A novel method based on extended uncertain 2-tuple linguistic muirhead mean operators to MAGDM under uncertain 2-tuple linguistic environment

The present work is focused on multi-attribute group decision-making (MAGDM) problems with the uncertain 2-tuple linguistic information (ULI2–tuple) based on new aggregation operators which can capture interrelationships of attributes by a parameter vector P. To begin with, we present some new uncertain 2-tuple linguistic MM aggregation (UL2–tuple-MM) operators to handle MAGDM problems with ULI2–tuple, including the uncertain 2-tuple linguistic Muirhead mean (UL2–tuple-MM) operator, uncertain 2-tuple linguistic weighted Muirhead mean (UL2–tuple-WMM) operator. In addition, we extend UL2–tuple-WMM operator to a new aggregation operator named extended uncertain 2-tuple linguistic weighted Muirhead mean (EUL2–tuple-WMM) operators in order to handle some decision-making problems with ULI2–tuple whose attribute values are expressed in ULI2–tuple and attribute weights are also 2-tuple linguistic information. Whilst, the some properties of these new aggregation operators are obtained and some special cases are discussed. Moreover, we propose a new method to solve the MAGDM problems with ULI2–tuple. Finally, a numerical example is given to show the validity of the proposed method and the advantages of proposed method are also analysed

Interval-valued 2-tuple hesitant fuzzy linguistic term set and its application in multiple attribute decision making

[EN] The hesitant fuzzy linguistic term sets can retain the completeness of linguistic information elicitation by assigning a set of possible linguistic terms to a qualitative variable. However, sometimes experts cannot make sure that the objects attain these possible linguistic terms but only provide the degrees of confidence to express their hesitant cognition. Given that the interval numbers can denote the possible membership degrees that an object belongs to a set, it is suitable and convenient to provide an interval-valued index to measure the degree of a linguistic variable to a given hesitant fuzzy linguistic term set. Inspired by this idea, we introduce the concept of interval-valued 2-tuple hesitant fuzzy linguistic term set (IV2THFLTS) based on the interval number and the hesitant fuzzy linguistic term set. Then, we define some interval-valued 2-tuple hesitant fuzzy linguistic aggregation operators. Afterwards, to overcome the instability of subjective weights, we propose a method to compute the weights of attributes. For the convenience of application, a method is given to solve the multiple attribute decision making problems with IV2THFLTSs. Finally, a case study is carried out to validate the proposed method, and some comparisons with other methods are given to show the advantages of the proposed method.The work was supported in part by the National Natural Science Foundation of China (Nos. 71501135, 71771156), the China Postdoctoral Science Foundation (2016T90863, 2016M602698), the Fundamental Research Funds for the central Universities (No. YJ201535), and the Scientific Research Foundation for Excellent Young Scholars at Sichuan University (No. 2016SCU04A23).Si, G.; Liao, H.; Yu, D.; Llopis Albert, C. (2018). Interval-valued 2-tuple hesitant fuzzy linguistic term set and its application in multiple attribute decision making. Journal of Intelligent & Fuzzy Systems. 34(6):4225-4236. https://doi.org/10.3233/JIFS-171967S4225423634

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

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

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

Generalized Hamacher aggregation operators for intuitionistic uncertain linguistic sets: Multiple attribute group decision making methods

© 2019 by the authors. In this paper, we consider multiple attribute group decision making (MAGDM) problems in which the attribute values take the form of intuitionistic uncertain linguistic variables. Based on Hamacher operations, we developed several Hamacher aggregation operators, which generalize the arithmetic aggregation operators and geometric aggregation operators, and extend the algebraic aggregation operators and Einstein aggregation operators. A number of special cases for the two operators with respect to the parameters are discussed in detail. Also, we developed an intuitionistic uncertain linguistic generalized Hamacher hybrid weighted average operator to reflect the importance degrees of both the given intuitionistic uncertain linguistic variables and their ordered positions. Based on the generalized Hamacher aggregation operator, we propose a method for MAGDM for intuitionistic uncertain linguistic sets. Finally, a numerical example and comparative analysis with related decision making methods are provided to illustrate the practicality and feasibility of the proposed method

Multiple-Attribute Decision-Making Method Using Similarity Measures of Hesitant Linguistic Neutrosophic Numbers Regarding Least Common Multiple Cardinality

Linguistic neutrosophic numbers (LNNs) are a powerful tool for describing fuzzy information with three independent linguistic variables (LVs), which express the degrees of truth, uncertainty, and falsity, respectively. However, existing LNNs cannot depict the hesitancy of the decision-maker (DM). To solve this issue, this paper first defines a hesitant linguistic neutrosophic number (HLNN), which consists of a few LNNs regarding an evaluated object due to DMs’ hesitancy to represent their hesitant and uncertain information in the decision-making process. Then, based on the least common multiple cardinality (LCMC), we present generalized distance and similarity measures of HLNNs, and then develop a similarity measure-based multiple-attribute decision-making (MADM) method to handle the MADM problem in the HLNN setting. Finally, the feasibility of the proposed approach is verified by an investment decision case

VIKOR method for multiple criteria group decision making under 2-tuple linguistic neutrosophic environment

In this article, the VIKOR method is proposed to solve the multiple criteria group decision making (MCGDM) with 2-tuple linguistic neutrosophic numbers (2TLNNs). Firstly, the fundamental concepts, operation formulas and distance calculating method of 2TLNNs are introduced. Then some aggregation operators of 2TLNNs are reviewed. Thereafter, the original VIKOR method is extended to 2TLNNs and the calculating steps of VIKOR method with 2TLNNs are proposed. In the proposed method, it’s more reasonable and scientific for considering the conflicting criteria. Furthermore, the VIKOR are extended to interval-valued 2-tuple linguistic neutrosophic numbers (IV2TLNNs). Moreover, a numerical example for green supplier selection has been given to illustrate the new method and some comparisons are also conducted to further illustrate advantages of the new method

Pythagorean 2-tuple linguistic power aggregation operators in multiple attribute decision making

In this paper, we investigate the multiple attribute decision making problems with Pythagorean 2-tuple linguistic information. Then, we utilize power average and power geometric operations to develop some Pythagorean 2-tuple linguistic power aggregation operators: Pythagorean 2-tuple linguistic power weighted average (P2TLPWA) operator, Pythagorean 2-tuple linguistic power weighted geometric (P2TLPWG) operator, Pythagorean 2-tuple linguistic power ordered weighted average (P2TLPOWA) operator, Pythagorean 2-tuple linguistic power ordered weighted geometric (P2TLPOWG) operator, Pythagorean 2-tuple linguistic power hybrid average (P2TLPHA) operator and Pythagorean 2-tuple linguistic power hybrid geometric (P2TLPHG) operator. The prominent characteristic of these proposed operators are studied. Then, we have utilized these operators to develop some approaches to solve the Pythagorean 2-tuple linguistic multiple attribute decision making problems. Finally, a practical example for enterprise resource planning (ERP) system selection is given to verify the developed approach and to demonstrate its practicality and effectiveness