Approach to multiple attribute decision making based on the Hamacher operation with fuzzy number intuitionistic fuzzy information and their application

2014 ◽  
Vol 27 (3) ◽  
pp. 1087-1094 ◽  
Author(s):  
Shenghan Zhou ◽  
Wenbing Chang
Keyword(s):  
Decision Making ◽  
Fuzzy Number ◽  
Multiple Attribute Decision Making ◽  
Fuzzy Information ◽  
Intuitionistic Fuzzy ◽  
Multiple Attribute

scholarly journals AN APPROACH TO MULTIPLE ATTRIBUTE DECISION MAKING BASED ON THE INDUCED CHOQUET INTEGRAL WITH FUZZY NUMBER INTUITIONISTIC FUZZY INFORMATION

2014 ◽  
Vol 15 (2) ◽  
pp. 277-298 ◽  
Author(s):  
Guiwu Wei ◽  
Rui Lin ◽  
Xiaofei Zhao ◽  
Hongjun Wang
Keyword(s):  
Decision Making ◽  
Fuzzy Number ◽  
Choquet Integral ◽  
Score Function ◽  
Multiple Attribute Decision Making ◽  
Aggregation Operators ◽  
Fuzzy Information ◽  
Intuitionistic Fuzzy ◽  
Operational Laws ◽  
Multiple Attribute

In this paper, we investigate the multiple attribute decision making problems with fuzzy number intuitionistic fuzzy information. Firstly, some operational laws of fuzzy number intuitionistic fuzzy values, score function and accuracy function of fuzzy number intuitionistic fuzzy values are introduced. Then, we have developed two fuzzy number intuitionistic fuzzy Choquet integral aggregation operators: induced fuzzy number intuitionistic fuzzy choquet ordered averaging (IFNIFCOA) operator and induced fuzzy number intuitionistic fuzzy choquet ordered geometric (IFNIFCOG) operator. The prominent characteristic of the operators is that they can not only consider the importance of the elements or their ordered positions, but also reflect the correlation among the elements or their ordered positions. We have studied some desirable properties of the IFNIFCOA and IFNIFCOG operators, such as commutativity, idempotency and monotonicity, and applied the IFNIFCOA and IFNIFCOGM operators to multiple attribute decision making with fuzzy number intuitionistic fuzzy information. Finally an illustrative example has been given to show the developed method.

scholarly journals Generalized Linguistic Hesitant Intuitionistic Fuzzy Hybrid Aggregation Operators

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Wei Yang ◽  
Jiarong Shi ◽  
Yongfeng Pang
Keyword(s):  
Decision Making ◽  
Geometric Mean ◽  
Multiple Attribute Decision Making ◽  
Aggregation Operators ◽  
New Method ◽  
Weighted Averaging ◽  
Fuzzy Information ◽  
Intuitionistic Fuzzy ◽  
Multiple Attribute ◽  
Special Cases

Some hybrid aggregation operators have been developed based on linguistic hesitant intuitionistic fuzzy information. The generalized linguistic hesitant intuitionistic fuzzy hybrid weighted averaging (GLHIFHWA) operator and the generalized linguistic hesitant intuitionistic fuzzy hybrid geometric mean (GLHIFHGM) operator are defined. Some special cases of the new aggregation operators are studied and many existing aggregation operators are special cases of the new operators. A new multiple attribute decision making method based on the new aggregation operators is proposed and a practical numerical example is presented to illustrate the feasibility and practical advantages of the new method.

Triangular Intuitionistic Fuzzy Multiple Attribute Decision Making and Their Applications to Risk Evaluation for Risk Investment Projects

2016 ◽  
Vol 13 (10) ◽  
pp. 7120-7124
Author(s):  
Hong Jin
Keyword(s):  
Decision Making ◽  
Risk Evaluation ◽  
Multiple Attribute Decision Making ◽  
Fuzzy Information ◽  
Investment Projects ◽  
Intuitionistic Fuzzy ◽  
Multiple Attribute ◽  
Risk Investment

In this paper, we investigate the multiple attribute decision making problems about risk evaluation for risk investment projects with triangular intuitionistic fuzzy information. Then, we proposed the triangular intuitionistic fuzzy Einstein weighted geometric (TIFEWG) operator, triangular intuitionistic fuzzy Einstein ordered weighted geometric (TIFEOWG) operator and triangular intuitionistic fuzzy Einstein hybrid geometric (TIFEHG) operator and we develop an approach to multiple attribute decision making with triangular intuitionistic fuzzy information. Finally, an illustrative example for evaluating the risk of the risk investment projects with triangular intuitionistic fuzzy information is given to verify the developed approach.

