477 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
Interval Consistency Repairing Method for Double Hierarchy Hesitant Fuzzy Linguistic Preference Relation and Application in the Diagnosis of Lung Cancer
Natural language is more in line with the real thoughts of people
than crisp numbers considering that qualitative language information
is more consistent with the expression habits of experts.
Double hierarchy hesitant fuzzy linguistic preference relation
(DHHFLPR) can be used to express complex linguistic preference
information accurately because the pairwise comparison methods
are more accurate than non-pairwise methods. Consistency
reflects the rationalization of a preference relation and can be
used to judge whether a preference relation is self-contradictory
or not. In this paper, an interval consistency index of DHHFLPR is
developed, which is consisted by the consistency indices of all
double hierarchy linguistic preference relations associated with
the DHHFLPR. Additionally, an average consistency index of
DHHFLPR is given by calculating the average value of the consistency
indices of all double hierarchy linguistic preference relations.
Moreover, we develop a consistency checking and repairing
method for DHHFLPR. Finally, we apply the proposed method
into a practical group decision-making problem that is to identify
the most critical factors in developing lung cancer, and some
comparative analyses involving the connections and differences
among the proposed consistency indices are analysed
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
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
Consistency-driven methodology to manage incomplete linguistic preference relation: A perspective based on personalized individual semantics
The file attached to this record is the author's final peer reviewed version.In linguistic decision making problems there may be cased when decision makers will not be able to provide complete linguistic preference relations. However, when estimating unknown linguistic preference values in incomplete preference relations, the existing research approaches ignore the fact that words mean different things for different people, i.e. decision makers have
personalized individual semantics (PISs) regarding words. To manage incomplete linguistic preference relations with PISs, in this paper we propose a consistency-driven methodology both to estimate the incomplete linguistic preference values and to obtain the personalized numerical meanings of linguistic values of the different decision makers. The proposed incomplete linguistic preference estimation method combines the characteristic of the personalized representation of decision makers and guarantees the optimum consistency of incomplete linguistic preference relations in the implementation process. Numerical examples and a comparative analysis are included to justify the feasibility of the PISs based incomplete linguistic preference estimation method
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
Integer programming modeling on group decision making with incomplete hesitant fuzzy linguistic preference relations
© 2013 IEEE. Complementing missing information and priority vector are of significance important aspects in group decision making (GDM) with incomplete hesitant fuzzy linguistic preference relations (HFLPRs). In this paper, an integer programming model is developed based on additive consistency to estimate missing values of incomplete HFLPRs by using additive consistency. Once the missing values are complemented, a mixed 0-1 programming model is established to derive the priority vectors from complete HFLPRs, in which the underlying idea of the mixed 0-1 programming model is the probability sampling in statistics and minimum deviation between the priority vector and HFLPR. In addition, we also propose a new GDM approach for incomplete HFLPRs by integrating the integer programming model and the mixed 0-1 programming model. Finally, two case studies and comparative analysis detail the application of the proposed models
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
Consistency improvement with a feedback recommendation in personalized linguistic 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.Consistency is an important issue in linguistic decision making with various consistency measures and consistency improving methods available in the literature. However, existing linguistic consistency studies omit the fact that words mean different things for different people, that is, decision makers' personalized individual semantics (PISs) over their expressed linguistic preferences are ignored. Therefore, the aim of this article is to propose a novel consistency improving approach based on PISs in linguistic group decision making. The proposed approach combines the characteristics of personalized representation and integrates the PIS-based model in measuring and improving the consistency of linguistic preference relations. A detailed numerical and comparative analysis to support the feasibility of the proposed approach is provided
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