05.13.10 Management in social and economic systems
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MULTI-CRITERIA ANALYSIS OF ALTERNATIVES IN SOLVING HUMAN RESOURCES MANAGEMENT PROBLEMS
05.13.10 Management in social and economic systems
DescriptionHuman resources have recently reasonably gained more and more importance. Today, along with material, intellectual, informational and financial resources, they affect the efficiency of enterprises and organizations. Competent assessment of human resources, a clear understanding of means of interaction with staff and developing human potential are the basis for the effective work of both human resources departments and organizations as a whole. The complexity of assessing human resources necessitates the development of a toolkit, the use of which will simplify it and ensure that one receives the most accurate advice and assistance in making management decisions. A promising direction for the implementation of the designated toolkit may be the development of a decision support system, within which, among other things, the possibility of a multi-criteria analysis of alternatives will be available. Due to the fact that there are no methods for multi-criteria analysis of alternatives intended solely for assessing human resources, it is necessary to conduct a thorough analysis, the main purpose of which is to identify the most suitable basis for further adaptation and development. After conducting preliminary studies, the TOPSIS, MAUT, AHP and ELECTRE methods were chosen as the most promising for solving the problem
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05.13.10 Management in social and economic systems
DescriptionThe article discusses modern approaches to the implementation of the migration of virtual machines between different virtualization platforms. A comparative characteristic of virtual migration tools is given. Conclusions are drawn on the expediency of applying different approaches depending on the task facing migration and available resources. The author presents a technique for migrating virtual machines from VMware vSphere virtualization platform to Microsoft Hyper-V virtualization platform, which allows to increase the speed and reliability of the migration process and significantly save on operating costs of the company
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05.13.10 Management in social and economic systems
DescriptionThe article considers the most used methods and means of knowledge extraction taking into account the quality assessment of models in decision support systems. In scientific and practical terms, the possibilities of joint effective use of expert systems, data mining (IAD) and machine logical inference (MLV), which provides deeper data processing, taking into account the significant differences between databases (DB) and knowledge bases (BZ). DB is a unit of information unrelated to each other information, while BZ – not only related to each other, but also with the concepts of the world, which makes it possible to solve complex multi-criteria problems in various subject areas. Currently, increasing attention is paid to non-network technologies that have the ability to simulate nonlinear processes, work with noisy data, as well as the ability to learn and self-study, extracting essential features from the incoming information. At the same time, the integration of neural network technologies and artificial intelligence models into a single hybrid system together with the methods of logical inference in the form of a hierarchical sequence of the "If-then" rules structure significantly improves the understanding of the studied process and the quality of presentation of the result. Nevertheless, these methods and means of knowledge extraction are insufficient if the fuzzy linguistic inference mechanism is not used. The basic characteristic of fuzzy sets is the membership function, which is a generalized characteristic of a normal set. To set this feature, we use three types of shapes – triangular, trapezoidal and Gaussian type and two main procedures – phasefication and de-phaseification which is considered by the example of the method of Mamdani. Along with the stated most promising direction in this area is the adaptive gain algorithm called AdaBoost, where the limitation of the gain due to the filtering is to apply the subsampling circuit which has the normal contour of batch training, reusable training data. This provides an opportunity to work with weak models, and in the conditions of hybridization causes efficiency increase, strengthens the classifiers united in the "Committee". Each next set of classifiers is built on objects incorrectly classified by previous sets. AdaBoost is sensitive to data noise and emissions and is less susceptible to retraining, which can significantly reduce the number of examples and obtain better output in the DSS