An Approach to Multiple Attribute Decision Making with Triangular Intuitionistic Fuzzy Information

2016 ◽  
Vol 13 (10) ◽  
pp. 7285-7288
Author(s):  
Jinping Chen
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Construction Projects ◽  
Multiple Attribute Decision Making ◽  
Intuitionistic Fuzzy Sets ◽  
Weighted Averaging ◽  
Fuzzy Information ◽  
Intuitionistic Fuzzy ◽  
Operational Laws ◽  
Multiple Attribute

The aim of this paper is to investigate the multiple attribute decision making problems with triangular intuitionistic fuzzy information. Some operational laws of triangular intuitionistic fuzzy sets, score functions of triangular intuitionistic fuzzy sets are introduced. Based on these operational laws, some Einstein aggregation operators, including triangular intuitionistic fuzzy Einstein weighted averaging (TIFEWA) operator, triangular intuitionistic fuzzy Einstein ordered weighted averaging (TIFEOWA) operator and triangular intuitionistic fuzzy Einstein hybrid aggregation (TIFEHA) operator, are proposed. An approach to multiple attribute decision making with triangular intuitionistic fuzzy information is developed based on the TIFEWA operator. Finally, an illustrative example for evaluating the construction projects quality with triangular intuitionistic fuzzy information is given to verify the developed approach.

scholarly journals An Approach to Evaluating Enterprise Financial Performance Based on the Control Inheritance of Family Businesses with Intuitionistic Fuzzy Information

2014 ◽  
Vol 2014 ◽  
pp. 1-4
Author(s):  
Hui Liu
Keyword(s):  
Decision Making ◽  
Financial Performance ◽  
Multiple Attribute Decision Making ◽  
Family Businesses ◽  
Fuzzy Information ◽  
Bonferroni Mean ◽  
Intuitionistic Fuzzy ◽  
Multiple Attribute

We investigate the multiple attribute decision making problems for evaluating the enterprise financial performance based on the control inheritance of family businesses with intuitionistic fuzzy information. We utilize the intuitionistic fuzzy Bonferroni mean (IFBM) operator to aggregate the intuitionistic fuzzy information corresponding to each alternative and get the overall value of enterprise financial performance and then rank the enterprises and select the most desirable one(s) by using the overall value of enterprise financial performance. Finally, an illustrative example for evaluating the enterprise financial performance based on the control inheritance of family businesses is given to verify the developed approach and to demonstrate its practicality and effectiveness.

scholarly journals Multiple-Attribute Decision-Making Problem Using TOPSIS and Choquet Integral with Hesitant Fuzzy Number Information

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Harish Garg ◽  
Abazar Keikha ◽  
Hassan Mishmast Nehi
Keyword(s):  
Decision Making ◽  
Fuzzy Number ◽  
Choquet Integral ◽  
Multiple Attribute Decision Making ◽  
Hesitant Fuzzy Set ◽  
Fuzzy Information ◽  
Multiple Attribute ◽  
Decision Making Problem ◽  
Collection Data ◽  
Topsis Technique

The paper aims are to present a method to solve the multiple-attribute decision-making (MADM) problems under the hesitant fuzzy set environment. In MADM problems, the information collection, aggregation, and the measure phases are crucial to direct the problem. However, to handle the uncertainties in the collection data, a hesitant fuzzy number is one of the most prominent ways to express uncertain and vague information in terms of different discrete numbers rather than a single crisp number. Additionally, to aggregate and to rank the collective numbers, a TOPSIS (“Technique for Order of Preference by Similarity to Ideal Solution”) and the Choquet integral (CI) are the useful tools. Keeping all these features, in the present paper, we combine the TOPSIS and CI methods for hesitant fuzzy information and hence present a method named as TOPSIS-CI to address the MADM problems. The presented method has been described with a numerical example. Finally, the validity of the stated method as well as a comparative analysis with the existing methods is addressed in detail.

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