Abstract

In the modern world, the computation of vague data is a challenging job. Different theories are presented to deal with such situations. Amongst them, fuzzy set theory and its extensions produced remarkable results. Samrandache extended the theory to a new horizon with the neutrosophic set (NS), which was further extended to interval neutrosophic set (INS). Neutrosophic cubic set (NCS) is the generalized version of NS and INS. This characteristic makes it an exceptional choice to deal with vague and imprecise data. Aggregation operators are key features of decision-making theory. In recent times several aggregation operators were defined in NCS. The intent of this paper is to generalize these aggregation operators by presenting neutrosophic cubic generalized unified aggregation (NCGUA) and neutrosophic cubic quasi-generalized unified aggregation (NCQGUA) operators. The accuracy and precision are a vital tool to minimize the potential threat in decision making. Generally, in decision making methods, alternatives and criteria are considered to evaluate the better outcome. However, sometimes the decision making environment has more components to express the problem completely. These components are named as the state of nature corresponding to each criterion. This complex frame of work is dealt with by presenting the multi-expert decision-making method (MEDMM).

Highlights

  • IntroductionOne of the complex phenomena is to deal with the vagueness and uncertainty in data

  • This paper generalized aggregation operators, such as neutrosophic cubic generalized unified aggregation (NCGUA) operators, which provide a single plate form for researchers and experts to deal with different types of aggregation operators

  • The NCGUA operator is further generalized to neutrosophic cubic quasi-generalized unified aggregation (NCQGUA) aggregation operators

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Summary

Introduction

One of the complex phenomena is to deal with the vagueness and uncertainty in data. Because uncertainty is inevitable in problems in different areas of life, conventional methods have failed to cope with such problems. Many models have been introduced to incorporate uncertainty into the description of the system. Zadeh presented their theory of Fuzzy sets [1]. The possibilistic nature of fuzzy set theory attracted researchers to apply it in different fields of sciences like artificial intelligence, decision making theory, information sciences, medical sciences and more. Researchers used it for decision making problems [7,8,9,10,11,12]

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