Approach to solve multi‐criteria group decision‐making problems by exponential operational law in generalised spherical fuzzy environment

Abstract

The construction of generalised spherical fuzzy number (GSFN) is a reliable philosophy to design and understanding of vagueness and impreciseness. In this study, at first, the authors have defined the generalised spherical fuzzy set and discussed its several properties. Then, a new score and accuracy functions have been introduced in the generalised spherical fuzzy environment which leads to a new method of conversion of fuzzy number into a crisp number. New exponential operational law has been defined for GSFNs where the bases are positive real numbers & components are GSFNs and its various algebraic properties have been studied explicitly. Using this exponential operational law, a generalised spherical weighted exponential averaging operator has been proposed, which is used to develop a multi-criteria group decision-making (MCGDM) method in the generalised spherical fuzzy environment. The newly developed MCGDM has been demonstrated through a real-life problem and its effectiveness and rationality have been shown through sensitivity analysis. instead of radius 1 (as it was considered in spherical fuzzy number) and introduced the perception of new GSFN.

Machine learning‐based energy efficient technologies for smart grid

“…The practice of energy efficiency saves energy and aids in decreasing carbon releases during power generation in the environment 11‐15 . The energy efficiency can be guaranteed by minimizing losses at the powder production side during the transmission, generation, and distribution stage 16‐20 . Energy efficiency on the customer side can be achieved by creating and using products that use minimal energy to operate their companies 21 .…”

“…[11][12][13][14][15] The energy efficiency can be guaranteed by minimizing losses at the powder production side during the transmission, generation, and distribution stage. [16][17][18][19][20] Energy efficiency on the customer side can be achieved by creating and using products that use minimal energy to operate their companies. 21 It is up to the users to use the power sensibly, that is, not to overly enable the system.…”

Large-scale group decision-making based on Pythagorean linguistic preference relations using experts clustering and consensus measure with non-cooperative behavior analysis of clusters

“…In this situation, Mandal et al [38] proposed the new type of preference relation called Pythagorean linguistic preference relation (PLPR), which is addressed the preferred degree and non-preferred degree of LVs according to Yagers Pythagorean fuzzy sets (PFSs) [53]. We suggest for interested researchers to see the attractive studies in [4,5,9,34,37].…”

Classification of trapezoidal bipolar neutrosophic number, de-bipolarization technique and its execution in cloud service-based MCGDM problem

“…As researches goes on, researchers developed single valued NS [9], triangular NS [10], trapezoidal NS [11] and recently, Chakraborty [12] constructed the theory of pentagonal neutrosophic set. Basset et al [13] developed the perception of type 2 neutrosophic numbers, Peng et al [14] ignited power aggregation operator-based MCGDM, Maity et al [15] focused on backlogging EOQ model in dense environment, Garg [16] analyzed L.P.P-based D.M problem, Wang et al [17] established linguistic MCGDM problem in cloud model, Jiang [18] introduced learning model using defuzzification skill, Nabeeh et al [19] formulated neutrosophic AHP model based on IoT, Biswas et al [20] proposed TOPSIS skill MCGDM problem, Yu-Han et al [21] proposed MCGDM problem using VIKOR method, Stanujkic et al [22] represents MULTIMOORA technique MCDM problem, Haque et al [23] structured generalized spherical number, Chakraborty [24] evolved neutron-logic oriented EOQ model, the authors [25,26] developed cylindrical neutrosophic number etc.…”