NEAT F-PROMETHEE - A new fuzzy multiple criteria decision making method based on the adjustment of mapping trapezoidal fuzzy numbers

The Fuzzy PROMETHEE (Preference Ranking Organization METHod for Enrichment Evaluation) method allows one to capture uncertainty and imprecision of input data of a decision problem as well as the uncertainty of the decision-maker's preferences. However, a considerable number of implementations of the Fuzzy PROMETHEE method are burdened with some imperfections related to, among other things, a high level of complexity, an applied computational procedure or the incompatibility with the classical PROMETHEE method paradigms. The article presents a new fuzzy MCDM method called NEAT F-PROMETHEE (New Easy Approach To Fuzzy PROMETHEE) characterized by low complexity, conformity with the methodological assumptions of the classical PROMETHEE method and the improvement of the process of mapping fuzzy numbers from a space X on Y with the use of the correction mechanism while mapping. Furthermore, other improvements have been introduced which are to simplify the computational procedure and increase its clarity for the sake of the decision-maker. The prepared method and its results have been compared to other Fuzzy PROMETHEE implementations, the classical PROMETHEE method as well as the Fuzzy TOPSIS method. In the research, it has been found out that the correction mechanism and other improvements applied in NEAT F-PROMETHEE make it possible to obtain a higher computational accuracy what results in a higher credibility of the solution of a decision problem in comparison with many other Fuzzy PROMETHEE implementations. The advantage of the prepared method over more complex implementations is its simplicity, easiness of application and interpretation of results. It has also been indicated that uncertainty of input data and uncertainty of the decision-maker's preferences are related to each other and the relationship is expressed by grouped relations of preferences, that is, preference, J-preference and outranking. (C) 2018 Elsevier Ltd. All rights reserved.