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Fuzzy sets (FSs), which were proposed by Zadeh (1965), are regarded as a comprehensive tool for solving multi-criteria decision-making(MCDM)problems(Bellman&Zadeh,1970).
The aim of this paper is to introduce some new operators for aggregating single-valued neutrosophic (SVN) information and to apply them to solve the multi-criteria decision-making (MCDM) problems.
This book is a printed edition of the Special Issue "Neutrosophic Multi-Criteria Decision Making" that was published in Axioms
Neutrosophic sets, being generalization of classic sets, fuzzy sets and intuitionistic fuzzy sets, can simultaneously represent uncertain, imprecise, incomplete, and inconsistent information existing in the real world. Neutrosophic theory has been developed in twenty first century and not much of arithmetic has been developed for this set.
In the paper, a method based on SVTNNs is proposed for dealing with multi-criteria group decision-making (MCGDM) problems.
Multi-attribute decision-making refers to the decision-making problem of selecting the optimal alternative or sorting the scheme when considering multiple attributes, which is widely used in engineering design, economy, management and military, etc. But in real application, the attribute information of many objects is often inaccurate or uncertain, so it is very important for us to find a useful and efficient method to solve the problem. Neutrosophic set is proposed from philosophical point of view to handle inaccurate information efficiently, and a single-valued neutrosophic set (SVNS) is a special case of neutrosophic set, which is widely used in actual application fields. In this paper, a new method based on single-valued neutrosophic sets aggregation to solve multi-attribute decision making problem is proposed. Firstly, the neutrosophic decision matrix is obtained by expert assessment, a score function of single-valued neutrosophic sets (SVNSs) is defined to obtain the positive ideal solution (PIS) and the negative ideal solution (NIS). Then all alternatives are aggregated based on TOPSIS method to make decision. Finally numerical examples are given to verify the feasibility and rationality of the method.
The aim of this paper is to introduce some new operators for aggregating single-valued neutrosophic (SVN) information and to apply them to solve the multi-criteria decision-making (MCDM) problems.
In this paper, we propose three similarity measure methods for single-valued neutrosophic refined sets and interval neutrosophic refined sets based on Jaccard, Dice and Cosine similarity measures of single-valued neutrosophic sets and interval neutrosophic sets.
This paper investigates some Frank aggregation operators of interval-valued neutrosophic numbers (IVNNs) and applies to multi-attribute group decision-making (MAGDM) problems. First, the Frank t-conorm and t-norm are extended to interval-valued neutrosophic environment. Some new operational laws for IVNNs are dened and their related properties are investigated. Based on these new operational laws, some new aggregation operators for IVNNs are developed including the interval-valued neutrosophic Frank weighted averaging (IVNFWA) operator and the interval-valued neutrosophic Frank weighted geometric (IVNFWG) operator. Then some desirable properties and special cases of these new operators are further discussed. To solve the MAGDM with IVNNs, the weights of decision makers (DMs) are determined by using extended technique for order preference by similarity to ideal solution (TOPSIS) method based on cross-entropy. Additionally, attribute weights are determined based on the similarity degrees between alternatives and the absolute ideal solutions. Further, two new decision-making approaches for MAGDM with IVNNs are put forward by means of the IVNFWA and IVNFWG operators, respectively.Finally, a case study of selecting an agricultural socialization service provider is analyzed to illustrate the practicality and effectiveness of the developed two approaches.
In this paper, the hesitant neutrosophic linguistic set is first defined by extending a hesitant fuzzy set to accommodate linguistic terms and neutrosophic fuzzy values. Some operational laws are defined for hesitant neutrosophic linguistic fuzzy information.