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The article examines models of indicative planning of control indicators of enterprises within the financial and industrial consortium of the electronic industry. The consortium may include enterprises of all types of ownership and organizational and legal forms, responsible for the implementation of plan tasks in accordance with the agreement on joining the financial and industrial consortium. The need to construct mathematical models for planning the development of enterprises, taking into account the possibilities of changing relationships, makes the study relevant. The main objective of the study is to develop models for assessing the control indicators of planned activities (control figures) of enterprises based on a comparison of their plans and available incentives. The scientific result of the research is the development of evolutionary-simulation models that ensure the selection of control indicators that take into account the capabilities and characteristics of enterprises, the ability to dynamically change their list, as well as forms of incentives. The models allow to produce control actions as a level of reward or punishment for legal entities depending on the extent to which the planned task has been fulfilled or not fulfilled. The reasons for the considering the production of electronic components to be “non-market” goods and services have been identified. An analysis of three most typical scenarios of the demand to production ratio was conducted: the product is in short supply, i. e. the demand is unlimited; the demand is comparable to the production volume; the demand is significantly less than the production volume. An example of planning enterprise indicators using control figures is given. The practical significance of the proposed models lies in the possibility of their use for developing recommendations for coordinating control indicators in a group of enterprises.
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This study presents a mathematical hiring model based on the integration of graph methods and blockchain verification. Unlike traditional recruitment approaches, the proposed model incorporates candidates’ network characteristics, such as degree centrality, betweenness centrality, and PageRank, while also utilizing trust scores (Trust Score) to predict hiring success. A cluster analysis of interactions between candidates, employers, HR platforms, and certification centers was conducted, revealing key patterns in professional network formation. The scientific novelty of this research lies in the development of a comprehensive personnel selection algorithm that, in addition to standard HR metrics, employs graph-based indicators and blockchain verification mechanisms to enhance transparency and accuracy in hiring. For the first time, a methodology for calculating trust scores based on network analysis has been proposed, allowing for a more objective decision-making process in HR analytics. The results indicate that the application of graph methods in recruitment reduces hiring time by 32 %, decreases the likelihood of mismatched hires by 18 %, and improves candidate success prediction accuracy to 85 %. These findings confirm the potential of integrating graph neural networks and blockchain verification in automated HR systems.
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№ 3(117)
30 june 2025 year
Rubric: Algorithmic efficiency
The author:
Bulygina O. V. Купить статью
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Many problems requiring finding optimal solutions may arise in the process of building and managing socio-economic systems. The use of traditional methods of deterministic search is limited by the presence of nonlinear relationships between elements, conflicting interests of agents, hard and soft constraints, and uncontrollable environmental factors. For such problems, it is recommended to use stochastic methods that take into account the random nature of variables in the objective functions and constraints, which are capable of finding acceptable solutions in an acceptable time even under conditions of information uncertainty. In recent years, population metaheuristics, which simultaneously explore several solutions, have undergone significant development. Interest in these methods is due to their suitability for non-convex solution spaces, the absence of conditions on the type of the objective function, the ability to take into account hard and soft constraints, and high convergence. However, according to the no free lunch theorem, there is no metaheuristic that can solve all optimization problems. The article shows that the choice of a specific algorithm is based on the conceptual and mathematical formulation of the optimization problem and the specifics of the implementation of search operations. Despite their subject independence and high flexibility, in practice such algorithms do not provide acceptable results when used in their canonical form. In such situations, they should be modified to suit the specifics of the problem being solved. The article proposes to take into account uncertainty (incompleteness, inaccuracy, unreliability, ambiguity of incoming data) by hybridizing the selected metaheuristics with different methods of fuzzy logic used for identification, evaluation and aggregation of information NON-factors. The article also formulates recommendations for choosing an approach to reducing the set of optimization criteria for the case of multi-objective problems. The use of hybrid algorithms built on the basis of fuzzy logic and swarm intelligence methods will improve the stability and achieve the adequacy of optimization models.
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