Multiple criteria decision making based on Hamy mean operators under the environment of spherical fuzzy sets

2021 ◽  
pp. 1-26
Author(s):  
Muhammad Sarwar Sindhu ◽  
Tabasam Rashid ◽  
Agha Kashif
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Closed Interval ◽  
Multiple Criteria Decision Making ◽  
Score Function ◽  
Vital Role ◽  
Multiple Criteria ◽  
Uncertain Information ◽  
Membership Degree ◽  
Picture Fuzzy Sets

Aggregation operators are widely applied to accumulate the vague and uncertain information in these days. Hamy mean (HM) operators play a vital role to accumulate the information. HM operators give us a more general and stretchy approach to develop the connections between the arguments. Spherical fuzzy sets (SpFSs), the further extension of picture fuzzy sets (PcFSs) that handle the data in which square sum of membership degree (MD), non-membership degree (NMD) and neutral degree (ND) always lie between closed interval [0, 1]. In the present article, we modify the HM operators like spherical fuzzy HM (SpFHM) operator and weighted spherical fuzzy HM (WSpFHM) operator to accumulate the spherical fuzzy (SpF) information. Moreover, various properties and some particular cases of SpFHM and the WSpFHM operators are discussed in details. Also, to compare the results obtained from the HM operators a score function is developed. Based on WSpFHM operator and score function, a model for multiple criteria decision-making (MCDM) is established to resolve the MCDM problem. To check the significance and robustness of the result, a comparative analysis and sensitivity analysis is also performed.

scholarly journals Rough q-Rung Orthopair Fuzzy Sets and Their Applications in Decision-Making

Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2010
Author(s):  
Muhammad Asim Bilal ◽  
Muhammad Shabir ◽  
Ahmad N. Al-Kenani
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Fuzzy Set ◽  
Multiple Criteria Decision Making ◽  
Score Function ◽  
Vital Role ◽  
Optimal Decision ◽  
Final Decision ◽  
Pythagorean Fuzzy Set ◽  
Accuracy Measure

Yager recently introduced the q-rung orthopair fuzzy set to accommodate uncertainty in decision-making problems. A binary relation over dual universes has a vital role in mathematics and information sciences. During this work, we defined upper approximations and lower approximations of q-rung orthopair fuzzy sets using crisp binary relations with regard to the aftersets and foresets. We used an accuracy measure of a q-rung orthopair fuzzy set to search out the accuracy of a q-rung orthopair fuzzy set, and we defined two types of q-rung orthopair fuzzy topologies induced by reflexive relations. The novel concept of a rough q-rung orthopair fuzzy set over dual universes is more flexible when debating the symmetry between two or more objects that are better than the prevailing notion of a rough Pythagorean fuzzy set, as well as rough intuitionistic fuzzy sets. Furthermore, using the score function of q-rung orthopair fuzzy sets, a practical approach was introduced to research the symmetry of the optimal decision and, therefore, the ranking of feasible alternatives. Multiple criteria decision making (MCDM) methods for q-rung orthopair fuzzy sets cannot solve problems when an individual is faced with the symmetry of a two-sided matching MCDM problem. This new approach solves the matter more accurately. The devised approach is new within the literature. In this method, the main focus is on ranking and selecting the alternative from a collection of feasible alternatives, reckoning for the symmetry of the two-sided matching of alternatives, and providing a solution based on the ranking of alternatives for an issue containing conflicting criteria, to assist the decision-maker in a final decision.

scholarly journals Multiple criteria decision making based on bipolar picture fuzzy sets and extended TOPSIS

2020 ◽  
Vol 23 (01) ◽  
pp. 49-57
Author(s):  
Muhammad Sarwar Sindhu ◽  
Muhammad Ahsan ◽  
Arif Rafiq ◽  
Imran Ameen Khan
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Multiple Criteria Decision Making ◽  
Multiple Criteria ◽  
Picture Fuzzy Sets

A Novel spherical fuzzy CRITIC method and its application to prioritization of supplier selection criteria

2021 ◽  
pp. 1-8
Author(s):  
Cengiz Kahraman ◽  
Sezi Cevik Onar ◽  
Başar Öztayşi
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Supplier Selection ◽  
Selection Criteria ◽  
Business Processes ◽  
Multiple Criteria Decision Making ◽  
Decision Matrix ◽  
Neutrosophic Sets ◽  
Numerical Illustration ◽  
Picture Fuzzy Sets

