Linguistic neutrosophic numbers (LNN) is presented by Fang and Ye in 2017, which can describe the truth, falsity, and indeterminacy linguistic information independently. In this paper, the LNN and the Bonferroni mean operator are merged together to propose a LNN normalized weighted Bonferroni mean (LNNNWBM) operator and a LNN normalized weighted geometric Bonferroni mean (LNNNWGBM) operator and the properties of these two operators are proved. Further, multi-attribute group decision methods are introduced based on the proposed LNNNWBM and LNNNWGBM operators, and then an example is provided to demonstrate the application and validity of the proposed methods. In addition, in order to consider the effect of the parameters p and q on the decision results, different pairs of parameter values are employed to verify the decision results.

Based on the multiplicity evaluation in some real situations, this paper firstly introduces a single-valued neutrosophic multiset (SVNM) as a subclass of neutrosophic multiset (NM) to express the multiplicity information and the operational relations of SVNMs. Then, a cosine measure between SVNMs and weighted cosine measure between SVNMs are presented to measure the cosine degree between SVNMs, and their properties are investigated. Based on the weighted cosine measure of SVNMs, a multiple attribute decision-making method under a SVNM environment is proposed, in which the evaluated values of alternatives are taken in the form of SVNMs. The ranking order of all alternatives and the best one can be determined by the weighted cosine measure between every alternative and the ideal alternative. Finally, an actual application on the selecting problem illustrates the effectiveness and application of the proposed method.

A valid aggregation operator can reflect the decision result more clearly and make the decision effect more correctly. In this article, a linguistic neutrosophic multiset is first proposed to handle the multiplicity information, which is an expanding of neutrosophic multiset. Two Heronian mean operators are proposed to aggregate the linguistic neutrosophic multiset, one is a linguistic neutrosophic multiplicity number generalized-weighted Heronian mean operator, the other is a linguistic neutrosophic multiplicity number improved-generalized-weighted Heronian mean operator, and then their properties are discussed. Furthermore, two decision-making methods are introduced based on linguistic neutrosophic multiplicity number generalized-weighted Heronian mean or linguistic neutrosophic multiplicity number improved-generalized-weighted Heronian mean operators under linguistic neutrosophic multiplicity number environment. Finally, an illustrative example is used to indicate the practicality and validity of these two methods.

In real applications, most decisions are fuzzy decisions, and the decision results mainly depend on the choice of aggregation operators. In order to aggregate information more scientifically and reasonably, the Heronian mean operator was studied in this paper. Considering the advantages and limitations of the Heronian mean (HM) operator, four Heronian mean operators for bipolar neutrosophic number (BNN) are proposed: the BNN generalized weighted HM (BNNGWHM) operator, the BNN improved generalized weighted HM (BNNIGWHM) operator, the BNN generalized weighted geometry HM (BNNGWGHM) operator, and the BNN improved generalized weighted geometry HM (BNNIGWGHM) operator. Then, their propositions were examined. Furthermore, two multi-criteria decision methods based on the proposed BNNIGWHM and BNNIGWGHM operator are introduced under a BNN environment. Lastly, the effectiveness of the new methods was verified with an example.

This paper introduces a Refined-Interval Neutrosophic Set (R-INS) as an extension of a Refined-Single Valued Neutrosophic Set (R-SVNS) and puts forward the decision-making models based on the Cosine Measures of R-SVNSs and Refined-Interval Neutrosophic Sets (R-INSs). By the cosine measure between every alternative and the ideal alternative, all the alternatives can be ranked with the measure values, and the best one of all alternatives can be selected. The proposed methods for multiple attribute decision making (MADM) are more clear (more refined) and the refined evaluation results are credible, so the proposed decision-making models are very suitable for handling with the decision-making problems with refined neutrosophic information.

Linguistic neutrosophic numbers (LNNs) include single-value neutrosophic numbers and linguistic variable numbers, which have been proposed by Fang and Ye. In this paper, we define the linguistic neutrosophic number Einstein sum, linguistic neutrosophic number Einstein product, and linguistic neutrosophic number Einstein exponentiation operations based on the Einstein operation. Then, we analyze some of the relationships between these operations. For LNN aggregation problems, we put forward two kinds of LNN aggregation operators, one is the LNN Einstein weighted average operator and the other is the LNN Einstein geometry (LNNEWG) operator. Then we present a method for solving decision-making problems based on LNNEWA and LNNEWG operators in the linguistic neutrosophic environment. Finally, we apply an example to verify the feasibility of these two methods.