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

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

Pythagorean cubic fuzzy sets represent an advancement beyond conventional interval-valued Pythagorean sets, integrating the principles of Pythagorean fuzzy sets and interval-valued Pythagorean fuzzy sets. Given the critical significance of distance measures in real-world decision-making and pattern recognition tasks, it is noteworthy that there exists a notable gap in the literature regarding distance measures specifically tailored for Pythagorean cubic fuzzy sets. The objectives of this paper are:•To define novel generalized distance measures between Pythagorean cubic fuzzy sets (PCFSs) to tackle intricate decision-making challenges.•These novel distance measures are undergoing testing on a real-world scenario concerning the management of anxiety and depression to evaluate their effectiveness and practical application.•We have illustrated the boundedness and nonlinear characteristics inherent in these distance measures. In addition, we conduct comparative analyses with existing approaches to validate the proposed methodology, thereby providing insights into its advantages and potential applications.

Visualization of uncertain data in the NEAT F-PROMETHEE method

One of the decision-making problems is the problem of description, which is supposed to explain the decision-making situation by describing the alternatives and their consequences. One of the basic multi-criteria decision analysis (MCDA) tools that are used when considering the problem of description are graphical visualizations. The article presents visualizations developed for the presentation of data in the MCDA method called NEAT F-PROMETHEE. Since the NEAT F-PROMETHEE method is used to consider decision-making problems characterized by uncertainty and imprecision, it operates on fuzzy numbers. Therefore, it was important that the developed visualizations also present data in the form of fuzzy numbers and consider uncertainty. The verification of the developed graphic representations consisted in comparing the ease of processing, understanding and interpreting information presented in a visual and tabular form. As a result of the conducted research, the advantages of the graphical representation over the tabular one were noticed. Data presented in the form of numerical values included in tables, if there are a lot of them, may cause information overload of the decision-maker. Fuzzy numbers included in the tables are often difficult to read, and a big challenge for the analyst is to establish mutual relationships and compare fuzzy numbers described numerically. It is much easier to process fuzzy numbers in a graphical form, because then you can immediately observe the relationships between the numbers.•Developing a visualization of uncertain data for the NEAT F-PROMETHEE method•Comparison of clarity of graphical and tabular representation.

A Fuzzy Extension of Simplified Best-Worst Method (F-SBWM) and Its Applications to Decision-Making Problems

Today, most of the issues and challenges faced by managers and decision makers are complex and multifaceted. More clearly, due to the developments of technologies, emerging trends in various industries, competitive markets, and rapid and transformative changes in the business environment, managers and decision makers have faced an uncertain environments and issues that cannot be resolved definitively. The use of multi-criteria decision-making (MCDM) methods as a practical and decision-supporting tool allows managers to examine decision-making issues in various organizations and industries based on various criteria, alternatives, and objectives and make decisions with greater reliability. The use of fuzzy techniques and concepts in MCDM methods and their mathematical relationships makes it possible to consider complexities and uncertainties in decisions related to various issues and it can lead to better and more realistic decisions. In this paper, the simplified best-worst method (SBWM), which is one of the methods based on pairwise comparisons, has been developed using triangular fuzzy numbers (TFNs) to propose a fuzzy extension of SBWM (F-SBWM). Triangular fuzzy numbers in different symmetric and asymmetric forms have widely been used in MCDM approaches and pairwise comparisons. It is noteworthy that symmetric numbers are used when we are using equal division of the domain due to an increased ambiguity and lack of information. The proposed approach as a simplified fuzzy MCDM method helps managers and decision makers in various industries to solve decision-making problems under uncertainty without the need for complex calculations, specialized skills, and software packages. To check the feasibility and applicability of the proposed approach, two numerical examples and a computational experiment with real data are presented, and the results are analyzed and discussed. Furthermore, to check the robustness of the results obtained from the proposed approach, sensitivity analysis and comparison of methods have been performed.

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

All over the world, including Poland, authorities are taking steps to increase consumer interest in electric vehicles and sustainable transport as a way to reduce environmental pollution. For this reason, the electric vehicle market is dynamically and constantly developing, more and more modern vehicles are introduced to it, and purchases are often subsidized by the government. The aim of the article is to analyse the A–C segments of the Polish electric vehicle market and to recommend the most attractive vehicle from the perspective of sustainable transport. The aim of the research was achieved with the use of three multi-criteria decision aid (MCDA) methods, which deal well with the uncertainty and imprecision of data that occur in the case of many different parameters of electric vehicles. In particular, the following methods were used: the fuzzy technique for order of preference by similarity to ideal solution (TOPSIS), the fuzzy simple additive weighting (SAW) method, and the new easy approach to fuzzy preference ranking organization method for enrichment evaluation II (NEAT F-PROMETHEE II). Electric vehicle rankings obtained using each method were compared and verified by stochastic analysis. The conducted analyses and comparisons allowed us to identify the most interesting electric vehicles, which currently appear to be the Volkswagen ID.3 Pro S and Nissan LEAF e+.

Forecasting and Assessment of the Energy Security Risk in Fuzzy Environment

Energy security is of key importance for states and international organizations. An important issue in energy security is the assessment of current and future energy security methods. While the assessment of the current methods is relatively easy, since it is based on recent information, the assessment of the future methods is burdened with uncertainty and is therefore much more difficult. Therefore, the aim of the article is to develop a new approach for assessing current and future energy security issues based on a complex security index, supported by the computationally transparent fuzzy multi-criteria decision analysis (MCDA) method. The use of the fuzzy MCDA methods allows one to capture the uncertainty of assessments and forecasts, and the forecasts themselves were based on the Holt’s method; the international energy security risk index (IESRI) was used as the source of the data to generate the forecasts. The research compared two data sources for forecasts (IESRI categories and metrics) and two methods of forecast fuzzification. As a result, the forecasted assessments and rankings of energy security for the 2020–2030 period were obtained. On the basis of these forecasts, general trends shaping energy security were also indicated.