scholarly journals Decision-Making Analysis Based on Hesitant Fuzzy N-Soft ELECTRE-I Approach

2021 ◽  
Author(s):  
Arooj Adeel ◽  
Muhammad Akram ◽  
Naim Cagman
Keyword(s):  
Decision Making ◽  
Set Theory ◽  
Fuzzy Set Theory ◽  
Uncertain Data ◽  
Hesitant Fuzzy Set ◽  
Topsis Method ◽  
Soft Sets ◽  
Research Article ◽  
Electre I ◽  
Multi Attribute Decision Making

Abstract For the formal expression of uncertain data, hesitant fuzzy set theory has established itself as a distinguished model because it has a broad use in multi-attribute decision-making problems. With the incorporation of features from N -soft sets, a useful framework referred to as hesitant fuzzy N -soft sets has acquired an even greater appeal. This model integrates and associates the hesitant environment with information regarding the existence of grades or star ratings. In this research article, we introduce a multi-attribute decision-making technique known as hesitant fuzzy N -soft ELECTRE-I, which computes the decision-maker assessments in an adjustable and formative manner. The proposed method also improves the robustness and accuracy of the decisions relying on grades or star ratings. Thus it lays a bedrock for subsequent analyses and applications. We justify the relevance and convenience of the proposed technique by testing it in actually existing scenarios. Finally, we give a comparison of this novel methodology with the HFNS-TOPSIS method.

A Novel Hybrid Clustering Method for Multi-Attribute Decision-Making Datasets

2012 ◽  
Vol 482-484 ◽  
pp. 103-108
Author(s):  
Kai Ping Liu ◽  
Wen Chin Chen ◽  
Ting Cheng Chang
Keyword(s):  
Decision Making ◽  
Set Theory ◽  
Rough Set ◽  
Fuzzy Set Theory ◽  
Uncertain Data ◽  
Clustering Method ◽  
Hybrid Scheme ◽  
Index Function ◽  
Single Attribute ◽  
Multi Attribute Decision Making

A function is proposed for descritizing and classifying the uncertain data of multi-attribute decision-making (MADM) datasets using a hybrid scheme incorporating fuzzy set theory, Rough Set (RS) theory and a modified form of the PBMF index function. The proposed MADM index function is used to extend the applicability of the single-attribute decision-making (SADM) function. The validity of the proposed MADM index function is evaluated by comparing the descritizing results obtained for a simple hypothetical function with those obtained using a SADM function and the conventional PBMF function.

scholarly journals Decision-Making Approach with Fuzzy Type-2 Soft Graphs

2020 ◽  
Vol 2020 ◽  
pp. 1-25
Author(s):  
Sundas Shahzadi ◽  
Musavarah Sarwar ◽  
Muhammad Akram
Keyword(s):  
Decision Making ◽  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Mathematical Structure ◽  
Soft Set ◽  
Soft Sets ◽  
Research Article ◽  
Primary Relationship

Molodtsov’s theory of soft sets is free from the parameterizations insufficiency of fuzzy set theory. Type-2 soft set as an extension of a soft set has an essential mathematical structure to deal with parametrizations and their primary relationship. Fuzzy type-2 soft models play a key role to study the partial membership and uncertainty of objects along with underlying and primary set of parameters. In this research article, we introduce the concept of fuzzy type-2 soft set by integrating fuzzy set theory and type-2 soft set theory. We also introduce the notions of fuzzy type-2 soft graphs, regular fuzzy type-2 soft graphs, irregular fuzzy type-2 soft graphs, fuzzy type-2 soft trees, and fuzzy type-2 soft cycles. We construct some operations such as union, intersection, AND, and OR on fuzzy type-2 soft graphs and discuss these concepts with numerical examples. The fuzzy type-2 soft graph is an efficient model for dealing with uncertainty occurring in vertex-neighbors structure and is applicable in computational analysis, applied intelligence, and decision-making problems. We study the importance of fuzzy type-2 soft graphs in chemical digestion and national engineering services.

scholarly journals Belief and Possibility Belief Interval-Valued N-Soft Set and Their Applications in Multi-Attribute Decision-Making Problems

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1498
Author(s):  
Shahbaz Ali ◽  
Muneeba Kousar ◽  
Qin Xin ◽  
Dragan Pamučar ◽  
Muhammad Shazib Hameed ◽  
...  
Keyword(s):  
Decision Making ◽  
Set Theory ◽  
Soft Set ◽  
Mathematical Tool ◽  
Soft Sets ◽  
Fundamental Properties ◽  
Research Article ◽  
Multi Attribute Decision Making ◽  
Algebraic Operations ◽  
Interval Valued

