TOPSIS method for intuitionistic fuzzy multiple-criteria decision making and its application to investment selection

Kybernetes ◽  
2016 ◽  
Vol 45 (2) ◽  
pp. 282-296 ◽  
Author(s):  
Shouzhen Zeng ◽  
Yao Xiao
Keyword(s):  
Decision Making ◽  
Decision Maker ◽  
Multiple Criteria Decision Making ◽  
Multiple Criteria ◽  
Weighted Averaging ◽  
Topsis Method ◽  
Content Type ◽  
Subjective Information ◽  
Intuitionistic Fuzzy ◽  
Wide Range

Purpose – The purpose of this paper is to present a hybrid intuitionistic fuzzy technique for order preference by similarity to ideal solution (TOPSIS) method, called intuitionistic fuzzy ordered weighted averaging weighted averaging (OWAWA) distance TOPSIS (IFOWAWAD-TOPSIS) method for intuitionistic fuzzy multiple-criteria decision making (MCDM) problems. Design/methodology/approach – Based on the OWAWA operator, the authors develop the intuitionistic fuzzy OWAWA distance measure, then the IFOWAWAD-TOPSIS method is obtained by using the IFOWAWAD and traditional TOPSIS. Findings – The developed IFOWAWAD-TOPSIS method can overcome the drawback of traditional TOPSIS method that cannot consider both the subjective information of attributes and the attitudinal character of decision maker. Research limitations/implications – Clearly, this paper is devoted to the OWA operator, MCDM and intuitionistic fuzzy theory. Practical implications – The developed method is applicable in a wide range of situations such as decision-making, statistics, engineering and economics. A numerical example concerning investment selection is given to illustrate the practicability and usefulness of the proposed approach. Originality/value – This paper fulfils an identified need to study how to make a decision considering both the subjective information of attribute and the attitudinal character of decision maker in intuitionistic fuzzy environment.

New Einstein Hybrid Aggregation Operators for Intuitionistic Fuzzy Sets and Applications in Multi-Criteria Decision-Making

2019 ◽  
pp. 252-283
Author(s):  
Bhagawati Prasad Joshi ◽  
Abhay Kumar
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Decision Maker ◽  
Intuitionistic Fuzzy Sets ◽  
Aggregation Operators ◽  
Weighted Averaging ◽  
Multi Criteria Decision Making ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Environment ◽  
Decision Making Processes

The fusion of multidimensional intuitionistic fuzzy information plays an important part in decision making processes under an intuitionistic fuzzy environment. In this chapter, it is observed that existing intuitionistic fuzzy Einstein hybrid aggregation operators do not follow the idempotency and boundedness. This leads to sometimes illogical and even absurd results to the decision maker. Hence, some new intuitionistic fuzzy Einstein hybrid aggregation operators such as the new intuitionistic fuzzy Einstein hybrid weighted averaging (IFEHWA) and the new intuitionistic fuzzy Einstein hybrid weighted geometric (IFEHWG) were developed. The new IFEHWA and IFEHWG operators can weigh the arguments as well as their ordered positions the same as the intuitionistic fuzzy Einstein hybrid aggregation operators do. Further, it is validated that the defined operators are idempotent, bounded, monotonic and commutative. Then, based on the developed approach, a multi-criteria decision-making (MCDM) procedure is given. Finally, a numerical example is conducted to demonstrate the proposed method effectively.

Generalized Archimedean Intuitionistic Fuzzy Averaging Aggregation Operators and their Application to Multicriteria Decision-Making

2016 ◽  
Vol 15 (02) ◽  
pp. 311-352 ◽  
Author(s):  
Chunqiao Tan ◽  
Xiaohong Chen
Keyword(s):  
Decision Making ◽  
Multiple Criteria Decision Making ◽  
Multicriteria Decision Making ◽  
Aggregation Operators ◽  
Weighted Averaging ◽  
Triangular Norm ◽  
Fuzzy Information ◽  
Intuitionistic Fuzzy ◽  
Averaging Operator ◽  
Fuzzy Aggregation

Aggregation operators play a key role in multiple criteria decision-making (MCDM). Extensions of aggregation operators to intuitionistic fuzzy sets (IFSs) usually involve replacing the standard arithmetic operations with those defined over the membership and nonmembership of IFS, which is essentially a pair of special Archimedean triangular norm (t-norm) and triangular conorm (t-conorm), called probabilistic sum t-conorm and product t-norm, on the membership and nonmembership of IFS, respectively. In this paper, we first introduce some operations on IFSs by means of Archimedean t-norm and t-conorm. Then some generalized Archimedean intuitionistic fuzzy aggregation operators are proposed, such as generalized Archimedean intuitionistic fuzzy weighted averaging operator, generalized Archimedean intuitionistic fuzzy ordered weighted averaging (GAIFOWA) operator, and generalized Archimedean intuitionistic fuzzy hybird averaging operator. Some desirable properties of these operators are investigated. The relations between these operators and the existing intuitionistic fuzzy aggregation operators are discussed. Finally, applying these proposed operators, we develop an approach for multi-criteria decision-making with intuitionistic fuzzy information, an illustrative example is used to verify the developed approach and to demonstrate its practicality and effectiveness.

