Abstract
The interval neutrosophic set (INS) can make it easier to articulate incomplete, indeterminate, and inconsistent information, and the Schweizer-Sklar (Sh-Sk) t-norm (tm) and t-conorm (tcm) can make the information aggregation process more flexible due to a variable parameter. To take full advantage of INS and Sh-Sk operations, in this article, we expanded the Sh-Sk and to IN numbers (INNs) in which the variable parameter takes values from [ ∞ − , 0 ) , develop the Sh-Sk operational laws for INNs and discussed its desirable properties. After that, based on these newly developed operational laws, two types of generalized prioritized aggregation operators are established, the generalized IN Sh-Sk prioritized weighted averaging (INSh-SkPWA) operator and the generalized IN Sh-Sk prioritized weighted geometric (INSh-SkPWG) operator. Additionally, we swot a number of valuable characteristics of these intended aggregation operators (AGOs) and created two novel decision-making models to match with multiple-attribute decision-making (MADM) problems under IN information established on INSh-SkPWA and INSh-SkPRWG operators. Finally, an expressive example regarding evaluating the technological innovation capability for the high-tech enterprises is specified to confirm the efficacy of the intended models.
Highlights
The most important utility of multiple-attribute decision-making (MADM) problems is to go for the preeminent alternative from the set of finite alternatives as stated to the partiality values specified by decision makers (DMs) with admiration to the attributes
It can be seen that no research attempted to merge Sh-Sk operational laws and generalized prioritized aggregation operators to deal with IN information
We develop some generalized prioritized aggregation operators based on the developed operational laws for INNs
Summary
The most important utility of multiple-attribute decision-making (MADM) problems is to go for the preeminent alternative from the set of finite alternatives as stated to the partiality values specified by decision makers (DMs) with admiration to the attributes. Zhang et al [12] initiated operational laws for IN numbers and established some IN weighted averaging and IN weighted geometric AGOs and applied these AGOs to solve MADM problems. Nagarajan et al [47] presented some Sh-Sk operational laws for INNs by taking the values of the variable parameter from They anticipated some weighted averaging and geometric AOs based on these Sh–Sk operational laws for INNs. In recent years, INSs have gained much attention from the researchers and a great number of achievement have been made, such as VIKOR [48,49,50], cross entropy [51], MABAC, EDAS [52], out ranking approach [53], distance and similarity measures [54], TOPSIS [55]
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