scholarly journals Some Interval 2-Tuple Linguistic Harmonic Mean Operators and Their Application in Material Selection

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Meng-Meng Shan ◽  
Jian-Xin You ◽  
Hu-Chen Liu
Keyword(s):  
Group Decision ◽  
Material Selection ◽  
Aggregation Operators ◽  
Harmonic Mean ◽  
Linguistic Variables ◽  
Practical Application ◽  
Linguistic Information ◽  
Selection Problems ◽  
Operational Laws ◽  
Multiple Attribute

We investigate the multiple attribute group material selection problems in which the attribute values take the form of interval 2-tuple linguistic information. Firstly, some operational laws and possibility degree of interval 2-tuple linguistic variables are introduced. Then, we develop some interval 2-tuple linguistic aggregation operators called interval 2-tuple hybrid harmonic mean (ITHHM) operator, induced interval 2-tuple ordered weighted harmonic mean (I-ITOWHM) operator, and induced interval 2-tuple hybrid harmonic mean (I-ITHHM) operator and study some desirable properties of the I-ITOWHM operator. In particular, all these operators can be reduced to aggregate 2-tuple linguistic variables. Based on the I-ITHHM and the ITWHM (interval 2-tuple weighted harmonic mean) operators, an approach to multiple attribute group decision-making with interval 2-tuple linguistic information is proposed. Finally, a practical application to material selection problem is given to verify the developed approach and to demonstrate its practicality and effectiveness.

Research on Evaluating Theory and Application to Design of Mechanism Scheme with Uncertain Linguistic Information

2012 ◽  
Vol 201-202 ◽  
pp. 749-752
Author(s):  
Tie Jun Wang ◽  
Chang Zhong Hao
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Primary Phase ◽  
Harmonic Mean ◽  
Linguistic Variables ◽  
Product Lifecycle ◽  
Linguistic Information ◽  
Multiple Attribute ◽  
Magdm Problems

The design of mechanism scheme is the primary phase and the creative and challenging part in product lifecycle. In this paper, we research the multiple attribute group decision making (MAGDM) problems for evaluating the design of mechanism scheme with uncertain linguistic variables. We employ the uncertain linguistic weighted harmonic mean (ULWHM) operator to aggregate the uncertain linguistic information corresponding to each alternative and get the overall value of the alternatives, then rank the alternatives and select the most desirable one(s) by using the formula of the degree of possibility for the comparison between two uncertain linguistic variables. Finally, a practical example for evaluating the design of mechanism scheme is used to illustrate the developed procedures.

Dependent Interval 2-Tuple Linguistic Aggregation Operators and Their Application to Multiple Attribute Group Decision Making

2014 ◽  
Vol 22 (05) ◽  
pp. 717-735 ◽  
Author(s):  
Hu-Chen Liu ◽  
Qing-Lian Lin ◽  
Jing Wu
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Aggregation Operators ◽  
Decision Makers ◽  
Weighted Averaging ◽  
Linguistic Variables ◽  
Preference Information ◽  
Operational Laws ◽  
Multiple Attribute

Consider the various types of uncertain preference information provided by the decision makers and the importance of determining the associated weights for the aggregation operator, the multiple attribute group decision making (MAGDM) methods based on some dependent interval 2-tuple linguistic aggregation operators are proposed in this paper. Firstly some operational laws and possibility degree of interval 2-tuple linguistic variables are introduced. Then, we develop a dependent interval 2-tuple weighted averaging (DITWA) operator and a dependent interval 2-tuple weighted geometric (DITWG) operator, in which the associated weights only depend on the aggregated interval 2-tuple arguments and can relieve the influence of unfair arguments on the aggregated results by assigning low weights to them. Based on the DITWA and the DITWG operators, some approaches for multiple attribute group decision making with interval 2-tuple linguistic information are proposed. Finally, an illustrative example is given to demonstrate the practicality and effectiveness of the proposed approaches.

