Fuzzy decision making based on likelihood-based comparison relations of hesitant fuzzy linguistic term sets and hesitant fuzzy linguistic operators

Lee, Li-Wei; Chen, Shyi‐Ming

doi:10.1016/j.ins.2014.09.061

Cited by 138 publications

(58 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The preference degree between two HFLTSs has been studied by many researchers [24,27,28,65]; generally, we let H 1 S and H 2 S be the two HFLTSs on…”

Section: A Pseudo-distance Between Two Hfltssmentioning

confidence: 99%

“…To make a multi-criteria linguistic decision with HFLTSs, Rodríguez et al developed the "min − upper" and "max − lower" operators to combine HFLTSs and obtain a linguistic interval for each alternative; then the linguistic intervals are used to build a preference relation between alternatives, and a nondominance choice degree is applied to obtain a solution set of alternatives for the decision problem. The use of the min − upper and max − lower operators produced the first method to deal with hesitant fuzzy linguistic information; since then, many researchers have paid attention to linguistic decision making with HFLTSs, such as in [27], where Lee and Chen proposed likelihood-based comparison relations of HFLTSs and several hesitant fuzzy linguistic aggregation operators to overcome the drawbacks of the methods in [24,28]. In [29], Liu and Rodríguez proposed a fuzzy envelope of HFLTSs for linguistic decision making with HFLTSs.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A New Hesitant Fuzzy Linguistic TOPSIS Method for Group Multi-Criteria Linguistic Decision Making

2017

View full text Add to dashboard Cite

Hesitant fuzzy linguistic decision making is a focus point in linguistic decision making, in which the main method is based on preference ordering. This paper develops a new hesitant fuzzy linguistic TOPSIS method for group multi-criteria linguistic decision making; the method is inspired by the TOPSIS method and the preference degree between two hesitant fuzzy linguistic term sets (HFLTSs). To this end, we first use the preference degree to define a pseudo-distance between two HFLTSs and analyze its properties. Then we present the positive (optimistic) and negative (pessimistic) information of each criterion provided by each decision maker and aggregate these by using weights of decision makers to obtain the hesitant fuzzy linguistic positive and negative ideal solutions. On the basis of the proposed pseudo-distance, we finally obtain the positive (negative) ideal separation matrix and a new relative closeness degree to rank alternatives. We also design an algorithm based on the provided method to carry out hesitant fuzzy linguistic decision making. An illustrative example shows the elaboration of the proposed method and comparison with the symbolic aggregation-based method, the hesitant fuzzy linguistic TOPSIS method and the hesitant fuzzy linguistic VIKOR method; it seems that the proposed method is a useful and alternative decision-making method.

show abstract

“…The preference degree between two HFLTSs has been studied by many researchers [24,27,28,65]; generally, we let H 1 S and H 2 S be the two HFLTSs on…”

Section: A Pseudo-distance Between Two Hfltssmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Hesitant Fuzzy Linguistic TOPSIS Method for Group Multi-Criteria Linguistic Decision Making

2017

View full text Add to dashboard Cite

show abstract

“…However, traditionally, it is assumed that the information provided by them are crisp in nature. However, due to the complexity of the system day-by-day, the real-life contains many MCDM problems where the information is either vague, imprecise or uncertain in nature [3]. To deal with it, the theory of fuzzy set (FS) [4] or extended fuzzy sets such as intuitionistic fuzzy set (IFS) [5], interval-valued IFS (IVIFS) [6] are the most successful ones, which characterize the criterion values in terms of membership degrees.…”

Section: Introductionmentioning

confidence: 99%

Multi-Criteria Decision-Making Method Based on Prioritized Muirhead Mean Aggregation Operator under Neutrosophic Set Environment

Garg

nancy

2018

Symmetry

View full text Add to dashboard Cite

Abstract:The aim of this paper is to introduce some new operators for aggregating single-valued neutrosophic (SVN) information and to apply them to solve the multi-criteria decision-making (MCDM) problems. Single-valued neutrosophic set, as an extension and generalization of an intuitionistic fuzzy set, is a powerful tool to describe the fuzziness and uncertainty, and Muirhead mean (MM) is a well-known aggregation operator which can consider interrelationships among any number of arguments assigned by a variable vector. In order to make full use of the advantages of both, we introduce two new prioritized MM aggregation operators, such as the SVN prioritized MM (SVNPMM) and SVN prioritized dual MM (SVNPDMM) under SVN set environment. In addition, some properties of these new aggregation operators are investigated and some special cases are discussed. Furthermore, we propose a new method based on these operators for solving the MCDM problems. Finally, an illustrative example is presented to testify the efficiency and superiority of the proposed method by comparing it with the existing method.

show abstract

“…Wei et al [12] pointed out that Rodriguez et al 's method may not accord with common sense, and they constructed the new suitable possibility degree formula of HFLTSs by using the possibility degree theory. Lee and Chen [13] noted that the Rodriguez et al 's and Wei et al 's methods were defined by using the maximum and minimum operators, which can not rank the preference orders in some cases. Thus, they proposed a novel comparison method of HFLTSs based on the likelihood.…”

Section: Introductionmentioning

confidence: 99%

“…First of all, the possibility degree for comparing HIFLTSs has not been studied. With respect to the shortcomings of the existing possibility degree of HFLTSs [11][12][13], we propose an improved probability degree of HFLTSs, then on the basis of the above, we define the possibility degree of HIFLTSs. This is the first motivation of our work.…”

Section: Introductionmentioning

confidence: 99%

Grey Relational Bidirectional Projection Method for Multicriteria Decision Making with Hesitant Intuitionistic Fuzzy Linguistic Information

Zang

Zhao

et al. 2018

Mathematical Problems in Engineering

View full text Add to dashboard Cite

We investigate a novel approach for multicriteria decision making (MCDM) with hesitant intuitionistic fuzzy linguistic information. To compare the hesitant intuitionistic fuzzy linguistic term sets (HIFLTSs), we propose a comparison method of HIFLTSs. A family of distance measures of HIFLTSs is developed. After that, we propose the grey relational bidirectional projection method based on the proposed comparison method and distance measures of IVHFLNs for dealing with MCDM problems. Furthermore, we establish a nonlinear optimization model to obtain the weight vector of criteria. Finally, an illustrative example is given to demonstrate the effectiveness and flexibility of the proposed approach.

show abstract

Fuzzy decision making based on likelihood-based comparison relations of hesitant fuzzy linguistic term sets and hesitant fuzzy linguistic operators

Cited by 138 publications

References 38 publications

A New Hesitant Fuzzy Linguistic TOPSIS Method for Group Multi-Criteria Linguistic Decision Making

A New Hesitant Fuzzy Linguistic TOPSIS Method for Group Multi-Criteria Linguistic Decision Making

Multi-Criteria Decision-Making Method Based on Prioritized Muirhead Mean Aggregation Operator under Neutrosophic Set Environment

Grey Relational Bidirectional Projection Method for Multicriteria Decision Making with Hesitant Intuitionistic Fuzzy Linguistic Information

Contact Info

Product

Resources

About