Power Aggregation Operators Research Articles

Classification of renewable energy and its sources with decision-making approach based on bipolar complex fuzzy frank power aggregation operators

The sources for renewable energy are those that can never be depleted as these sources are given by nature. For instance, biomass, solar, wind, hydro, etc. They don't damage the climate as they generate practically zero pollution. The utilization of energy is expanding because of the development of the total populace. In this mechanical age where every one of the gadgets works by employing energy, the new mankind challenge is giving adequate measures of energy. Hence the basic theme of this article is to study the energy crisis and combat global warming through renewable energy and investigate the most superb source of renewable energy under the setting of bipolar complex fuzzy (BCF) Frank power aggregation operators (AOs). That's why, we deduce bipolar complex fuzzy Frank power averaging (BCFFPA), bipolar complex fuzzy Frank power weighted averaging (BCFFPWA), bipolar complex fuzzy Frank power ordered weighted averaging (BCFFPOWA) bipolar complex fuzzy Frank power geometric (BCFFPG), bipolar complex fuzzy Frank power weighted geometric (BCFFPWG), and bipolar complex fuzzy Frank power ordered weighted geometric (BCFFPOWG) operators. Furthermore, we deduce an approach to decision-making (DM) based on the deduced operators. The proposed approach of DM is a powerful technique for DM in circumstances, where the information is two dimensional along with positive and negative aspects of the element or object. It can provide a more accurate and nuanced view of the DM approach, which can lead to better decisions and better results. Afterward, we investigate the sources of renewable energy and discover the better and finest source of renewable energy by considering the artificial data in the structure of BCFS and utilizing the proposed approach of DM. To reveal further advantages and domination of the deduced operators and approach of DM, we compare the deduced operators and DM approach with a few prevailing theories.

Multi-attribute group decision-making based on q-rung orthopair fuzzy Aczel–Alsina power aggregation operators

This article aims to develop the Aczel–Alsina operational laws based on power aggregation operators (PAOs) for the q-Rung orthopair fuzzy set (FS) (q-ROFS) framework. Changing the parameter can alter these sets according to the degree of fluctuation, providing a range of options. The decision-making sciences use “q-ROFS” to describe the family of the most distinct fuzzy information thoughts. In response to the degree of variation, the q-ROFSs can increasingly adjust the information region by fluctuating the restriction q≥1, making numerous options. But on the other hand, PAOs have the advantage of vanishing the influence of awkward data from the final results. To take advantages of POAs, based on Aczel- Alsina (AA) operational laws, q-Rung orthopair fuzzy (q-ROF) AA power-weighted averaging (q-ROFAAPWA) and q-ROF AA power-weighted geometric (q-ROFAAPWG) operators are stated. Also, it studied that the proposed AOs fulfilled the conditions of boundedness, monotonicity, and idempotency. Based on these newly constructed AOs, a technique for dealing the multi-attribute group decision-making (MAGDM) problems is suggested. Numerous research, correlations with another modern approach and a numerical example of selecting stock market companies have been used to show the suggested system’s applicability. The sensitivity analysis of the developed method is examined. A comparative study with other prevailing methods is also providing for superiority analysis. Finally, we demonstrated that in the results and discussion section, the proposed AOs in the q-ROPFS framework are more reliable in aggregating information than intuitionistic fuzzy (IF) set (IFS) and Pythagorean FS (PyFS) frameworks.

Digital technology implementation and impact of artificial intelligence based on bipolar complex fuzzy Schweizer–Sklar power aggregation operators

Digital technology refers to any technology that uses digital signals or electronic data to process, store, and transmit information. Some examples of digital technologies include social media platforms, cloud computing, artificial intelligence, virtual and augmented reality, and blockchain technology. Digital technology has the potential to play a significant role in achieving sustainable development goals by providing solutions for a wide range of environmental, social, and economic challenges. In this manuscript, we investigate digital technology implementation under sustainable development and would find which area of sustainable development is most in need of digital technology. Further, we investigate the operational laws based on Schweizer–Sklar t-norm and t-conorm and originate aggregation operators based on these deduced operational laws under the environment of bipolar complex fuzzy set that is bipolar complex fuzzy Schweizer–Sklar power averaging, bipolar complex fuzzy Schweizer–Sklar power weighted averaging, bipolar complex fuzzy Schweizer–Sklar power geometric and bipolar complex fuzzy Schweizer–Sklar power weighted geometric operators and then we deduce techniques of decision-making utilizing these originated operators. Afterward, we tackle a numerical example related to the digital technology implementation under sustainable development by considering artificial data and finding the area of sustainable development which is most in need of digital technology. Moreover, we reveal the impact of one of the digital technologies that are artificial intelligence in the field of healthcare and study a numerical example by considering hypothetical data by employing the originated technique of decision-making. At the last, we do a comparison of the deduced operators with numerous current operators to reveal the superiority and benefits of the deduced operators.

