OPTIMIZATION OF THE PROCESS OF CARGO DELIVERY OF AGRO-INDUSTRIAL COMPLEX THROUGH THE INTRODUCTION OF NEURAL NETWORKS
Apatenko A.S. Nekrasov S.I. Sevryugina N.S. Kozhukhova N.I. Begimkulova E.A.
2025National Academy of Sciences of the Republic of Kazakhstan
News of the National Academy of Sciences of the Republic of Kazakhstan, Series of Geology and Technical Sciences
2025#2025Issue 258 - 69 pp.
Studies have been carried out in the field of improving the quality of cargo movement of mining enterprises. The paper substantiates the introduction of a control system module using digital tools based on neural network algorithms as a tool for optimizing the process of cargo delivery in the mining sector. Goals and objectives. Formation of a model for the functioning of the mining sector, in terms of real-time regulation of cargo transportation, by developing a logistics architecture for the use of digital tools of neural networks to optimize the process of cargo delivery, including the automation of warehouse operations, demand forecasting, routing and delivery planning. The methods include analytical research, probabilistic estimation method, simulation modeling, modal analysis, modeling of digital architectures, neural network programming techniques, basic theories of solving optimization solutions, theory of transport logistics. Results of the study – the created neural network includes a simulation model, a cloud platform for permanent storage of a bank of solutions. Thanks to functional and cloud resources, when managing the process of product delivery, it is possible to take into account the technical characteristics of the vehicle, the method of cargo placement, optimize the logistics of delivery and distribution for the regional consumer. A cloud database was created, which made it possible to transfer the neural network training process offline. An interface for information processing has been developed. Conclusions: the use of the data bank allows you to effectively solve the problem of choosing optimal solutions, accumulate an information resource for adaptation to real transportation conditions, taking into account the variability of influencing factors.
cargo , efficiency , logistics , mining sector , neural networks , resource management
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Russian State Agrarian University – Moscow Timiryazev Agricultural Academy, Moscow, Russian Federation
Moscow Polytechnic University, Moscow, Russian Federation
V.G. Shukhov Belgorod State Technological University, Belgorod, Russian Federation
ALT University named after Mukhametzhan Tynyshbaev, Almaty, Kazakhstan
Russian State Agrarian University – Moscow Timiryazev Agricultural Academy
Moscow Polytechnic University
V.G. Shukhov Belgorod State Technological University
ALT University named after Mukhametzhan Tynyshbaev
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