The purpose of research is to evaluate the factors influencing the competitiveness of machine-building enterprises and to develop a methodology for fuzzy cognitive mapping (FCM) to analyze these factors.Methods. Data analysis and modeling methods, including FCM, were used in this study to formalize the relationships between factors and their influence on the competitiveness of machine-building enterprises. Data related to factors affecting competitiveness, such as technological level, product quality, price level, innovation potential, and managerial experience, were analyzed.Results. The study resulted in the development of an FCM methodology to analyze the relationships between factors and their influence on the competitiveness of machine-building enterprises. The factors influencing competitiveness, including technological level, product quality, price level, innovation potential, and managerial experience, were evaluated. Key factors with the greatest impact on competitiveness were identified, and the degrees of influence of each factor on competitiveness were established using a scale to formalize the strength of influence.Conclusion. The results of the study demonstrate that the FCM method is an effective tool for analyzing the competitiveness of machine-building enterprises. It allows for uncertainty and vagueness in the input data and provides a better understanding of the relationships between conceptual variables. The developed FCM methodology can be used to make informed decisions to improve enterprise competitiveness. Overall, the study confirms the importance of analyzing factors influencing competitiveness and demonstrates that FCM can help to represent a complex system and its relationships in a more visual and understandable form. This can be useful for enterprise management in making informed decisions to improve company competitiveness and increase market share.

Purpose of reseach. Development of a forecast model of energy consumption and assessment of factors influencing its consumption. The obtained forecast estimates of energy consumption will improve the quality and efficiency of management decisions at all levels of administrative management.Methods. The article presents an analytical review of the existing methods of cognitive modelling and forecasting of electric power consumption, the description of the software implementation of the information-computing system that allows to make a forecast of electric power consumption by the population of the administrative-territorial formation. The approach to the description of factors of electric power consumption by both population and various branches of national economy, as well as organisations engaged in rendering various services has been proposed. Special software has been developed, which allows to obtain model results of electric power consumption in an automated mode, to carry out factor analysis of power consumption. The experimental verification of the work of the programme of cognitive modelling and forecasting of electric power consumption by the population of Lgovsky district of Kursk region is given. The developed software also makes it possible to evaluate the adequacy of the obtained results and promptly adjust the model parameters.Results. As a result of the research a fuzzy cognitive map of energy consumption for a municipal entity was developed. The concepts of the subject area describing the influence of various groups of factors on the level of electric energy consumption were identified. Forecast estimates of electricity consumption were obtained, which were based on the data for the retrospective period. Adequacy indicators based on the calculation of statistical criteria are determined for the obtained estimates.Conclusion. The results of the study have shown that the combination of cognitive and statistical methods allows to achieve an adequate solution when solving the problem of energy consumption forecasting.

Purpose of research. The main purpose of this work is to improve the quality and efficiency of managerial decisionmaking based on the development of a method for assessing and forecasting economic risks of an enterprise. This method is based on data mining technology.Methods. The paper uses methods of panel data processing and analysis, for which a mathematical model for predicting the level of competitiveness of an enterprise was built, as well as a model for predicting economic risks of an enterprise based on combining several methods of data mining: clustering of merging panel data for assessing economic risks of an enterprise and the method of merging fuzzy correlation for statistical analysis of panel data.Results. As a result of the application of the developed method, quantitative assessments of the level of competitiveness and economic risks of the enterprise were obtained. Based on the obtained quantitative assessments of the level of competitiveness and the level of economic risk, a cluster analysis of enterprises in some industry was carried out. The developed methods have high accuracy in predicting economic risks of enterprises, improve the capabilities of data mining and combining information about economic risks of enterprises, which increases the competitiveness of enterprises.Conclusion. A method of forecasting economic risks of an enterprise based on data mining technology has been developed. Weighted estimates of spatial features of panel data were obtained, which allow to obtain integral estimates of the economic risks of the enterprise and the level of competitiveness of the enterprise. A model for the analysis of fuzzy rules of semantic features of panel intelligent data analysis of the assessment of economic risks of the enterprise is proposed. The analysis shows that the developed method has high accuracy and better protection against interference when predicting data.