Dual Probabilistic Linguistic Full Consistency Additive Ratio Assessment Model for Medical Equipment Supplier Selection

In comparison to probabilistic linguistic term sets (PLTSs), dual probabilistic linguistic term sets (DPLTSs) reveal the complexity and uncertainty of this realistic world more accurately. The power average operator can mitigate the consequences of some decision-makers’ strong biases while evaluating data. Additionally, the Dombi operators during the phase of aggregation are very adaptable with general parameter. Inspired by these topics, we propose some dual probabilistic linguistic Dombi power aggregation operators to aggregate the decision-making expert’s preferences and various elegant properties of these proposed dual probabilistic linguistic (DPL)-aggregating operators. Further, we establish an integrated method with the combination of proposed DPL-aggregation operators, full consistency method (FUCOM) and additive ratio assessment (ARAS) model called the “DPL-FUCOM-ARAS” methodology for solving multi-criteria decision analysis problems under dual probabilistic linguistic environment. In this methodology, the FUCOM is applied to determine criteria weight, while the ARAS model is presented to prioritize the options from dual probabilistic linguistic perspective. We present a case study on the selection of a medical equipment supplier to highlight the applicability and show the usefulness of the proposed DPL-FUCOM-ARAS methodology. Further, we discuss the sensitivity investigation and comparative discussion to check the stability and robustness of the proposed DPL-FUCOM-ARAS methodology.

Spherical fuzzy hamacher power aggregation operators based on entropy for multiple attribute group decision making

As an improved form of fuzzy sets (FSs), spherical fuzzy sets (SFSs) could provide decision makers (DMs) with more free space to express their preference information. In this article, we first develop some Hamacher power aggregation operators under SFSs by power operators and Hamacher operators, including spherical fuzzy Hamacher power average (SFHPA) operator, spherical fuzzy Hamacher power geometric (SFHPG) operator, spherical fuzzy Hamacher power weighted average (SFHPWA) operator, spherical fuzzy Hamacher power weighted geometric (SFHPWG) operator, spherical fuzzy Hamacher power ordered weighted average (SFHPOWA) operator, spherical fuzzy Hamacher power ordered weighted geometric (SFHPOWG) operator, spherical fuzzy Hamacher power hybrid average (SFHPHA) operator and spherical fuzzy Hamacher power hybrid geometric (SFHPHG) operator. At the same time, some properties of the proposed operators are investigated, and the relationships between these operators and existing operators are discussed. Furthermore, a novel spherical fuzzy entropy measure is introduced to calculate unknown attribute weights. Then, some novel multiple attribute group decision making (MAGDM) methods are established by the proposed operators as well as entropy measure under SFSs. Lastly, the practicability of the presented methods is verified with a numerical case. Moreover, the robustness, availability and superiority for the developed methods are demonstrated via sensitivity analysis and further comparation with the existing methods.

Power aggregation operators based on Yager t-norm and t-conorm for complex q-rung orthopair fuzzy information and their application in decision-making problems

AbstractThe complex q-rung orthopair fuzzy (CQ-ROF) set can describe the complex uncertain information. In this manuscript, we develop the Yager operational laws based on the CQ-ROF information and Yager t-norm and t-conorm. Furthermore, in aggregating the CQ-ROF values, the power, averaging, and geometric aggregation operators have played a very essential and critical role in the environment of fuzzy set. Inspired from the discussed operators, we propose the CQ-ROF power Yager averaging (CQ-ROFPYA), CQ-ROF power Yager ordered averaging (CQ-ROFPYOA), CQ-ROF power Yager geometric (CQ-ROFPYG), and CQ-ROF power Yager ordered geometric (CQ-ROFPYOG) operators. These operators are the modified version of the Power, Yager, averaging, geometric, and the combination of these all based on fuzzy set (FS), intuitionistic FS, Pythagorean FS, q-rung orthopair FS, complex FS, complex intuitionistic FS, and complex Pythagorean FS. Moreover, we also discuss the main properties of the proposed operators. Additionally, we develop a multi-attribute decision-making (MADM) method based on the developed operators. To show the supremacy and validity of the proposed method, the comparison between the proposed method and some existing methods is done by some examples, and results show that the proposed method is better than the others in terms of generality and effectiveness.