SOME INDUCED UNCERTAIN GEOMETRIC AGGREGATION OPERATORS WITH PURE LINGUISTIC INFORMATION AND THEIR APPLICATION TO GROUP DECISION MAKING

2013 ◽  
Vol 21 (05) ◽  
pp. 723-742 ◽  
Author(s):  
BO PENG ◽  
CHUNMING YE
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Geometric Mean ◽  
Aggregation Operators ◽  
Linguistic Variables ◽  
Linguistic Information ◽  
Attribute Weights ◽  
Multiple Attribute ◽  
Linguistic Labels

In this paper, we develop some new aggregation operators with pure linguistic information including the uncertain pure linguistic weighted geometric mean (UPLWGM) operator, the induced uncertain pure linguistic ordered weighted geometric mean (IUPLOWGM) operator, and the induced uncertain pure linguistic hybrid geometric mean (IUPLHGM) operator. These developed aggregation operators are very suitable to deal with the situation where the input arguments are represented in uncertain pure linguistic variables. Also, as a more general type of aggregation operator, the IUPLHGM operator is based on the UPLWGM and IUPLOWGM operators, and it can reflect the importance degrees of both the given uncertain linguistic variables and their ordered positions. Moreover, in the situations where the information about all the attribute weights, the attribute values and the expert weights are expressed in the form of linguistic labels variables, we develop an approach based on the IUPLHGM operator for multiple attribute group decision making with pure linguistic information. Finally, an application of the developed approach to group decision making problem regarding the selection of investments is given. Also, we present a comparative analysis with other related decision making methods to demonstrate the effectiveness of the developed approach.

Model for Software Patterns Selection with Fuzzy Linguistic Information

2016 ◽  
Vol 13 (10) ◽  
pp. 7533-7537
Author(s):  
Zhi-Min Li ◽  
Yi-Ding Zhao
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Practical Method ◽  
Harmonic Mean ◽  
Linguistic Variables ◽  
Linguistic Information ◽  
Attribute Weights ◽  
Multiple Attribute ◽  
Fuzzy Linguistic

With respect to multiple attribute group decision making problem with triangular fuzzy linguistic information, in which the attribute weights and expert weights take the form of real numbers, and the preference values take the form of triangular fuzzy linguistic variables, some operators for aggregating triangular fuzzy linguistic variables, such as the fuzzy linguistic harmonic mean (FLHM) operator, fuzzy linguistic weighted harmonic mean (FLWHM) operator, fuzzy linguistic ordered weighted harmonic mean (FLOWHM) operator, and fuzzy linguistic hybrid harmonic mean (FLHHM) operator are proposed. Based on the FLWHM and FLHHM operators, a practical method is developed for group decision making with triangular fuzzy linguistic variables. Finally, an illustrative example about software patters selection is given to verify the developed approach.

AN APPROACH TO PURE LINGUISTIC MULTIPLE ATTRIBUTE DECISION MAKING UNDER UNCERTAINTY

2005 ◽  
Vol 04 (02) ◽  
pp. 197-206 ◽  
Author(s):  
ZESHUI XU
Keyword(s):  
Decision Making ◽  
Multiple Attribute Decision Making ◽  
Aggregation Operator ◽  
Aggregation Operators ◽  
Decision Making Under Uncertainty ◽  
Linguistic Variables ◽  
Linguistic Information ◽  
Attribute Weights ◽  
Operational Laws ◽  
Multiple Attribute

The pure linguistic multiple attribute decision making problems are studied, in which the information about the attribute weights are expressed in the form of linguistic variables or uncertain linguistic variables, and the attribute values take the form of uncertain linguistic variables. The operational laws of uncertain linguistic variables are introduced, and two uncertain linguistic aggregation operators called linguistic weighted aggregation operator and uncertain linguistic weighted aggregation operator are developed based on uncertain linguistic variables and their operational laws. An approach based on the developed operators for pure linguistic multiple attribute decision making under uncertainty is proposed. The prominent characteristic of the proposed approach is that it can compute with uncertain linguistic information directly. Furthermore, the approach is straightforward and does not produce any loss of information. Finally, an illustrative example is given.

