352 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
Hesitant Fuzzy Linguistic Analytic Hierarchical Process With Prioritization, Consistency Checking, and Inconsistency Repairing
Analytic hierarchy process (AHP), as one of the most important methods to tackle multiple
criteria decision-making problems, has achieved much success over the past several decades. Given that
linguistic expressions are much closer than numerical values or single linguistic terms to a human way of
thinking and cognition, this paper investigates the AHP with comparative linguistic expressions. After providing
the snapshot of classical AHP and its fuzzy extensions, we propose the framework of hesitant
fuzzy linguistic AHP, which shows how to yield a decision for qualitative decision-making problems with
complex linguistic expressions. First, the comparative linguistic expressions over criteria or alternatives
are transformed into hesitant fuzzy linguistic elements and then the hesitant fuzzy linguistic preference
relations (HFLPRs) are constructed. Considering that HFLPRs may be inconsistent, we conduct consistency
checking and improving processes after obtaining priorities from the HFLPRs based on a linear programming
method. Regarding the consistency-improving process, we develop a new way to establish a perfectly
consistent HFLPR. The procedure of the hesitant fuzzy linguistic AHP is given in stepwise. Finally,
a numerical example concerning the used-car management in a lemon market is given to illustrate the
ef ciency of the proposed hesitant fuzzy linguistic AHP method.This work was supported in part by the National Natural Science Foundation of China under Grant 71771156, in part by the 2019 Sichuan
Planning Project of Social Science under Grant SC18A007, in part by the 2019 Soft Science Project of Sichuan Science and Technology
Department under Grant 2019JDR0141, and in part by the Project of Innovation at Sichuan University under Grant 2018hhs-43
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
Consistency and Consensus Driven for Hesitant Fuzzy Linguistic Decision Making with Pairwise Comparisons
Hesitant fuzzy linguistic preference relation (HFLPR) is of interest because
it provides an efficient way for opinion expression under uncertainty. For
enhancing the theory of decision making with HFLPR, the paper introduces an
algorithm for group decision making with HFLPRs based on the acceptable
consistency and consensus measurements, which involves (1) defining a hesitant
fuzzy linguistic geometric consistency index (HFLGCI) and proposing a procedure
for consistency checking and inconsistency improving for HFLPR; (2) measuring
the group consensus based on the similarity between the original individual
HFLPRs and the overall perfect HFLPR, then establishing a procedure for
consensus ensuring including the determination of decision-makers weights. The
convergence and monotonicity of the proposed two procedures have been proved.
Some experiments are furtherly performed to investigate the critical values of
the defined HFLGCI, and comparative analyses are conducted to show the
effectiveness of the proposed algorithm. A case concerning the performance
evaluation of venture capital guiding funds is given to illustrate the
availability of the proposed algorithm. As an application of our work, an
online decision-making portal is finally provided for decision-makers to
utilize the proposed algorithms to solve decision-making problems.Comment: Pulished by Expert Systems with Applications (ISSN: 0957-4174
Granular computing and optimization model-based method for large-scale group decision-making and its application
In large-scale group decision-making process, some decision makers hesitate among several linguistic terms and cannot compare
some alternatives, so they often express evaluation information
with incomplete hesitant fuzzy linguistic preference relations.
How to obtain suitable large-scale group decision-making results
from incomplete preference information is an important and
interesting issue to concern about. After analyzing the existing
researches, we find that: i) the premise that complete preference
relation is perfectly consistent is too strict, ii) deleting all incomplete linguistic preference relations that cannot be fully completed will lose valid assessment information, iii) semantics given
by decision makers are greatly possible to be changed during the
consistency improving process. In order to solve these issues, this
work proposes a novel method based on Granular computing
and optimization model for large-scale group decision-making,
considering the original consistency of incomplete hesitant fuzzy
linguistic preference relation and improving its consistency without changing semantics during the completion process. An illustrative example and simulation experiments demonstrate the
rationality and advantages of the proposed method: i) semantics
are not changed during the consistency improving process, ii)
completion process does not significantly alter the inherent quality of information, iii) complete preference relations are globally
consistent, iv) final large-scale group decision-making result is
acquired by fusing complete preference relations with different weights
Managing Incomplete Preference Relations in Decision Making: A Review and Future Trends
In decision making, situations where all experts are able to efficiently express their preferences over all the available options are the exception rather than the rule. Indeed, the above scenario requires all experts to possess a precise or sufficient level of knowledge of the whole problem to tackle, including the ability to discriminate the degree up to which some options are better than others. These assumptions can be seen unrealistic in many decision making situations, especially those involving a large number of alternatives to choose from and/or conflicting and dynamic sources of information. Some methodologies widely adopted in these situations are to discard or to rate more negatively those experts that provide preferences with missing values. However, incomplete information is not equivalent to low quality information, and consequently these methodologies could lead to biased or even bad solutions since useful information might not being taken properly into account in the decision process. Therefore, alternative approaches to manage incomplete preference relations that estimates the missing information in decision making are desirable and possible. This paper presents and analyses methods and processes developed on this area towards the estimation of missing preferences in decision making, and highlights some areas for future research
Ordering based decision making: a survey
Decision making is the crucial step in many real applications such as organization management, financial planning, products evaluation and recommendation. Rational decision making is to select an alternative from a set of different ones which has the best utility (i.e., maximally satisfies given criteria, objectives, or preferences). In many cases, decision making is to order alternatives and select one or a few among the top of the ranking. Orderings provide a natural and effective way for representing indeterminate situations which are pervasive in commonsense reasoning. Ordering based decision making is then to find the suitable method for evaluating candidates or ranking alternatives based on provided ordinal information and criteria, and this in many cases is to rank alternatives based on qualitative ordering information. In this paper, we discuss the importance and research aspects of ordering based decision making, and review the existing ordering based decision making theories and methods along with some future research directions
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
Group decision making with incomplete reciprocal preference relations based on multiplicative consistency
This paper comprises a new iterative method for multi-person decision making based on multiplicative consistency with incomplete reciprocal preference relations (IRPRs). Additionally, multiplicative transitivity property of reciprocal preference relation (RPR) is used at the first level to estimate the unknown preference values and get the complete preference relation, then it is confirmed to be multiplicative consistent by using transitive closure formula. Following this, expert's weights are evaluated by merging consistency and trust weights. The consistency weights against the experts are evaluated through multiplicative consistency investigation of the preferences given by each expert, while trust weights play the role to measure the level of trust for an expert. The consensus process determines whether the selection procedure should start or not. If it results in negative, the feedback mechanism is used to enhance the consensus degree. At the end, a numerical example is given to demonstrate the efficiency and practicality of the proposed method
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