Power Aggregation Operators of Interval-Valued Atanassov-Intuitionistic Fuzzy Sets Based on Aczel–Alsina t-Norm and t-Conorm and Their Applications in Decision Making

Aczel–Alsina t-norm and t-conorm are important t-norm and t-conorm, and they are extended from algebraic t-norm and t-conorm. Obviously, Aczel–Alsina t-norm and t-conorm are more general than some existing t-norm and t-conorm. Furthermore, the power aggregation (PA) operator is also a very famous and valuable operator which can consider the power relation between any two input parameters. In addition, Interval-valued Atanassov-intuitionistic fuzzy set (IVA-IFS) can easily express uncertain information. In order to fully use their advantages, in this analysis, we extend the PA operators based on Aczel–Alsina t-norm and t-conorm to IVA-IFS and propose the interval-valued Atanassov-intuitionistic fuzzy Aczel–Alsina power averaging (IVA-IFAAPA), interval-valued Atanassov-intuitionistic fuzzy Aczel–Alsina power ordered averaging (IVA-IFAAPOA), interval-valued Atanassov-intuitionistic fuzzy Aczel–Alsina power geometric (IVA-IFAAPG) and interval-valued Atanassov-intuitionistic fuzzy Aczel–Alsina power ordered geometric (IVA-IFAAPOG) operators. Moreover, we discuss the properties of the presented operators such as idempotency, monotonicity, and boundedness. In addition, a multi-attribute decision-making (MADM) procedure is proposed to process the IVA-IF information. Finally, a practical example is used to show the effectiveness and superiority of the proposed method by comparing it with some existing operators.

Aczel–Alsina Power Aggregation Operators for Complex Picture Fuzzy (CPF) Sets with Application in CPF Multi-Attribute Decision Making

Complex picture fuzzy sets are the updated version of the complex intuitionistic fuzzy sets. A complex picture fuzzy set covers three major grades such as membership, abstinence, and falsity with a prominent characteristic in which the sum of the triplet will be contained in the unit interval. In this scenario, we derive the power aggregation operators based on the Aczel–Alsina operational laws for managing the complex picture of fuzzy values. These complex picture fuzzy power aggregation operators are complex picture fuzzy Aczel–Alsina power averaging, complex picture fuzzy Aczel–Alsina weighted power averaging, complex picture fuzzy Aczel–Alsina power geometric, and complex picture fuzzy Aczel–Alsina weighted power geometric operators. We also investigate their theoretical properties. To justify these complex picture fuzzy power aggregation operators, we illustrate a procedure of a decision-making technique in the presence of complex picture fuzzy values and derive an algorithm to evaluate some multi-attribute decision-making problems. Finally, a practical example is examined to illustrate the decision-making procedure under the consideration of derived operators, and their performance is compared with that of various operators to show the supremacy and validity of the proposed approaches.

Multi-attribute decision-making methods based on Aczel–Alsina power aggregation operators for managing complex intuitionistic fuzzy sets

Multi-attribute decision-making methods based on Aczel–Alsina power aggregation operators for managing complex intuitionistic fuzzy sets

Prioritization of thermal energy techniques by employing picture fuzzy soft power average and geometric aggregation operators

