Informatica

Nowadays, it is agreed that fuzzy sets are suitable for capturing and representing the concept of vagueness and uncertainty, and various fuzzy reasoning systems are being developed based on them. Researchers have proposed fuzzy set extensions to improve the performance and accuracy of these systems. The research questions arise regarding how fuzzy sets have evolved and what the main trends in their evolution are. To address these questions, our research presents a chronological and bibliometric analysis of fuzzy sets based on papers extracted from the Web of Science database. The main findings and contributions have been identified, systematized and visualized in a fuzzy set keyword map of 65 fuzzy set extensions. These extensions are primarily used for decision-making, reasoning, and prediction, particularly in the context of digital transformation, by integrating digital technologies into all areas of business, transforming operations and enhancing value delivery to customers. As organisations increasingly adopt digital technologies, the need for robust frameworks to manage uncertainty becomes critical. The main trends indicating the directions of fuzzy sets development, an overview of the variety and popularity of fuzzy sets over the years, and the impact of countries engaged in fuzzy set research are also identified and reported. The results support researchers and practitioners working on fuzzy sets and their applications by providing valuable insights into the fuzzy set topic, its existing extensions, and, more generally, to any field of investigation where fuzzy sets are relevant, particularly in the realm of digital transformation.

Modelling the reliability information in decision making process is an important issue to inclusively reflect the thoughts of decision makers. The Evaluation Based on Distance from Average Solution (EDAS) and Analytic Hierarchy Process (AHP) are frequently used MCDM methods, yet their fuzzy extensions in the literature are incapable of representing the reliability of experts’ fuzzy preferences, which may have important effects on the results. The first goal of this study is to extend the EDAS method by using Z-fuzzy numbers to reinforce its representation ability of fuzzy linguistic expressions. The second goal is to propose a decision making methodology for the solution of fuzzy MCDM problems by using Z-fuzzy AHP method for determining the criteria weights and Z-fuzzy EDAS method for the selection of the best alternative. The contribution of the study is to present an MCDM based decision support tool for the managers under vague and imprecise data, which also considers the reliability of these data. The applicability of the proposed model is presented with an application to wind energy investment problem aiming at the selection of the best wind turbine. Finally, the effectiveness and competitiveness of the proposed methodology is demonstrated by making a comparative analysis with the Z-fuzzy TOPSIS method. The results show that the proposed methodology can not only represent experts’ evaluation information extensively, but also reveal a logical and consistent sequence related to wind turbine alternatives using reliability information.

In some cases of using multi-criteria decision making methods for solving real-world problems ratings of alternatives cannot be determined precisely, and that is why they are expressed in the form of intervals. Therefore, the aim of this paper is to extend the MOORA method for solving decision making problems with interval data. By extending the ratio system part of MOORA method, an algorithm to determine the most preferable alternative among all possible alternatives, when performance ratings are given as intervals, is presented. Finally, an example is shown to highlight the proposed procedure, at the end of this paper.

The exact solution of the reliability of structures under stochastic loading is generally difficult, and various approximate methods have been developed. The most popular are the linearization method, the Monte-Carlo method and its numerous variants. In this paper new modification of the Monte-Carlo method based on asymptotical expansion is examined. Results of mathematical simulation are given.