Generalized intuitionistic fuzzy aggregation operators based on confidence levels for group decision making

Intuitionistic fuzzy fairly operators and additive ratio assessment-based integrated model for selecting the optimal sustainable industrial building options

In the past few years, the private sectors and industries have focused their attention on sustainable development goals to achieve the better and more sustainable future for all. To accomplish a sustainable community, one requires to better recognize the fundamental indicators and selects the most suitable sustainable policies in diverse regions of the community. Considering the huge impact of construction industry on sustainable development, very less research efforts have been made to obtain worldwide sustainable elucidations for this type of industry. As a large sector of construction industry, industrial buildings consume enormous amounts of energy and financial assets, and play a key character in job creation and life quality improvement in the community. In order to assess the sustainable industrial buildings by means of multiple indicators, the present study introduces a hybrid multi-criteria decision-making methodology which integrates the fairly aggregation operator, the MEthod based on the Removal Effects of Criteria (MEREC), the stepwise weight assessment ratio analysis (SWARA) and the additive ratio assessment (ARAS) methods with intuitionistic fuzzy set (IFS). In this respect, firstly new intuitionistic fuzzy weighted fairly aggregation operators are proposed and then employed to aggregate the decision information in the proposed hybrid method. This operator overcomes the limitations of basic intuitionistic fuzzy aggregation operators. To find the criteria weights, an integrated model is presented based on the MEREC for objective weights and the SWARA for subjective weights of indicators under IFS context. To rank the sustainable industrial buildings, an integrated ARAS method is employed from uncertain perspective. Further, a case study concerning sustainable industrial buildings evaluation is presented to illustrate the superiority and practicality of the developed methodology. The advantages of the developed approach are highlighted in terms of stability and reliability by comparison with some of the existing methods.

An Optimization Strategy for MADM Framework with Confidence Level Aggregation Operators under Probabilistic Neutrosophic Hesitant Fuzzy Rough Environment

In this research, we first offer unique notions of averaging and geometric aggregation operators with confidence level by employing a probabilistic neutrosophic hesitant fuzzy rough framework. Then, we look into other descriptions of the suggested operators, such as idempotency, boundedness, and monotonicity. Additionally, for the derived operators, we establish the score and accuracy functions. We also provide a novel approach to assessing the selection procedure for smart medical devices (SMDs). The selection criteria for SMDs are quite complex, which is the most noteworthy feature of this investigation. It is suggested that these processes be simulated using a method utilizing a hesitant fuzzy set, a rough set, and a probabilistic single-valued neutrosophics set. The proposed approach is employed in the decision-making process, while taking into consideration the decision-makers’ (DMs’) level of confidence in the data they have obtained in order to deal with ambiguity, incomplete data, and uncertainty in lower and upper approximations. The major goal was to outline the issue’s complexities in order to pique interest among experts in the health care sector and encourage them to evaluate SMDs using various evaluation standards. The analysis of the technique’s outcomes demonstrated that the rankings and the results themselves were adequate and trustworthy. The effectiveness of our suggested improvements is also demonstrated through a symmetrical analysis. The symmetry behavior shows that the current techniques address more complex and advanced data.

Complex pythagorean fuzzy aggregation operators based on confidence levels and their applications

The most important influence of this assessment is to analyze some new operational laws based on confidential levels (CLs) for complex Pythagorean fuzzy (CPF) settings. Moreover, to demonstrate the closeness between finite numbers of alternatives, the conception of confidence CPF weighted averaging (CCPFWA), confidence CPF ordered weighted averaging (CCPFOWA), confidence CPF weighted geometric (CCPFWG), and confidence CPF ordered weighted geometric (CCPFOWG) operators are invented. Several significant features of the invented works are also diagnosed. Moreover, to investigate the beneficial optimal from a large number of alternatives, a multi-attribute decision-making (MADM) analysis is analyzed based on CPF data. A lot of examples are demonstrated based on invented works to evaluate the supremacy and ability of the initiated works. For massive convenience, the sensitivity analysis and merits of the identified works are also explored with the help of comparative analysis and they're graphical shown.

Bipolar Complex Fuzzy Hamacher Aggregation Operators and Their Applications in Multi-Attribute Decision Making

On the basis of Hamacher operations, in this manuscript, we interpret bipolar complex fuzzy Hamacher weighted average (BCFHWA) operator, bipolar complex fuzzy Hamacher ordered weighted average (BCFHOWA) operator, bipolar complex fuzzy Hamacher hybrid average (BCFHHA) operator, bipolar complex fuzzy Hamacher weighted geometric (BCFHWG) operator, bipolar complex fuzzy Hamacher ordered weighted geometric (BCFHOWG) operator, and bipolar complex fuzzy Hamacher hybrid geometric (BCFHHG) operator. We present the features and particular cases of the above-mentioned operators. Subsequently, we use these operators for methods that can resolve bipolar complex fuzzy multiple attribute decision making (MADM) issues. We provide a numerical example to authenticate the interpreted methods. In the end, we compare our approach with existing methods in order to show its effectiveness and practicality.