Energy storage is a way of storing energy to reduce imbalances between demand and energy production. The ability to store electricity and use it later is one of the keys to reaching large quantities of renewable energy on the grid. There are several methods to store energy such as mechanical, electrical, chemical, electrochemical, and thermal energy. Regarding their operation, storage, and cost, the choice of these energy storage techniques appears to be interesting. This issue becomes very serious when there involves uncertainty. To consider this kind of uncertain information, a picture fuzzy soft set is found to be a more appropriate parameterization tool to deal with imprecise data. Based on the advanced structure of picture fuzzy soft set, here in this article, firstly, we have developed the notions of basic operational laws for picture fuzzy soft numbers. Then based on these developed operational laws, we have established the notions of picture fuzzy soft power average left({mathrm{PFS}}_{mathrm{ft}}mathrm{PA}right), weighted picture fuzzy soft power average left({mathrm{WPFS}}_{mathrm{ft}}mathrm{PA}right) and ordered weighted picture fuzzy soft power average left({mathrm{OWPFS}}_{mathrm{ft}}mathrm{PA}right) aggregation operators. Moreover, we have introduced the notions for picture fuzzy soft power geometric left({mathrm{PFS}}_{mathrm{ft}}mathrm{PG}right), weighted picture fuzzy soft power geometric left({mathrm{WPFS}}_{mathrm{ft}}mathrm{PG}right) and ordered weighted picture fuzzy soft power geometric left({mathrm{OWPFS}}_{mathrm{ft}}mathrm{PG}right) aggregation operators. Furthermore, we have established the application of picture fuzzy soft power aggregation operators for the selection of thermal energy storage techniques. For this, we have developed a decision-making approach along with an explanatory example to show the effective use of the developed theory. Furthermore, a comparative analysis of the introduced work shows the advancement of developed notions.

Evaluation of Electric Motor Cars Based Frank Power Aggregation Operators Under Picture Fuzzy Information and a Multi-Attribute Group Decision-Making Process

Evaluation of Electric Motor Cars Based Frank Power Aggregation Operators Under Picture Fuzzy Information and a Multi-Attribute Group Decision-Making Process

Power partitioned neutral aggregation operators for T-spherical fuzzy sets: An application to [formula omitted] refuelling site selection

T-spherical fuzzy set (T-SFS) is emerged as one of the effective tools for dealing uncertainty in decision-making process. Whereas, power aggregation operators help us in normalizing the impact of extreme values and capture the interconnectedness of the arguments. Meantime, one of the most prominent factors in multi-attribute decision-making (MADM) problems is the lack of awareness of biasness. Neutral operations highlight fair and unbiased character of decision makers. Thus, aiming these advantages and heterogeneity of arguments, a hybrid form of operators, weighted power partitioned neutral average operator and weighted power partitioned neutral geometric operator are developed under T-SFS environment for the first time. Beside these, power weighted neutral average, power ordered weighted neutral average, power hybrid neutral average operators, and their dual forms are initiated too. A new modified score function for T-SFS is formulated. Based on the developed operators and score function, an MADM algorithm is constituted and utilized in solving a hypothetical case study problem on hydrogen (H2) refuelling station site selection. Finally, comparative study of the developed operators with other operators is carried out to explore the applicability and supremacy of the designed MADM algorithm.

Applications of complex picture fuzzy soft power aggregation operators in multi-attribute decision making

The major theme of this analysis is to suggest a new theory in the form of complex picture fuzzy soft (CPFS) information and to initiate their major algebraic laws, score value, and accuracy values. The mathematical form of the CPFS set includes three main functions, called supporting, abstinence, and supporting against terms with a prominent characteristic that the sum of the triplet will lie in the unit interval. Further, in the consideration of the power aggregation operator using generalized t-norm and t-conorm and CPFS information, we diagnosed the mathematical concept of CPFS power averaging (CPFSPA), CPFS weighted power averaging (CPFSWPA), CPFS ordered weighted power averaging (CPFSOWPA), CPFS power geometric (CPFSPG), CPFS weighted power geometric (CPFSWPG), CPFS ordered weighted power geometric (CPFSOWPG). Moreover, the major results and their particular investigation of the invented approaches are also deliberated. Additionally, in the consideration of diagnosed operators using CPFS information, we illustrated a MADM (“multi-attribute decision-making”) tool to find the best option from the family of decisions. Finally, we have shown the supremacy and feasibility of the diagnosed operators with the help of sensitive analysis and geometrical representations.

