Implementation of the new easy approach to fuzzy multi-criteria decision aid in the field of management

Abstract

Decision-making is one of the most important management functions and a critical task for managers. The tools that support decision makers in making decisions are Multi-criteria Decision Making/Aid/Analysis (MCDM/MCDA) methods. Since most decisions are made under conditions of uncertainty, the fuzzy MCDM/MCDA methods are particularly important as they allow capturing the uncertainty and imprecision of the information used in making decisions. This method is the Fuzzy Preference Ranking Organization Method for Enrichment Evaluation (Fuzzy PROMETHEE), and its extension in the form of New Easy Approach to Fuzzy PROMETHEE (NEAT F-PROMETHEE). However, the unavailability of software using the NEAT F-PROMETHEE method significantly reduces its ease of use and may discourage potential users and researchers considering using it in their studies. Therefore, to facilitate the use of this MCDA method, the article presents the implementation of NEAT F-PROMETHEE in the MATLAB environment. Moreover, the verification of the developed implementation and its application in the management decision-making problem is presented, together with the analysis of the operation of the mapping correction function used in NEAT F-PROMETHEE. The results obtained with NEAT F-PROMETHEE were compared with the results of the Fuzzy PROMETHEE method which did not apply correction. The analysis shows that the correction applied in NEAT F-PROMETHEE allows obtaining results with a smaller error than the non-corrected implementations of PROMETHEE Fuzzy. Therefore, a more accurate solution of the decision problem is obtained. improving the process of mapping fuzzy numbers in the Fuzzy PROMETHEE method implementing a correction mechanism while mapping trapezoidal fuzzy numbers

Selection of Electric Vehicles for the Needs of Sustainable Transport under Conditions of Uncertainty—A Comparative Study on Fuzzy MCDA Methods

“…The alternatives performance model and the preference model provided input to the three fuzzy MCDA methods included in the reference methods collection. These methods were: fuzzy SAW [31], fuzzy TOPSIS [30] and NEAT F-PROMETHEE II [56]. The decision problem was solved using each of the reference methods, resulting in three rankings of alternatives.…”

Some distance measures for pythagorean cubic fuzzy sets: Application selection in optimal treatment for depression and anxiety

“…Distance measure is a crucial concept within the field of FS theory and has been widely used in different fields. It plays an important role in these fields, as it allows for the quantification of the similarity or dissimilarity between fuzzy sets, which in turn is useful for making decisions, identifying patterns, training machine learning models, and forecasting market trends, among other applications [16] , [17] , [18] , [19] and [20] . It has intrigued several researchers and they have delved deeper into it through further investigations.…”