An approach for linguistic multi-attribute decision making based on linguistic many-valued logic

There are various types of multi-attribute decision-making (MADM) problems in our daily lives and decision-making problems under uncertain environments with vague and imprecise information involved. Therefore, linguistic multi-attribute decision-making problems are an important type studied extensively. Besides, it is easier for decision-makers to use linguistic terms to evaluate/choose among alternatives in real life. Based on the theoretical foundation of the Hedge algebra and linguistic many-valued logic, this study aims to address multi-attribute decision-making problems by linguistic valued qualitative aggregation and reasoning method. In this paper, we construct a finite monotonous Hedge algebra for modeling the linguistic information related to MADM problems and use linguistic many-valued logic for deducing the outcome of decision making. Our method computes directly on linguistic terms without numerical approximation. This method takes advantage of linguistic information processing and shows the benefit of Hedge algebra

On the relatively Pseudo-complement operation in finite RHAs

Refined hedge algebras were introduced and  investigated by Ho Nam in [6--9]. It is known [9] that every refined hedge algebra (RHA, for short) with a chair of the primary generators is a distributive lattice. In this paper we restrict our consideration to finite version of RHAs (see [7, 9]). It is shown that every finite RHA is a Heyting (pseudo-Boolean) algebra. Furthermore, some computing results for the relatively pseudo-complement operation in  these algebras will be exhibited