In this research article, we motivate and introduce the concept of possibility belief interval-valued N-soft sets. It has a great significance for enhancing the performance of decision-making procedures in many theories of uncertainty. The N-soft set theory is arising as an effective mathematical tool for dealing with precision and uncertainties more than the soft set theory. In this regard, we extend the concept of belief interval-valued soft set to possibility belief interval-valued N-soft set (by accumulating possibility and belief interval with N-soft set), and we also explain its practical calculations. To this objective, we defined related theoretical notions, for example, belief interval-valued N-soft set, possibility belief interval-valued N-soft set, their algebraic operations, and examined some of their fundamental properties. Furthermore, we developed two algorithms by using max-AND and min-OR operations of possibility belief interval-valued N-soft set for decision-making problems and also justify its applicability with numerical examples.

scholarly journals Decision-Making Approach under Pythagorean Fuzzy Yager Weighted Operators

Mathematics ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 70 ◽  
Author(s):  
Gulfam Shahzadi ◽  
Muhammad Akram ◽  
Ahmad N. Al-Kenani
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Aggregation Operators ◽  
Weighted Averaging ◽  
Ordered Weighted Averaging ◽  
Multi Attribute Decision Making ◽  
Parametric Families

In fuzzy set theory, t-norms and t-conorms are fundamental binary operators. Yager proposed respective parametric families of both t-norms and t-conorms. In this paper, we apply these operators for the analysis of Pythagorean fuzzy sets. For this purpose, we introduce six families of aggregation operators named Pythagorean fuzzy Yager weighted averaging aggregation, Pythagorean fuzzy Yager ordered weighted averaging aggregation, Pythagorean fuzzy Yager hybrid weighted averaging aggregation, Pythagorean fuzzy Yager weighted geometric aggregation, Pythagorean fuzzy Yager ordered weighted geometric aggregation and Pythagorean fuzzy Yager hybrid weighted geometric aggregation. These tools inherit the operational advantages of the Yager parametric families. They enable us to study two multi-attribute decision-making problems. Ultimately we can choose the best option by comparison of the aggregate outputs through score values. We show this procedure with two practical fully developed examples.

Multi-Attribute Decision Making Using Interval Type-2 Fuzzy AHP and Its Application on Borsa Istanbul (BIST)

2021 ◽  
pp. 30-56
Author(s):  
Beyza Ahlatcioglu Ozkok ◽  
Hale Gonce Kocken
Keyword(s):  
Decision Making ◽  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Fuzzy Ahp ◽  
Short Review ◽  
Analytic Hierarchy ◽  
Multi Attribute Decision Making ◽  
Interval Type

Analytic hierarchy process (AHP) is a widely used multi-attribute decision-making (MADM) approach. Due to the complexity and uncertainty involved in real world problems, decision makers might be prefer to make fuzzy judgments instead of crisp ones. Furthermore, even when people use the same words, individual judgments of events are invariably subjective, and the interpretations that they attach to the same words may differ. This is why fuzzy numbers has been introduced to characterize linguistic variables. Fuzzy AHP methods have recently been extended by using type-2 fuzzy sets. Type-2 fuzzy set theory incorporates the uncertainty of membership functions into the fuzzy set theory. In this chapter, the authors firstly provide a short review on applications of interval type-2 fuzzy AHP on MADM problems. Then, they present a very efficient MADM technique, interval type-2 fuzzy AHP, to solve the portfolio selection problem that is to decide which stocks are to be chosen for investment and in what proportions they will be bought. And finally, they provided a case study on BIST.

A sequential three-way decision model based on hesitant fuzzy sets

2021 ◽  
pp. 1-12
Author(s):  
Mo Zhang ◽  
Qinghua Zhang ◽  
Man Gao
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Set Theory ◽  
Decision Model ◽  
Hesitant Fuzzy Set ◽  
Extended Model ◽  
Uncertain Information ◽  
Fuzzy Distance ◽  
Shadowed Set ◽  
Multi Attribute Decision Making

As a new extended model of fuzzy sets, hesitant fuzzy set theory is a useful tool to process uncertain information in decision making problems. The traditional hesitant fuzzy multi-attribute decision making (MADM) can only choose an optimal strategy, which is not suitable for all of the complex scenarios. Typically, in practical application, decision making problems may be more complicated involving three options of acceptance, non-commitment and rejection decisions. Three-way decisions, which divide universe into three disjoint regions by a pair of thresholds, are more efficient to deal with these problems. Therefore, how to utilize three-way decision theory to process hesitant fuzzy information is an essential issue to be studied. In this paper, from the perspective of hesitant fuzzy distance, a hesitant fuzzy three-way decision model is proposed. First, because hesitant fuzzy element (HFE) is a set of several possible membership degrees, it cannot be compared with thresholds directly. Hence, this paper converts it into the comparison between the distance and the thresholds. Then, to calculate thresholds more reasonably, shadowed set theory is introduced to avoid the subjectivity of threshold acquisition. Furthermore, sequential strategy is adopted to solve the multi-attribute decision making problems. Finally, an example of medical diagnosis and simulation experiments are given to prove the accuracy and efficiency of the proposed hesitant fuzzy three-way decision model.

