GLDS method for multiple attribute group decision making under 2-Tuple linguistic neutrosophic environment

In this article, the gained and lost dominance score (GLDS) method is extended into the 2-tuple linguistic neutrosophic environment, which also combined the power aggregation operator with the evaluation information to deal with the multi-attribute group decision-making problem. Since the power aggregation operator can eliminate the effects of extreme evaluating data from some experts with prejudice, this paper further proposes the 2-tuple linguistic neutrosophic numbers power-weighted average operator and 2-tuple linguistic neutrosophic numbers power-weighted geometric operator to aggregate the decision makers’ evaluation. Moreover, a model based on the score function and distance measure of 2-tuple linguistic neutrosophic numbers (2TLNNs) is developed to get the criteria weights. Combing the GLDS method with 2-tuple linguistic neutrosophic numbers and developing a 2TLNN-GLDS method for multiple attribute group decision making, it can express complex fuzzy information more conveniently in a qualitative environment and also consider the dominance relations between alternatives which can get more effective results in real decision-making problems. Finally, an applicable example of selecting the optimal low-carbon logistics park site is given. The comparing results show that the proposed method outperforms the other existing methods, as it can get more reasonable results than others and it is more convenient and effective to express uncertain information in solving realistic decision-making problems.

MULTI-CRITERIA MODEL BASED ON LINGUISTIC NEUTROSOPHIC NUMBERS: THE SELECTION OF UNMANNED AIRCRAFT

The paper presents a new approach in treating uncertainty and subjectivity in the decision making process based on the modification of Multi Attributive Border Approximation area Comparison (MABAC) and an Objective-Subjective (OS) model by applying linguistic neutrosophic numbers (LNN) instead of traditional numerical values. By integrating these models with linguistic neutrosophic numbers it was shown that it is possible to a significant extent to eliminate subjective qualitative assessments and assumptions by decision makers in complex decision-making conditions. On this basis, a new hybrid LNN OS-MABAC model was formed. This model was tested and validated on a case-study of the selection of optimal unmanned aircraft intended to combat forest fires.

Linguistic Neutrosophic Numbers Einstein Operator and Its Application in Decision Making

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.

A Novel Approach for the Selection of Power-Generation Technology Using a Linguistic Neutrosophic CODAS Method: A Case Study in Libya

Rapid increases in energy demand and international drive to reduce carbon emissions from fossil fuels have led many oil-rich countries to diversify their energy portfolio and resources. Libya is one of these countries, and it has recently become interested in utilizing its renewable-energy resources in order to reduce financial and energy dependency on oil reserves. This paper introduces an original multicriteria decision-making Pairwise-CODAS model in which the modification of the CODAS method was made using Linguistic Neutrosophic Numbers (LNN). The paper also suggests a new LNN Pairwise (LNN PW) model for determining the weight coefficients of the criteria developed by the authors. By integrating these models with linguistic neutrosophic numbers, it was shown that it is possible to a significant extent to eliminate subjective qualitative assessments and assumptions by decision makers in complex decision-making conditions. The LNN PW-CODAS model was tested and validated in a case study of the selection of optimal Power-Generation Technology (PGT) in Libya. Testing of the model showed that the proposed model based on linguistic neutrosophic numbers provides objective expert evaluation by eliminating subjective assessments when determining the numerical values of criteria. A sensitivity analysis of the LNN PW-CODAS model, carried out through 68 scenarios of changes in the weight coefficients, showed a high degree of stability of the solutions obtained in the ranking of the alternatives. The results were validated by comparison with LNN extensions of four multicriteria decision-making models.

Multiple-Attribute Decision-Making Method Using Similarity Measures of Hesitant Linguistic Neutrosophic Numbers Regarding Least Common Multiple Cardinality

Linguistic neutrosophic numbers (LNNs) are a powerful tool for describing fuzzy information with three independent linguistic variables (LVs), which express the degrees of truth, uncertainty, and falsity, respectively. However, existing LNNs cannot depict the hesitancy of the decision-maker (DM). To solve this issue, this paper first defines a hesitant linguistic neutrosophic number (HLNN), which consists of a few LNNs regarding an evaluated object due to DMs’ hesitancy to represent their hesitant and uncertain information in the decision-making process. Then, based on the least common multiple cardinality (LCMC), we present generalized distance and similarity measures of HLNNs, and then develop a similarity measure-based multiple-attribute decision-making (MADM) method to handle the MADM problem in the HLNN setting. Finally, the feasibility of the proposed approach is verified by an investment decision case.