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DOI: 10.15862/08FAOR425 (https://doi.org/10.15862/08FAOR425)

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Mirgorodskaya M.G., Dolgov K.V., Anichkina O.A. Conceptual foundations of the application of economic and mathematical modeling in the processes of modernization of industrial enterprises. Russian journal of resources, conservation and recycling. 2025; 12(s4). Available at: https://resources.today/PDF/08FAOR425.pdf (in Russian). DOI: 10.15862/08FAOR425

Conceptual foundations of the application of economic and mathematical modeling in the processes of modernization of industrial enterprises

Mirgorodskaya Marina Gennadyevna
K.G. Razumovsky Moscow State University of Technologies and Management (the First Cossack University),
Moscow, Russia
E-mail: mgm2502@mail.ru

Dolgov Konstantin Vadimovich
K.G. Razumovsky Moscow State University of Technologies and Management (the First Cossack University),
Moscow, Russia
E-mail: mar.levchenko2010@yandex.ru

Anichkina Olga Aleksandrovna
K.G. Razumovsky Moscow State University of Technologies and Management (the First Cossack University),
Moscow, Russia
E-mail: F-1980@yandex.ru

Abstract. The modern transformation of industrial production is characterized by fundamental changes in approaches to the management of production processes, caused by the deployment of the fourth industrial revolution and the introduction of digitalization technologies within the framework of the concept of «Industry 4.0». The subject of the study is economic and mathematical methods and models as a tool for optimizing production processes and increasing the efficiency of industrial enterprises in the context of digital transformation. The article considers the theoretical and methodological foundations of using linear and nonlinear programming, dynamic modeling, Goldratt’s theory of constraints of systems, machine learning methods and neural network technologies to solve problems of modernization of production systems. The analysis of the evolution of scientific approaches to economic and mathematical modeling demonstrates the transition from deterministic optimization models of classical Kantorovich-Dantzig linear programming to stochastic models using artificial intelligence methods and digital twins of production processes. The key results of the study include systematization of methodological approaches to the integration of economic and mathematical models into industrial enterprise management systems, identification of patterns of digitalization influence on the efficiency of optimization models, development of a conceptual model of a multi-level system of economic and mathematical modeling of production processes. Scientific novelty lies in the synthesis of classical methods of mathematical programming with modern machine learning and predictive analytics technologies to create hybrid models for managing the modernization of industrial enterprises. The practical significance is determined by the possibility of applying the developed conceptual provisions to increase the efficiency of production processes by 15–30 %, reduce operating costs by 20–25 % and accelerate the processes of making management decisions in conditions of high uncertainty of the external environment.

Keywords: economic and mathematical modeling; modernization of industrial enterprises; linear programming; theory of constraints; digital transformation; production optimization; machine learning; digital twins; neural network technologies; predictive analytics

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