Research on Interval-valued Intuitionistic Fuzzy Multi-attribute Decision Making Based on Projection Model

2019 ◽  
Vol 13 ◽  
Author(s):  
Sha Fu ◽  
Xi-long Qu ◽  
Ye-zhi Xiao ◽  
Hang-jun Zhou ◽  
Yun Zhou
Keyword(s):  
Decision Making ◽  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Ideal Point ◽  
Projection Model ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Ideal ◽  
Multi Attribute Decision Making ◽  
Interval Valued

Background: Regarding the multi-attribute decision making where the decision information is the interval-valued intuitionistic fuzzy number and the attribute weight information is not completely determined. Method: Intuitionistic fuzzy set theory introduces non-membership function, as an extension of the fuzzy set theory, it has certain advantages in solving complex decision making problems. a projection model based interval-valued intuitionistic fuzzy multi-attribute decision making scheme was proposed in this study. The objective weight of the attribute was obtained using improved interval-valued intuitionistic fuzzy entropy, and thus the comprehensive weight of the attribute was obtained according to the preference information. Results: In the aspect of the decision-making matrix processing, the concept of interval-valued intuitionistic fuzzy ideal point and its related concepts were defined, the score vector of each scheme was calculated, the projection model was constructed to measure the similarity between each scheme and the interval-valued intuitionistic fuzzy ideal point, and the scheme was sorted according to the projection value. Conclusion: The efficiency and usability of the proposed approach are considered on the case study.

Application of Soft Set in Game Theory

2019 ◽  
pp. 421-435 ◽  
Author(s):  
B. K. Tripathy ◽  
Sooraj T. R. ◽  
Radhakrishna N. Mohanty
Keyword(s):  
Game Theory ◽  
Decision Making ◽  
Fuzzy Sets ◽  
Set Theory ◽  
Fuzzy Set Theory ◽  
Choice Function ◽  
Soft Set ◽  
Mathematical Tool ◽  
Soft Sets ◽  
The Game Theory

In recent years, most of the applications in game theory have been developed based on the theory of fuzzy sets. But the inadequacy of the parameterization tool in fuzzy set theory leads to difficulties for decision making in the game theory. Soft sets were introduced by Molodtsov to overcome this problem in fuzzy sets and it was illustrated by him. Choice functions play an important role in game theory. Soft set theory gives an opportunity to construct new mathematical tool which keeps all good sides of choice function and eliminates its drawbacks. Also, decision making is an integral part of games and many researchers have applied soft set theory in decision making. In this chapter, the authors describe all these and propose some important improvements leading to better deals in game environments.

scholarly journals Multi-Attribute Multi-Perception Decision-Making Based on Generalized T-Spherical Fuzzy Weighted Aggregation Operators on Neutrosophic Sets

Mathematics ◽  
2019 ◽  
Vol 7 (9) ◽  
pp. 780 ◽  
Author(s):  
Quek ◽  
Selvachandran ◽  
Munir ◽  
Mahmood ◽  
Ullah ◽  
...  
Keyword(s):  
Decision Making ◽  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Aggregation Operators ◽  
Neutrosophic Sets ◽  
Operational Laws ◽  
Fuzzy Aggregation ◽  
Multi Attribute Decision Making ◽  
Do So

The framework of the T-spherical fuzzy set is a recent development in fuzzy set theory that can describe imprecise events using four types of membership grades with no restrictions. The purpose of this manuscript is to point out the limitations of the existing intuitionistic fuzzy Einstein averaging and geometric operators and to develop some improved Einstein aggregation operators. To do so, first some new operational laws were developed for T-spherical fuzzy sets and their properties were investigated. Based on these new operations, two types of Einstein aggregation operators are proposed namely the Einstein interactive averaging aggregation operators and the Einstein interactive geometric aggregation operators. The properties of the newly developed aggregation operators were then investigated and verified. The T-spherical fuzzy aggregation operators were then applied to a multi-attribute decision making (MADM) problem related to the degree of pollution of five major cities in China. Actual datasets sourced from the UCI Machine Learning Repository were used for this purpose. A detailed study was done to determine the most and least polluted city for different perceptions for different situations. Several compliance tests were then outlined to test and verify the accuracy of the results obtained via our proposed decision-making algorithm. It was proved that the results obtained via our proposed decision-making algorithm was fully compliant with all the tests that were outlined, thereby confirming the accuracy of the results obtained via our proposed method.

Sign in / Sign up

Export Citation Format

Share Document