scholarly journals Triangular Cubic Hesitant Fuzzy Einstein Hybrid Weighted Averaging Operator and Its Application to Decision Making

Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 658 ◽  
Author(s):  
Aliya Fahmi ◽  
Fazli Amin ◽  
Florentin Smarandache ◽  
Madad Khan ◽  
Nasruddin Hassan
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Aggregation Operators ◽  
Weighted Averaging ◽  
Linguistic Information ◽  
Ordered Weighted Averaging ◽  
Averaging Operator ◽  
Multiple Attribute ◽  
The Relationship

In this paper, triangular cubic hesitant fuzzy Einstein weighted averaging (TCHFEWA) operator, triangular cubic hesitant fuzzy Einstein ordered weighted averaging (TCHFEOWA) operator and triangular cubic hesitant fuzzy Einstein hybrid weighted averaging (TCHFEHWA) operator are proposed. An approach to multiple attribute group decision making with linguistic information is developed based on the TCHFEWA and the TCHFEHWA operators. Furthermore, we establish various properties of these operators and derive the relationship between the proposed operators and the existing aggregation operators. Finally, a numerical example is provided to demonstrate the application of the established approach.

2-tuple linguistic Muirhead mean operators for multiple attribute group decision making and its application to supplier selection

Kybernetes ◽  
2016 ◽  
Vol 45 (1) ◽  
pp. 2-29 ◽  
Author(s):  
Jindong Qin ◽  
Xinwang Liu
Keyword(s):  
Decision Making ◽  
Supplier Selection ◽  
Group Decision ◽  
Aggregation Operators ◽  
Management Decision ◽  
Linguistic Information ◽  
Content Type ◽  
Multiple Attribute ◽  
Supplier Selection Problem ◽  
Magdm Problems

Purpose – The purpose of this paper is to develop some 2-tuple linguistic aggregation operators based on Muirhead mean (MM), which is combined with multiple attribute group decision making (MAGDM) and applied the proposed MAGDM model for supplier selection under 2-tuple linguistic environment. Design/methodology/approach – The supplier selection problem can be regarded as a typical MAGDM problem, in which the decision information should be aggregated. In this paper, the authors investigate the MAGDM problems with 2-tuple linguistic information based on traditional MM operator. The MM operator is a well-known mean type aggregation operator, which has some particular advantages for aggregating multi-dimension arguments. The prominent characteristic of the MM operator is that it can capture the whole interrelationship among the multi-input arguments. Motivated by this idea, in this paper, the authors develop the 2-tuple linguistic Muirhead mean (2TLMM) operator and the 2-tuple linguistic dual Muirhead mean (2TLDMM) operator for aggregating the 2-tuple linguistic information, respectively. Some desirable properties and special cases are discussed in detail. Based on which, two approaches to deal with MAGDM problems under 2-tuple linguistic information environment are developed. Finally, a numerical example concerns the supplier selection problem is provided to illustrate the effectiveness and feasibility of the proposed methods. Findings – The results show that the proposed can solve the MAGDM problems within the context of 2-tuple linguistic information, in which the attributes are existing interaction phenomenon. Some 2-tuple aggregation operators based on MM have been developed. A case study of supplier selection is provided to illustrate the effectiveness and feasibility of the proposed methods. The results show that the proposed methods are useful to aggregate the linguistic decision information in which the attributes are not independent so as to select the most suitable supplier. Practical implications – The proposed methods can solve the 2-tuple linguistic MAGDM problem, in which the interactions exist among the attributes. Therefore, it can be used to supplier selection problems and other similar management decision problems. Originality/value – The paper develop some 2-tuple aggregation operators based on MM, and further present two methods based on the proposed operators for solving MAGDM problems. It is useful to deal with multiple attribute interaction decision-making problems and suitable to solve a variety of management decision-making applications.

