Implementation of Fuzzy Aggregation Operators in Group Decision Making for Investment Analysis

Abstract

In many cases, experts involved in a group run out of comprehensive knowledge about a complex problem to make a reasonable collective decision. Since its inception, the experts’ preferences in decision making process were to represent their opinions mostly in qualitative and quantitative forms of information, but in case the representation of experts’ views will be unable for quantitative decision analysis, the linguistic terms can be successfully used instead. In a lot of group decision making problems, especially in the last two decades, the significance of criteria is determined in fuzzy environment to evaluate the alternatives performance. The application of classical group decision making techniques indisputably demonstrates the ineffectiveness of solving a complex human-centric problems to properly represent the aggregation of experts’ opinions which are mostly evaluated subjectively, to reach the consensus in selection the best group result in the presence of imprecise and ambiguous data, and to rank the alternatives from most favorable to least favorable or inversely in accordance with their priority. For this purpose, the fuzzy group decision making techniques have gained immense popularity and have been successfully applied in such fields of science as engineering, business, sociology, education etc. Gathering fuzzy information from a group of decision makers to develop a combined opinion or judgment is an important problem in expert system theory to get a more complete and rational solution to decision making problem. The present study investigates the fuzzy aggregation operators applied in group decision making for investment analysis. The systematic sorting and ranking of approaches that deal with fuzzy aggregation is represented. The effectiveness of aggregation operators is demonstrated through a provided numerical example. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.