A dynamic large-scale multiple attribute group decision-making method with probabilistic linguistic term sets based on trust relationship and opinion correlation

Dynamic large-scale multiple attribute group decision making (DLMAGDM) is ubiquitous in many areas of the real world. It is composed of large numbers of decision makers, several continuous periods, alternative set and attribute set changed with time. Given the characteristics implicited in decision-making elements and the advantages of probabilistic linguistic term sets (PLTSs) in modelling uncertainty and complexity of decision makers’ subjective opinions, this paper constructs a probabilistic linguistic DLMAGDM method. First of all, a dynamic weight determination model based on trust relationships and evidential conflicts between decision makers is proposed to obtain current dynamic weights of decision makers. Then, a comprehensive hierarchical clustering method that divides large numbers of decision makers into several subgroups is constructed based on three clustering constrains. Moreover, some probabilistic linguistic extended evidential power aggregation operators are proposed to aggregate PLTSs. These operators can appropriately handle the extreme PLTSs and fully consider the role of incomplete probabilistic distributions in PLTSs. In addition, a dynamic decision-making method based on PROMETHEE is developed to determine the final priority order of alternatives according to preferences between alternatives over several periods. Lastly, a case study for supply chain finance risk assessment for several firms in Chinese household appliance industry is utilized to illustrate the practicality and effectiveness of the probabilistic linguistic DLMAGDM method. Furthermore, the comparative analysis with some other existing methods and the sensitivity analysis are made to verify its advantages.

A Novel Approach Based on Power Aggregation Operator with the Hybrid-Indicator Information for Evaluating Regional Development Level of Rural Practical Talents

The development level evaluation of regional rural practical talents needs to comprehensively consider a variety of factors. The general development level of rural practical talents in different regions has little difference, but there are significant differences between different factors. This paper proposes a set of scientific indicators for evaluating rural practical talents, which can be used to understand the development status and level of rural practical talents in various regions and to further provide method support for the development level evaluation of rural practical talents in various regions of China. To avoid the overly subjective drawback of setting index weights in the Analytic Hierarchy Process (AHP), we set the indicator weights via the variation coefficient method and fuse the evaluation information based on an aggregation approach with a hybrid-indicator information power operator, which can lead to a more scientific and effective evaluation of the development level of rural practical talents. Finally, we apply the proposed approach to conducting the applied research on real cases, give relevant analysis and suggestions, and propose the training and introduction policies for the rural practical talents, to promote the development of rural revitalization and construction.

Applications of Hesitant Interval Neutrosophic Linguistic Schweizer-Sklar Power Aggregation Operators to MADM

Hesitant interval neutrosophic linguistic sets (HINLSs) are one of the core generalization of various sets, such as neutrosophic set (NS), interval neutrosophic set (INS), and interval neutrosophic linguistic set (INLS). HINLS can represent the uncertainty, inconsistency, and reluctance of assessment specialists by expressing qualitative and quantitative information. The goal of this article is to introduce a novel MADM technique that can account for changes in the semantic environment as well as negative consequences of experts’ excessive evaluation values. First, several innovative operational rules based on Schweizer-Sklar (SS) t -norm and t -conorm and a novel comparison procedure for HINLS are established by integrating different linguistic scale functions. This allows for varied semantic settings to be handled. Then, various innovative HINL Schweizer-Sklar power aggregation operators (AOs) are suggested, containing hesitant interval neutrosophic SS power average (HINLSSPA) operator, weighted hesitant interval neutrosophic SS power average (WHINLSSPA) operator, hesitant interval neutrosophic SS power geometric average (HINLSSPGA) operator, weighted hesitant interval neutrosophic SS power geometric average (WHINLSSPGA) operator, some core properties, and various special cases of these AOs are examined. Additionally, based on the initiated AOs, a multiple attribute decision making (MADM) technique with HINL information is anticipated. Finally, a numerical example is illustrated to show the effectiveness and practicality of the anticipated MADM method. A comparison with existing approaches are also discussed.

Performance Measure Using a Multi-Attribute Decision-Making Approach Based on Complex T-Spherical Fuzzy Power Aggregation Operators

Performance measurement has a vital role in almost every field of life especially when uncertainty is involved in processing information. The purpose of this research is to use the concept of power aggregation operators (PAOs) of complex T-spherical fuzzy sets (CTSFSs) to analyze the performance measures of software packages. In comparison to other studies, the main advantage of the PAOs of CTSFSs is its ability to feature four possible aspects of the information under uncertainty. In CTSFSs, each component has further two aspects denoted by their amplitude and phase terms and hence provides us a better ground to deal with practical problems. Another advantage of using PAOs for performance evaluation is the involvement of the relationship of the input arguments that play an essential role in aggregation. Other traditional aggregation operators (AOs) do not have such capabilities. We aim to develop complex T-spherical fuzzy (TSF) power weighted averaging and complex TSF power weighted geometric (CTSFPWG) operators and evaluated their validity using some tests. Ultimately, using the recommended operators, multi-attribute decision-making (MADM) algorithm is established for the performance evaluation of the software packages. With the help of a numerical example, we demonstrated the proposed MADM algorithm using complex uncertain information. The results show a positive impact by analyzing them after a comparative analysis where the results obtained using complex TSF PAOs seem to be more reliable than the results obtained using previously developed AOs. To see the effectiveness of the algorithm, the results are numerically compared using some existing approaches, and conclusions are drawn.

