Linguistic decision analysis: steps for solving decision problems under linguistic information

By Francisco Herrera, Enrique Herrera-Viedma. Fuzzy Sets Syst., 2000

1,633 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/b33bc523b3c94a644817e649bd302dcdd456c2c4

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In Computational Theory and Mathematics

In Management Science and Operations Research

Citations in the corpus

A fuzzy approach for supplier evaluation and selection in supply chain management (2006, background): “Because linguistic assessments merely approximate the subjective judgment of decision-makers, we can consider linear trapezoidal membership functions to be adequate for capturing the vagueness of these linguistic assessments (Delgado et al., 1998; Herrera et al., 1996; Herrera and Herrera-Viedma, 20”
A multicriteria intuitionistic fuzzy group decision making for plant location selection with ELECTRE method (2012, background): “Variables whose values are not numbers, but words or sentences in natural or artificial languages, are called linguistic variables [52, 53].”
Deviation measures of linguistic preference relations in group decision making (2005, background): “, when evaluating the speed of a car, linguistic terms like “fast”, “very fast”, “slow” may be used [4]; when evaluating the “comfort” or “design” of a car, terms like “good”, “medium”, “bad” can be used [11]).”
Hesitant Fuzzy Linguistic VIKOR Method and Its Application in Qualitative Multiple Criteria Decision Making (2015, background): “In such an approach, experts’ opinions are taken as the values of a linguistic variable which is established by a linguistic descriptors and its semantics [45].”
Fuzzy performance evaluation in Turkish Banking Sector using Analytic Hierarchy Process and TOPSIS (2009, background): “Herrera and Herrera-Viedma (2000) sustained that linguistic terms are intuitively easier to use when decision makers express the subjectivity and imprecision of their assessments.”
Induced uncertain linguistic OWA operators applied to group decision making (2006, methodology): “The linguistic approach is an approximate technique, which represents qualitative aspects as linguistic values by means of linguistic variables [7–14].”
Computing with Words in Decision support Systems: An overview on Models and Applications (2010, methodology): “The use of linguistic information plays a key role not only in linguistic decision making 33 , 35 , 63 but also in other ﬁelds 2 , 43 , 44 , 75 , 85 that need to operate with linguistic information.”
Consensus reaching in social network group decision making: Research paradigms and challenges (2018, background): “Decision makers' preferences on a set of alternatives X can be represented in different ways using distinct preference representation structures, such as: utility values [22]; preference orderings [23,24]; additive preference relations [25-27]; linguistic preferences [28-32]; multiple attribute pref”
Using fuzzy number for measuring quality of service in the hotel industry (2007, background): “Herrera and Herrera-Viedma (2000) sustained that linguistic terms are intuitively easier to use when decision makers express the subjectivity and imprecision of their assessments, and for this reason fuzzy set theory is becoming a very popular method in the field of the evaluation of the performance”
Computing with words in decision making: foundations, trends and prospects (2009, background): “In recent years, many researchers have seen CW as a very interesting methodology to be applied in decision making [40].”
Fuzzy decision making: A bibliometric-based review (2017, neutral): “In TFDM, Herrera-Viedma E has 7384 citations, Her-rera F has 6896 citations and ZS Xu 5626 citations.”
An approach to multiattribute decision making with interval-valued intuitionistic fuzzy assessments and incomplete weights (2009, background): “24 This section adapts an investment decision problem in [12] to demonstrate how to 567 apply the proposed approach.”
Selecting suppliers using a new fuzzy multiple criteria decision model: the fuzzy balancing and ranking method (2010, methodology): “…instead of numerical values, that is, to suppose that the ratings of the alternatives by decision makers under criteria in the problem are assessed by means of linguistic variables (Bellman and Zadeh 1970, Delgado et al. 1992, Herrera et al. 1996, Chen 2000, Herrera and Herrera-Viedma 2000).”
Application of TOPSIS in evaluating initial training aircraft under a fuzzy environment (2007, background): “Since words are less precise than numbers, the concept of a linguistic variable approximately characterizes phenomena that are too cumbersome or poorly defined to be described with conventional quantitative terms (Herrera & Herrera-Viedma, 2002).”
Applying fuzzy linguistic preference relations to the improvement of consistency of fuzzy AHP (2008, background): “The following briefly describes some definitions and propositions presented in [8,9,17–23,25].”