scholarly journals THE MULTI-ATTRIBUTE GROUP DECISION MAKING METHOD BASED ON THE INTERVAL GREY LINGUISTIC VARIABLES WEIGHTED HARMONIC AGGREGATION OPERATORS

2013 ◽  
Vol 19 (3) ◽  
pp. 409-430 ◽  
Author(s):  
Fang Jin ◽  
Peide Liu ◽  
Xin Zhang
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Aggregation Operators ◽  
Computational Results ◽  
Linguistic Variables ◽  
Operation Rules ◽  
Operator Interval ◽  
Multiple Attribute ◽  
Novel Method

With respect to the characteristics of fuzziness, complexity and uncertainty for many group-decision making problems in real world, the paper proposes a novel method based on the interval grey linguistic variables hybrid weighted harmonic aggregation operators to solve the multiple attribute group decision making problems in which the attribute values and the weights take the form of the interval grey linguistic variables. In the approach, the relative concepts and the operation rules of interval grey linguistic variables are defined, and some operators (such as interval grey linguistic weighted harmonic aggregation (IGLWHA) operator, interval grey linguistic ordered weighted harmonic aggregation (IGLOWHA) operator, and interval grey linguistic hybrid weighted harmonic aggregation (IGLHWHA) operator) are proposed to solve the group decision making problems. The computational results from an illustrative example have shown that the proposed approach is feasible and effective for the group-decision making problems.

scholarly journals SOME HERONIAN MEAN OPERATORS WITH 2-TUPLE LINGUISTIC INFORMATION AND THEIR APPLICATION TO MULTIPLE ATTRIBUTE GROUP DECISION MAKING

2015 ◽  
Vol 21 (5) ◽  
pp. 797-814 ◽  
Author(s):  
Ye Ye ◽  
Peide LIU
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Aggregation Operators ◽  
Linguistic Information ◽  
Multiple Attribute ◽  
Heronian Mean

With respect to multi-attribute group decision-making problems, in which attribute values take the form of 2-tuple linguistic information, a new decision making method that considers the interrelationships of attribute values is proposed. Firstly, some new aggregation operators of 2-tuple linguistic information based on Heronian mean are proposed, such as 2-tuple linguistic Heronian mean operator (2TLHM) and 2-tuple linguistic weighted Heronian mean operator (2TLWHB), and some desired properties of the proposed operators are studied. Then, a method based on the 2TLHM and 2TLWHB operators for multiple attribute group decision making is developed. In this approach, the interrelationships of attribute values are considered. Finally, an illustrative example is given to verify the developed approach and to demonstrate its practicality and effectiveness.

UNCERTAIN LINGUISTIC PRIORITIZED AGGREGATION OPERATORS AND THEIR APPLICATION TO MULTIPLE ATTRIBUTE GROUP DECISION MAKING

2013 ◽  
Vol 21 (04) ◽  
pp. 603-627 ◽  
Author(s):  
L. Y. ZHOU ◽  
R. LIN ◽  
X. F. ZHAO ◽  
G. W. WEI
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Aggregation Operators ◽  
Linguistic Information ◽  
Priority Level ◽  
Multiple Attribute ◽  
Prioritized Aggregation Operators ◽  
Magdm Problems

In this paper, we investigate the uncertain linguistic multiple attribute group decision making (MAGDM) problems in which the attributes and experts are in different priority level. Motivated by the idea of prioritized aggregation operators (R. R. Yager, Prioritized aggregation operators, Int. J. Approximate Reasoning48 (2008) 263–274.), we develop some prioritized aggregation operators for aggregating uncertain linguistic information, and then apply them to develops some models for uncertain linguistic multiple attribute group decision making (MAGDM) problems in which the attributes and experts are in different priority level. Finally, a practical example about talent introduction is given to verify the developed approach and to demonstrate its practicality and effectiveness.

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