Abstract
The current study presents a novel multi-criteria decision making (MCDM) approach to address decision analysis with T-spherical fuzzy data, whose weights of criteria are fully unknown. To serve the purpose, we design some generalized operational laws, namely Frank operational laws for T-spherical fuzzy numbers (T-SFNs) using Frank t-norm and t-conorm. Then, based on the proposed operations, a range of T-spherical fuzzy aggregation operators is developed to aggregate T-spherical fuzzy information efficiently. Also, discuss their particular cases and desirable properties are well proved. Next, we propound the T-spherical fuzzy entropy measure and its capability to fulfil the required properties. Then it is further used for criteria weight determination in the proposed aggregation based MCDM approach. In addition, a descriptive example is provided for viewing the applicability of the established approach. Lastly, the superiority and validity of this approach are highlighted by parameter analysis and comparative analysis.
Highlights
D ECISION-MAKING is a frequent and daily action in human existence to select the best alternative based on a set of criteria
SOME BASIC CONCEPTS The notion of T-spherical FS (SFS) is propounded by Mahmood et al [20] as a synthesis of SFS to offer a broader range of preferences for DMs and enable them to express their hesitation about an alternative
Based on the operational laws given in Definition 6, we examine the following results
Summary
D ECISION-MAKING is a frequent and daily action in human existence to select the best alternative based on a set of criteria. With the passage of time, it is noticed that these operators [24], [25] cannot model DMs opinions when we obtain information in the form of a triplet like To tackle such issues, Garg et al [26] studied some geometric aggregation operators based on their developed T-spherical fuzzy operational rules. Guleria and Bajaj [29] several averaging and geometric aggregation operators for T-spherical fuzzy soft numbers Based on their proposed operators, they provided an MCDM technique for handling complex decision making problems. The last section presents some concluding remarks summarizing the article
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