4,344 research outputs found
An overview on managing additive consistency of reciprocal preference relations for consistency-driven decision making and Fusion: Taxonomy and future directions
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.The reciprocal preference relation (RPR) is a powerful tool to represent decision makersâ preferences in decision making problems. In recent years, various types of RPRs have been reported and investigated, some of them being the âclassicalâ RPRs, interval-valued RPRs and hesitant RPRs. Additive consistency is one of the most commonly used property to measure the consistency of RPRs, with many methods developed to manage additive consistency of RPRs. To provide a clear perspective on additive consistency issues of RPRs, this paper reviews the consistency measurements of the different types of RPRs. Then, consistency-driven decision making and information fusion methods are also reviewed and classified into four main types: consistency improving methods; consistency-based methods to manage incomplete RPRs; consistency control in consensus decision making methods; and consistency-driven linguistic decision making methods. Finally, with respect to insights gained from prior researches, further directions for the research are proposed
A Consensus Model for Group Decision Making with Hesitant Fuzzy Information
This article presents a more improved consensus-based method for dealing with multi-person decision making (MPDM) that uses hesitant fuzzy preference relations (HFPRĂs) that arenĂt in the usual format. We proposed a Lukasiewicz transitivity (TL-transitivity)-based technique for establishing normalised hesitant fuzzy preference relations (NHFPRĂs) at the most essential level, after that, a model based on consensus is constructed. After that, a transitive closure formula is created in order to build TL -consistent hesitant fuzzy preference relations (HFPRĂs) and symmetrical matrices. Afterwards, a consistency analysis is performed to determine the degree of consistency of the data given by the decision makers (DMs), as a result, the consistency weights must be assigned to them. After combining consistency weights and preset(predeĂned) priority weights, the Ănal priority weights vector of DMs is obtained (if there are any). The consensus process determines either data analysis and selection of a suitable alternative should be done directly or externally. The enhancement process aims to improve the DMĂs consensus measure, despite the implementation of an indicator for locating sluggish points, in the circumstance that an unfavorable agreement is achieved. Finally, a comparison case demonstrates the relevance and e§ectiveness of the proposed system. The conclusions indicate that the suggested strategy can provide insight into the MPDM system
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 expertsâ 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 expertsâ 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 expertsâ 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
An optimal feedback model to prevent manipulation behaviours in consensus under social network group decision making
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.A novel framework to prevent manipulation behaviour
in consensus reaching process under social network
group decision making is proposed, which is based on a theoretically
sound optimal feedback model. The manipulation
behaviour classification is twofold: (1) âindividual manipulationâ
where each expert manipulates his/her own behaviour to achieve
higher importance degree (weight); and (2) âgroup manipulationâ
where a group of experts force inconsistent experts to adopt
specific recommendation advices obtained via the use of fixed
feedback parameter. To counteract âindividual manipulationâ, a
behavioural weights assignment method modelling sequential
attitude ranging from âdictatorshipâ to âdemocracyâ is developed,
and then a reasonable policy for group minimum adjustment cost
is established to assign appropriate weights to experts. To prevent
âgroup manipulationâ, an optimal feedback model with objective
function the individual adjustments cost and constraints related
to the threshold of group consensus is investigated. This approach
allows the inconsistent experts to balance group consensus and
adjustment cost, which enhances their willingness to adopt the
recommendation advices and consequently the group reaching
consensus on the decision making problem at hand. A numerical
example is presented to illustrate and verify the proposed optimal
feedback model
Pairwise Comparison and Distance Measure of Hesitant Fuzzy Linguistic Term Sets
A hesitant fuzzy linguistic term set (HFLTS), allowing experts using several possible linguistic terms to assess a qualitative linguistic variable, is very useful to express peopleâs hesitancy in practical decision-making problems. Up to now, a little research has been done on the comparison and distance measure of HFLTSs. In this paper, we present a comparison method for HFLTSs based on pairwise comparisons of each linguistic term in the two HFLTSs. Then, a distance measure method based on the pairwise comparison matrix of HFLTSs is proposed, and we prove that this distance is equal to the distance of the average values of HFLTSs, which makes the distance measure much more simple. Finally, the pairwise comparison and distance measure methods are utilized to develop two multicriteria decision-making approaches under hesitant fuzzy linguistic environments. The results analysis shows that our methods in this paper are more reasonable
Fuzzy Techniques for Decision Making 2018
Zadeh's fuzzy set theory incorporates the impreciseness of data and evaluations, by imputting the degrees by which each object belongs to a set. Its success fostered theories that codify the subjectivity, uncertainty, imprecision, or roughness of the evaluations. Their rationale is to produce new flexible methodologies in order to model a variety of concrete decision problems more realistically. This Special Issue garners contributions addressing novel tools, techniques and methodologies for decision making (inclusive of both individual and group, single- or multi-criteria decision making) in the context of these theories. It contains 38 research articles that contribute to a variety of setups that combine fuzziness, hesitancy, roughness, covering sets, and linguistic approaches. Their ranges vary from fundamental or technical to applied approaches
Expanding Grey Relational Analysis With the Comparable Degree for Dual Probabilistic Multiplicative Linguistic Term Sets and Its Application on the Cloud Enterprise
Under the cloud trend of enterprises, how do traditional businesses get on the cloud becomes a
worth pondering question. To help those traditional businesses that have no experience to dispel the clouds
and see the sun as soon as possible, we are planning to choose one corporation with rich experience to take
them into the cloud market. The quintessence of dual probabilistic linguistic term sets (DPLTSs) is that it uses
the combination of several linguistic terms and their proportions to reveal decision information by opposite
angles. This paper proposes the dual probabilistic multiplicative linguistic preference relations (DPMLPRs)
based upon the dual probabilistic multiplicative linguistic term sets (DPMLTSs). Then, it de nes the
comparable degree between the DPMLPRs and studies the consensus of the group DPMLPR. Moreover,
it probes the expanding grey relational analysis (EGRA) under the proposed comparable degree between the
DPMLTSs. After that, one example of choosing the experienced cloud cooperative partner is simulated under
the dual probabilistic linguistic circumstance. Besides, the comparative analysis is performed by considering
the similarity among the EGRA, TODIM, and VIKOR.Postgraduate Research and Practice Innovation Program of Jiangsu Province under Grant KYCX18_0199Scientific Research Foundation of the Graduate School of Southeast University under Grant
YBJJ1832FEDER Financial Support under Grant TIN2016-75850-
The generalized dice similarity measures for multiple attribute decision making with hesitant fuzzy linguistic information
In this paper, we shall present some novel Dice similarity measures of hesitant fuzzy linguistic term sets and the generalized Dice similarity measures of hesitant fuzzy linguistic term sets and indicate that the Dice similarity measures and asymmetric measures (projection measures) are the special cases of the generalized Dice similarity measures in some parameter values. Then, we propose the generalized Dice similarity measures-based multiple attribute decision making models with hesitant fuzzy linguistic term sets. Finally, a practical example concerning the evaluation of the quality of movies is given to illustrate the applicability and advantage of the proposed generalized Dice similarity measure
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