Contemporary education in higher education institutions has a goal to provide necessary competences determined by legislative demands, science trends and job market needs to graduated students intending to strengthen their employability. The main idea of this research is to assess the level of students’ competences for lifelong learning (LLL). Since it is a very complex task, it is characterized by a lack of information and the vagueness of the inherently subjective nature of human thinking. The uncertain values of the assessed level of students’ competences for LLL are represented by linguistic expressions which are modeled by the interval type 2 trapezoidal fuzzy numbers. Ranking of the level of students’ competences for LLL is stated as a fuzzy multi-criteria optimization task. The aggregation of students’ opinions into unique marks is given by using the proposed fuzzy Delphi technique. The rank of students’ competences for LLL is given by using the proposed fuzzy Simple Additive Weighting (FSAW). The order of measures that should be delivered to improve students’ competences is based on the obtained rank. The model is tested and discussed on the real data collected from a significant sample of the students with a proposal of future directions of the research.

In terms of uncertain business conditions, the ability of an enterprise to bounce back after severe disruptions, or simply resilience, may be seen as one of the major features needed to sustain successful business operations. This research has the objective of proposing an algorithm for the organizational resilience assessment in industrial companies and conducting an analysis of the relationship between the organizational Resilience Factors and Key Performance Indicators recovery times. As the variables that are an integral part of the research are exposed to a high degree of uncertainty, they are modeled using fuzzy set theory. The methodology used for the research is an enhanced fuzzy Delphi, where the fuzzy geometric mean is employed as an aggregation operator. The relationship between the organizational resilience factors and Key Performance Indicators’ recovery time is based on the correlation analysis. The proposed model is based on real data from one complex industrial enterprise. The main finding of the research is that calculations indicate a significant negative correlation between treated variables.