Purpose of research. The main goal of this work is to increase the efficiency of automated game learning based on cognitive modeling. Based on the methodology of system analysis and cognitive modeling of weakly structured situations, the structure of a gaming automated training complex is proposed, which can be used in training personnel in various subject areas. In our case, staff training was simulated in stereotypical and non-stereotypical situations.Methods. This work is based on the general provisions of systems theory and system analysis, mathematical graph theory (which is based on cognitive modeling). The main tool of cognitive modeling was the construction of fuzzy cognitive maps of Silov. A modification of the algorithm for calculating the main system indicators of a fuzzy cognitive map was proposed. Game modeling, based on business games, was used for automated learning. The concept of an operational game was introduced, then modeling of the development of some unfavorable situation was carried out using fuzzy cognitive maps..Results. he main result of this work is the method of cognitive modeling of information support for game-based automated learning. Based on the developed methodology, a game simulation simulation of the operational game "House Fire" was carried out, which was based on the construction of a fuzzy cognitive map, for which the main quantitative system indicators of mutual influence, consonance and dissonance were calculated.Conclusion: the developed methodology allows for game modeling of unfavorable (including emergency) situations, which in the future will ensure adequate behavior of students in real situations.

The paper proposes a methodological approach to the development of a model for assessing the investment attractiveness of innovative projects based on the provisions of fuzzy logic and set theory. Methods and models for evaluating the investment project effectiveness are described. The limitations of existing methods due to the uncertainty of the external environment are presented. An example of a quantitative assessment of a specific innovative project investment attractiveness is given.

Purpose of reseach is to improve the operation of technical systems or performance of socio-economic systems by developing a fuzzy system for selecting a supplier of inventories in logistics companies. The relevance of the study is determined by the absence of a system approach to the formation of a procurement logistics strategy in the overwhelming majority of companies, which makes the operations management of the analysis and evaluation process difficult. When making decisions concerning selecting a supplier, based on the analysis of a variety of sources, various mathematical methods and techniques are used; nevertheless these methods and techniques have a number of drawbacks that do not allow a company to solve the set task with the required effectiveness. Methods. Since the original methods in solving the task of assessing and managing the attractiveness of an object are incomplete and noе clear, the method applied is based on the systems of fuzzy logic inference. The basis of the proposed method is a scheme of the direct fuzzy inference. It is modified by introducing an ordered weighted averaging operator to aggregate the outputs of the production rules. At the first stage of the derivation, the membership functions defined for the input variables are applied to their actual values for the subsequent determination of the degree of truth for each premise of each rule. Then, the calculated truth value for the premises of each rule is applied to the inferences of each rule. This leads to the formation of a fuzzy subset, which is assigned to each output variable for each rule. As a rule of logic inference, we use min operation. At the next stage, the fuzzy value of the membership function of the output variable of each rule is reduced to a crisp type by applying the fast centroid method. To aggregate the output of each production rule, taking into account the degree of their importance for the decision-maker, it is proposed to use the Ronald R. Yager’s ordered weighted averaging (OWA) operator. Results. Ordered weighted averaging and the subsequent ranking of the pairs of alternative firms made it possible to create a list of supplying companies corresponding to the objective ranking. Conclusion. A model verification of the ranking of suppliers of packaging materials for the firms of finished products was performed. The results of an experimental study demonstrate the prospects of the proposed approach taking into account the mutual influence of the indicators.

The article considers the application of the network programming method to the solution of the discrete problem of minimizing the cost of the project for a given duration of its implementation. The essence of the method is that the target function and the restriction in the scheduling problem can be represented as a superposition of simpler functions. This representation is convenient to depict in the form of a network, at the lower level of which there are vertices corresponding to variables (network inputs), intermediate vertices correspond to the functions included in the superposition, and the final vertex (output) corresponds to the original function. Calendar planning tasks are very common in practice and at the same time belong to the class of NP-difficult. This makes the development of algorithms for their solution actual. The paper describes two basic algorithms for solving the problem for the cases of independent and sequential works. More complex cases (tree-type network and an aggregated network) can be represented as a combination of these cases and solved based on sequential application of basic algorithms. As an example of a production network is given a network of the type "Assembly with a components". For it the method which consists in definition of a set of works which fixing of duration leads to one of the cases considered above (tree-type network or aggregated network) is offered. Next all possible options for fixing the duration of the work of the selected set and the solution of the problem for each option are considered. The best of all the options is chosen. The algorithms proposed in paper may be useful in the of the project management, particularly in solving scheduling tasks.