In multiple-criteria decision making/aiding/analysis (MCDM/MCDA) weights of criteria constitute a crucial input for finding an optimal solution (alternative). A large number of methods were proposed for criteria weights derivation including direct ranking, point allocation, pairwise comparisons, entropy method, standard deviation method, and so on. However, the problem of correct criteria weights setting persists, especially when the number of criteria is relatively high. The aim of this paper is to approach the problem of determining criteria weights from a different perspective: we examine what weights’ values have to be for a given alternative to be ranked the best. We consider a space of all feasible weights from which a large number of weights in the form ofn−tuples is drawn randomly via Monte Carlo method. Then, we use predefined dominance relations for comparison and ranking of alternatives, which are based on the set of generated cases. Further on, we provide the estimates for a sample size so the results could be considered robust enough. At last, but not least, we introduce the concept ofcentral weightsand the measure of its robustness (stability) as well as the concept of alternatives’multi-dominance, and show their application to a real-world problem of the selection of the best wind turbine.

We forecast 1,000,000 COVID-19 cases outside of China by March 31st, 2020 based on a heuristic and WHO situation reports. We do not model the COVID-19 pandemic; we model only the number of cases.

Rating scales are used to elicit data about qualitative entities (e.g., research collaboration). This study presents an innovative method for reducing the number of rating scale items without the predictability loss. The “area under the receiver operator curve method” (AUC ROC) is used. The presented method has reduced the number of rating scale items (variables) to 28.57% (from 21 to 6) making over 70% of collected data unnecessary. Results have been verified by two methods of analysis: Graded Response Model (GRM) and Confirmatory Factor Analysis (CFA). GRM revealed that the new method differentiates observations of high and middle scores. CFA proved that the reliability of the rating scale has not deteriorated by the scale item reduction. Both statistical analysis evidenced usefulness of the AUC ROC reduction method.

Areas of experience allow for the acquisition and consolidation of both existing knowledge and skills. These are significant factors in the training of staff members for companies in the Industry 4.0 area. One of the currently available modern tools used in the teaching process is virtual reality (VR) technology. This technology, due to its high level of immersion and involvement of the different senses, and the need to focus on the performed activities, allows one to develop skills in solving various tasks and problems. The extended VR environment enables the creation of diverse teaching scenarios adapted to the needs of industry. This paper presents the possibility of building training scenarios in the field of digital techniques. The software solution, developed and presented by the authors, uses elements of computer game mechanics and is designed to familiarize students with the idea of digital circuits, their construction, logical implementation and application. This paper also presents a comparison of the features of different forms of education used in teaching digital techniques, as well as a comparison of these forms, from the point of view of the student and his/her perceptions.