy linguistic term set is an effective tool to express qualitative evaluations since it is close to human reasoning and expressing habits. In this paper, we propose a multi-expert multi-criterion decision-making method integrating the double normalization-based multi-aggregation (DNMA) method with a cardinal consensus reaching process, where the assessments of alternatives over multiple criteria are expressed as hesitant fuzzy linguistic term sets. To do so, the DNMA method involving double normalizations and three aggregation tools is extended to deal with the hesitant fuzzy linguistic information and derive the ranking of alternatives with respect to each expert. In addition, a cardinal consensus reaching process is introduced to help experts reach an acceptable consensus level. In other words, the soft consensus is considered in the multi-expert multi-criterion decision-making process. Subsequently, an extended Borda rule is developed to aggregate the subordinate ranks and integrated scores of alternatives, and then deduce the comprehensive ranking of alternatives. A case study is given to illustrate the practicability of the proposed method for selecting the optimal geographical location of a larger-scale shopping mall in the new urbanization for a construction investment agency. The proposed method is compared with other ranking methods to illustrate its advantages
An analysis of consensus approaches based on different concepts of coincidence
The file attached to this record is the author's final peer reviewed version.Soft consensus is a relevant topic in group decision making problems. Soft consensus measures are utilized to reflect the different agreement degrees between the experts leading the consensus reaching process. This may determine the final decision and the time needed to reach it. The concept of coincidence has led to two main approaches to calculating the soft consensus measures, namely, concordance among expert preferences and concordance among individual solutions. In the first approach the coincidence is obtained by evaluating the similarity among the expert preferences, while in the second one the concordance is derived from the measurement of the similarity among the solutions proposed by these experts. This paper performs a comparative study of consensus approaches based on both coincidence approaches. We obtain significant differences between both approaches by comparing several distance functions for measuring expert preferences and a consensus measure over the set of alternatives for measuring the solutions provided by experts. To do so, we use the nonparametric Wilcoxon signed-ranks test. Finally, these outcomes are analyzed using Friedman mean ranks in order to obtain a quantitative classification of the considered measurements according to the convergence criterion considered in the consensus reaching process
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