Some Strategical Methods for Solving Decision Making Problems under Fuzzy Intuitionistic Fuzzy Set Environment

Abstract

Multiattribute group decision-making (MAGDM) problems are the imperative part of modern decision theory where a set of alternatives has to be assessed against the multiple influential attributes before the best alternative is selected. Indeed in ordinary life, ``to do or not to do' is one of the foremost riddles that a person faces before jumping to action. The whole decision making (DM) process is subordinate upon the proper data being accessible to the proper individuals at the correct times. In general, in order to evaluate the given objects, a decision-maker may set some characteristic or criteria which need to be fulfilled/satisfied to select the best one(s) during solving the problems. Based on the criteria, DM problems are classified into two types, $(1)$ decision based on the single criteria; $(2)$ decision based two or more attributes known as multi-attribute decision-making (MADM). Due to the increasing complexity of the socioeconomic environment and the lack of knowledge, it is difficult for the decision maker to give the exact decision as there is always an imprecise, vague or uncertain information. To deal with this, the theory of the fuzzy sets (FSs) or its extensions such as intuitionistic FSs (IFSs), interval-valued IFSs (IVIFSs), Soft sets, etc., are widely used by the researchers so as to minimize the uncertainty level. In the last few decades, several types of research paid more attention to MADM or MAGDM problems in various fields. However, one of the most important factors to access the best one(s) is the considered environment, under which the decision-maker(s) have to evaluate the given alternatives. The environment considered during the DM problems may be quantitative and qualitative according to the situation of real-life problems. To address it, a concept of a linguistic variable (LV) and hence their corresponding approaches are developed by the researchers to analyze the information by using various information measures and the aggregation operators (AOs).