Decision support system based on complex T-Spherical fuzzy power aggregation operators

<abstract><p>The goal of this research is to develop many aggregation operators for aggregating various complex T-Spherical fuzzy sets (CT-SFSs). Existing fuzzy set theory and its extensions, which are a subset of real numbers, handle the uncertainties in the data, but they may lose some useful information and so affect the decision results. Complex Spherical fuzzy sets handle two-dimensional information in a single set by covering uncertainty with degrees whose ranges are extended from the real subset to the complex subset with unit disk. Thus, motivated by this concept, we developed certain CT-SFS operation laws and then proposed a series of novel averaging and geometric power aggregation operators. The properties of some of these operators are investigated. A multi-criteria group decision-making approach is also developed using these operators. The method's utility is demonstrated with an example of how to choose the best choices, which is then tested by comparing the results to those of other approaches.</p></abstract>

Applications of Cubic Schweizer–Sklar Power Heronian Mean to Multiple Attribute Decision‐Making

The cubic set (CS) is a basic simplification of several fuzzy notions, including fuzzy set (FS), interval‐valued FS (IVFS), and intuitionistic FS (IFS). By the degrees of IVFS and FS, CS exposes fuzzy judgement, and this is a much more potent mathematical approach for dealing with information that is unclear, ambiguous, or indistinguishable. The article provides many innovative operational laws for cubic numbers (CNs) drawn on the Schweizer–Sklar (SS) t‐norm (SSTN), and the SS t‐conorm (SSTCN), as well as several desired properties of these operational laws. We also plan to emphasise on the cubic Schweizer–Sklar power Heronian mean (CSSPHM) operator, as well as the cubic Schweizer–Sklar power geometric Heronian mean (CSSPGHM) operator, in order to maintain the supremacy of the power aggregation (PA) operators that seize the complications of the unsuitable information and Heronian mean (HM) operators that contemplate the interrelationship between the input data being aggregated. A novel multiple attribute decision‐making (MADM) model is anticipated for these freshly launched aggregation operators (AOs). Finally, a numerical example of enterprise resource planning is used to validate the approach’s relevance and usefulness. There is also a comparison with existing decision‐making models.

Power Aggregation Operators Based on t-Norm and t-Conorm under the Complex Intuitionistic Fuzzy Soft Settings and Their Application in Multi-Attribute Decision Making

Multi-attribute decision-making (MADM) is commonly used to investigate fuzzy information effectively. However, selecting the best alternative information is not always symmetric because the alternatives do not have complete information, so asymmetric information is often involved. In this analysis, we use the massive dominant and more consistent principle of power aggregation operators (PAOs) based on general t-norm and t-conorm, which manage awkward and inconsistent data in real-world dilemmas such as medical diagnosis, pattern recognition, cleaner production evaluation in gold mines, the analysis of the cancer risk factor, etc. The principle of averaging, geometric, Einstein, and Hamacher aggregation operators are specific cases of generalized PAOs. We combine the principle of complex intuitionistic fuzzy soft (CIFS) information with PAOs to initiate CIFS power averaging (CIFSPA), CIFS weighted power averaging (CIFSWPA), CIFS ordered weighted power averaging (CIFSOWPA), CIFS power geometric (CIFSPG), CIFS weighted power geometric (CIFSWPG), and CIFS ordered weighted power geometric (CIFSOWPG), and their flexible laws are elaborated. Certain specific cases (such as averaging, Einstein, and Hamacher operators) of the explored operators are also illustrated with the help of different t-norm and t-conorm operators. A MADM process is presented under the developed operators based on the CIFS environment. Finally, to investigate the supremacy of the demonstrated works, we employed a sensitivity analysis and geometrical expressions of the initiated operators with numerous prevailing works to verify the efficiency of the proposed works. This manuscript shows how to make decisions when there is asymmetric information about enterprises.