Linguistic terms are quite suitable to make evaluations in multiple criteria decision making problems since humans prefer them rather than sharp evaluations. When linguistic evaluations are used in the decision matrix instead of exact numerical values, fuzzy set theory can capture the vagueness in the linguistic evaluations. Ordinary fuzzy sets have been extended to many new types of fuzzy sets such as intuitionistic fuzzy sets, neutrosophic sets, spherical fuzzy sets and picture fuzzy sets. Spherical fuzzy sets are an extension of picture fuzzy sets whose squared sum of their parameters is at most equal to one. This paper develops a novel spherical fuzzy CRiteria Importance Through Intercriteria Correlation (CRITIC) method and applies it for prioritizing supplier selection criteria. Supplier selection is one of the most critical aspects of any organization since any mistake in this process may cause poor supplier performance and inefficiencies in the business processes. Supplier selection is a multi-criteria decision making problem involving several conflicting criteria and alternatives. A numerical illustration of the proposed method is also given for this problem.

scholarly journals Complex q-Rung Orthopair Fuzzy Aggregation Operators and Their Applications in Multi-Attribute Group Decision Making

Information ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 5 ◽  
Author(s):  
Liu ◽  
Mahmood ◽  
Ali
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Imaginary Part ◽  
Group Decision Making ◽  
Group Decision ◽  
Aggregation Operators ◽  
Uncertain Information ◽  
Membership Degree ◽  
The Real ◽  
Complex Valued

In this manuscript, the notions of q-rung orthopair fuzzy sets (q-ROFSs) and complex fuzzy sets (CFSs) are combined is to propose the complex q-rung orthopair fuzzy sets (Cq-ROFSs) and their fundamental laws. The Cq-ROFSs are an important way to express uncertain information, and they are superior to the complex intuitionistic fuzzy sets and the complex Pythagorean fuzzy sets. Their eminent characteristic is that the sum of the qth power of the real part (similarly for imaginary part) of complex-valued membership degree and the qth power of the real part (similarly for imaginary part) of complex-valued non‐membership degree is equal to or less than 1, so the space of uncertain information they can describe is broader. Under these environments, we develop the score function, accuracy function and comparison method for two Cq-ROFNs. Based on Cq-ROFSs, some new aggregation operators are called complex q-rung orthopair fuzzy weighted averaging (Cq-ROFWA) and complex q-rung orthopair fuzzy weighted geometric (Cq-ROFWG) operators are investigated, and their properties are described. Further, based on proposed operators, we present a new method to deal with the multi‐attribute group decision making (MAGDM) problems under the environment of fuzzy set theory. Finally, we use some practical examples to illustrate the validity and superiority of the proposed method by comparing with other existing methods.

scholarly journals An Extension of the MULTIMOORA Method for Multiple Criteria Group Decision Making Based upon Hesitant Fuzzy Sets

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Zhi-Hui Li
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Distance Measure ◽  
Group Decision ◽  
Multiple Criteria Decision Making ◽  
Score Function ◽  
Multiple Criteria ◽  
Hesitant Fuzzy Set ◽  
Multiple Objective Optimization ◽  
Multimoora Method

In order to determine the membership of an element to a set owing to ambiguity between a few different values, the hesitant fuzzy set (HFS) has been proposed and widely diffused to deal with vagueness and uncertainty involved in the process of multiple criteria group decision making (MCGDM) problems. In this paper, we develop novel definitions of score function and distance measure for HFSs. Some examples are given to illustrate that the proposed definitions are more reasonable than the traditional ones. Furthermore, our study extends the MULTIMOORA (Multiple Objective Optimization on the basis of Ratio Analysis plus Full Multiplicative Form) method with HFSs. The proposed method thus provides the means for multiple criteria decision making (MCDM) regarding uncertain assessments. Utilization of hesitant fuzzy power aggregation operators also enables facilitating the process of MCGDM. A numerical example of software selection demonstrates the possibilities of application of the proposed method.

scholarly journals Multiple Criteria Decision Making Based on Probabilistic Interval-Valued Hesitant Fuzzy Sets by Using LP Methodology

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
M. Sarwar Sindhu ◽  
Tabasam Rashid ◽  
Agha Kashif ◽  
Juan Luis García Guirao
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Real Life ◽  
Multiple Criteria Decision Making ◽  
Multiple Criteria ◽  
Decision Makers ◽  
Occurrence Probability ◽  
Hesitant Fuzzy Sets ◽  
Life Problems ◽  
Interval Valued

Probabilistic interval-valued hesitant fuzzy sets (PIVHFSs) are an extension of interval-valued hesitant fuzzy sets (IVHFSs) in which each hesitant interval value is considered along with its occurrence probability. These assigned probabilities give more details about the level of agreeness or disagreeness. PIVHFSs describe the belonging degrees in the form of interval along with probabilities and thereby provide more information and can help the decision makers (DMs) to obtain precise, rational, and consistent decision consequences than IVHFSs, as the correspondence of unpredictability and inaccuracy broadly presents in real life problems due to which experts are confused to assign the weights to the criteria. In order to cope with this problem, we construct the linear programming (LP) methodology to find the exact values of the weights for the criteria. Furthermore these weights are employed in the aggregation operators of PIVHFSs recently developed. Finally, the LP methodology and the actions are then applied on a certain multiple criteria decision making (MCDM) problem and a comparative analysis is given at the end.

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