Approaches to multi-attribute decision making with risk preference under extended Pythagorean fuzzy environment

2019 ◽  
Vol 36 (1) ◽  
pp. 325-335 ◽  
Author(s):  
Aurang Zeb ◽  
Muhammad Sajjad Ali Khan ◽  
Muhammad Ibrar
Keyword(s):  
Decision Making ◽  
Risk Preference ◽  
Fuzzy Environment ◽  
Multi Attribute Decision Making

scholarly journals A Novel Decision-Making Approach under Complex Pythagorean Fuzzy Environment

2019 ◽  
Vol 24 (3) ◽  
pp. 73 ◽  
Author(s):  
Muhammad Akram ◽  
Sumera Naz
Keyword(s):  
Decision Making ◽  
Fuzzy Set ◽  
Research Study ◽  
Regular Graphs ◽  
Pythagorean Fuzzy Set ◽  
Improvement Project ◽  
Fuzzy Environment ◽  
Fuzzy Graphs ◽  
Technology Improvement ◽  
Multi Attribute Decision Making

A complex Pythagorean fuzzy set (CPFS) is an extension of a Pythagorean fuzzy set that is used to handle the vagueness with the degrees whose ranges are enlarged from real to complex subset with unit disc. In this research study, we propose the innovative concept of complex Pythagorean fuzzy graphs (CPFGs). Further, we present the concepts of regular and edge regular graphs in a complex Pythagorean fuzzy environment. Moreover, we develop a complex Pythagorean fuzzy graph based multi-attribute decision making an approach to handling the situations in which the graphic structure of attributes is obscure. A numerical example concerning information technology improvement project selection is utilized to illustrate the availability of the developed approach.

scholarly journals A Novel q-Rung Dual Hesitant Fuzzy Multi-Attribute Decision-Making Method Based on Entropy Weights

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1322
Author(s):  
Yaqing Kou ◽  
Xue Feng ◽  
Jun Wang
Keyword(s):  
Decision Making ◽  
Multiple Attribute Decision Making ◽  
Aggregation Operators ◽  
Entropy Measure ◽  
Fuzzy Environment ◽  
Multiple Attribute ◽  
Special Cases ◽  
Multi Attribute Decision Making ◽  
Novel Method ◽  
And Performance

In this paper, a new multiple attribute decision-making (MADM) method under q-rung dual hesitant fuzzy environment from the perspective of aggregation operators is proposed. First, some aggregation operators are proposed for fusing q-rung dual hesitant fuzzy sets (q-RDHFSs). Afterwards, we present properties and some desirable special cases of the new operators. Second, a new entropy measure for q-RDHFSs is developed, which defines a method to calculate the weight information of aggregated q-rung dual hesitant fuzzy elements. Third, a novel MADM method is introduced to deal with decision-making problems under q-RDHFSs environment, wherein weight information is completely unknown. Finally, we present numerical example to show the effectiveness and performance of the new method. Additionally, comparative analysis is conducted to prove the superiorities of our new MADM method. This study mainly contributes to a novel method, which can help decision makes select optimal alternatives when dealing with practical MADM problems.

CNC router selection under dynamic fuzzy environment

2020 ◽  
Vol 39 (5) ◽  
pp. 6259-6269
Author(s):  
Ahmet Aktas ◽  
Serhat Aydin ◽  
Mehmet Kabak
Keyword(s):  
Decision Making ◽  
Manufacturing Systems ◽  
Selection Procedure ◽  
Time Dynamic ◽  
Hard Materials ◽  
Fuzzy Environment ◽  
Multi Attribute Decision Making ◽  
Cnc Router ◽  
Value Changes

To cut several types of hard materials in manufacturing, Computer Numeric Control (CNC) router machines are commonly used. The tasks to be done by different machines can be performed by a single CNC router machine. Production of parts with better quality is possible at lower costs by production with CNC router machines, and these machines improve the productivity of manufacturing system. For these reasons, determination of the appropriate CNC router machine for manufacturing systems is a crucial decision. Different factors related to properties of machines are effective on the decision. Therefore, decision makers must include different effective aspects into decision process. Under this consideration, an analytic selection procedure for CNC router machines by taking uncertain expressions of experts on the selection criteria and variable values occur over time is proposed in this study. In order to handle the modelling difficulty of uncertainty of the statements and the value changes by time, dynamic intuitionistic multi attribute decision making is used to select the best CNC router. Applicability of the proposed selection procedure is demonstrated on an application, and a comparative analysis with dynamic neutrosophic multi attribute decision making is presented.

A Preference Structure in Multi-attribute Decision Making – an Algorithmic Approach Based on Hesitant Fuzzy Sets

2021 ◽  
Author(s):  
Bhaba Krishna Mohanty ◽  
Eshika Aggarwal
Keyword(s):  
Decision Making ◽  
Sensitivity Analysis ◽  
Pairwise Comparison ◽  
Preference Structure ◽  
Hesitant Fuzzy Sets ◽  
Pairwise Comparison Matrix ◽  
Algorithmic Approach ◽  
Fuzzy Environment ◽  
Eigen Value ◽  
Multi Attribute Decision Making

Abstract This paper introduces a new methodology for solving Multi-Attribute Decision Making (MADM) problems under hesitant fuzzy environment. The uncertainty in Hesitant Fuzzy Elements (HFE) are derived by means of entropy. The resulting uncertainty is subsequently used in HFE to derive a single representative value (RV) of alternatives in each attribute. Our work transforms the RVs into their linguistic counterparts and then formulates a methodology for pairwise comparison of the alternatives via their linguistically defines RVs. The Eigen vector corresponding to maximum Eigen value of the pairwise comparison matrix prioritize the alternatives in each attribute. The priority vectors of the alternatives are aggregated to derive the weights of the attributes using Quadratic programming. The weighted aggregation of the attribute values provides the ranking of the alternatives in MADM. An algorithm is written to validate the procedure developed. The proposed methodology is compared with similar existing methods and the advantages of our method are presented. The robustness of our methodology is demonstrated through sensitivity analysis. To highlight the procedure a car purchasing problem is illustrated.

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