scholarly journals INTUITIONISTIC FUZZY GENERALIZED PROBABILISTIC ORDERED WEIGHTED AVERAGING OPERATOR AND ITS APPLICATION TO GROUP DECISION MAKING

2015 ◽  
Vol 22 (2) ◽  
pp. 177-193 ◽  
Author(s):  
Shouzhen ZENG ◽  
Weihua SU ◽  
Chonghui ZHANG
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Weighted Averaging ◽  
Uncertain Environments ◽  
Intuitionistic Fuzzy Numbers ◽  
Ordered Weighted Averaging ◽  
Intuitionistic Fuzzy ◽  
Wide Range ◽  
Ordered Weighted Averaging Operator

In this paper, we present the intuitionistic fuzzy generalized probabilistic ordered weighted averaging (IFGPOWA) operator. It is a new aggregation operator that uses generalized means in a unified model between the probability and the OWA operator. The main advantage of this new operator is that it is able to deal with probabilities (objective information) and ordered weighted averages (subjective information) in the same formulation. Moreover, it is also able to deal with uncertain environments that can be assessed with intuitionistic fuzzy numbers. Furthermore, it uses generalized means providing a very general formulation that includes a wide range of situations. We study some of its main properties and particular cases such as the generalized intuitionistic fuzzy ordered weighted averaging (GIFOWA) operator and intuitionistic fuzzy probabilistic ordered weighted averaging (IFPOWA) operator. We end the paper by applying the new operator to a group decision making problem concerning the selection of investments.

Improving risk evaluation in FMEA with a hybrid multiple criteria decision making method

2015 ◽  
Vol 32 (7) ◽  
pp. 763-782 ◽  
Author(s):  
Hu-Chen Liu ◽  
Jian-Xin You ◽  
Xue-Feng Ding ◽  
Qiang Su
Keyword(s):  
Decision Making ◽  
Failure Modes ◽  
Multiple Criteria Decision Making ◽  
Multiple Criteria ◽  
Analytic Hierarchy ◽  
Effect Analysis ◽  
Maintenance Strategies ◽  
Content Type ◽  
Safety And Reliability ◽  
Hierarchy Process

Purpose – The purpose of this paper is to develop a new failure mode and effect analysis (FMEA) framework for evaluation, prioritization and improvement of failure modes. Design/methodology/approach – A hybrid multiple criteria decision-making method combining VIKOR, decision-making trial and evaluation laboratory (DEMATEL) and analytic hierarchy process (AHP) is used to rank the risk of the failure modes identified in FMEA. The modified VIKOR method is employed to determine the effects of failure modes on together. Then the DEMATEL technique is used to construct the influential relation map among the failure modes and causes of failures. Finally, the AHP approach based on the DEMATEL is utilized to obtain the influential weights and give the prioritization levels for the failure modes. Findings – A case study of diesel engine’s turbocharger system is provided to illustrate the potential application and benefits of the proposed FMEA approach. Results show that the new risk priority model can be effective in helping analysts find the high risky failure modes and create suitable maintenance strategies. Practical implications – The proposed FMEA can overcome the shortcomings and improve the effectiveness of the traditional FMEA. Particularly, the dependence and interactions between different failure modes and effects have been addressed by the new failure analysis method. Originality/value – This paper presents a systemic analytical model for FMEA. It is able to capture the complex interrelationships among various failure modes and effects and provide guidance to analysts by setting the suitable maintenance strategies to improve the safety and reliability of complex systems.

scholarly journals Fuzzy Linear Physical Programming for Multiple Criteria Decision-Making Under Uncertainty

2015 ◽  
Vol 11 (1) ◽  
pp. 26 ◽  
Author(s):  
Ahmed ElSayed ◽  
Elif Kongar ◽  
Surendra M. Gupta
Keyword(s):  
Decision Making ◽  
Problem Solving ◽  
Decision Maker ◽  
Multiple Criteria Decision Making ◽  
Multiple Criteria ◽  
Decision Making Under Uncertainty ◽  
Physical Programming ◽  
Fuzzy Environment ◽  
Linear Physical Programming ◽  
Proposed Model

<p>This paper presents a newly developed fuzzy linear physical programming (FLPP) model that allows the decision maker to introduce his/her preferences for multiple criteria decision making in a fuzzy environment. The major contribution of this research is to generalize the current models by accommodating an environment that is conducive to fuzzy problem solving. An example is used to evaluate, compare and discuss the results of the proposed model.</p>

Sign in / Sign up

Export Citation Format

Share Document