To date, the level of development of metallurgical production imposes high
requirements to production management systems and the quality of steel products, due to the
development of information technology. Metallurgical production from the point of view of
management and multistage character of production is a complex, large system with different
characteristics of subsystems and elements of processing. Traditional methods of modeling for
the management of such systems are ineffective, as one of the main problems is the choice of
optimal management decisions taking into account current situations and restrictions on
changes in the values of technological parameters. In this regard, there is a need to develop a
methodology that would improve the management of technological systems, organize decisionmaking support in the face of uncertainty, to ensure the speed and accuracy of information to
improve the quality of metal products and technical and economic indicators and the reliability
of production. The application of new methods of analysis of complex production systems of
information processing, management improvement and decision-making will improve the
efficiency of enterprises and reduce the proportion of low-quality products. The aim of the
study is to use new methods of analysis of complex production systems of information
processing, improvement of management and decision-making, which will improve the
efficiency of enterprises and reduce the share of low-quality products. As a result, the
formalization of the problem of integrated management of output indicators of product quality,
taking into account the uncertainty of internal factors of metallurgical production. The
software implementation of the algorithms will increase the efficiency of decision-making by
determining the optimal technological parameters from the range of permissible values.
1. Bestuzhev-Lada I. V. working book on forecasting. M.: Mysl, 1982. 430 p.
2. Kabulova E.G. Mathematical modeling of production processes in
metallurgy. Stary Oskol: TNT Publishing house, 2014. - 131 c.
3. Mesarovich M., Mako D., Takahara I. theory of hierarchical multilevel
systems. M.: Publishing House "Mir", 1973. - 344 c.
4. Rozhkov I. M., Vlasov S. A., Malko, G. N. A mathematical model for the
choice of rational technology and quality control of steel. M.: Metallurgy,
1990. - 398 p.
5. Baldwin J.F., Guild N.C. Comparison of Fuzzy Sets on the Same Decision
Space // Fuzzy Sets and Systems. - 1879. - Vol. 2. - № 3. - Pp. 231-231.
6. Gitman M.B., Trusov P.V., Fedoseev S.A. On optimization of metal forming
with adaptable characteristics // Journal of Applied Mathematics and
Computing. - 2000. - Vol. 7. - No. 2. - Pp. 387-396.
7. Matsko I.I. Adaptive fuzzy decision tree with dynamic structure for automatic
process control system o of continuous-cast billet production // IOSR Journal
of Engineering. - 2012. - Vol. 2. - № 8. - Pp. 53-55.
8. Merkuryeva G. Computer Simulation in Industrial Management Games //
Proc. of MlM 2000. IFAK Symp. on Manufacturing, Modeling, Management
and Control. University of Patras, Rio, Greece. - 2000. - Pp. 69 -73.
9. Michalska H., Ellis J.E., Roberts P.D. Joint coordination method for the
steady state control of large-scale systems // Int. J. Syst. Sci. - 1985. - № 5. -
Pp. 605 - 618.
10. Saati T. and Kerns K. Analytical planning. Organization of systems. M: Radio
and communication, 1991. - 224 p.
11. Saati T.L. Decision-making. Method of the analysis of hierarchies. M.: Radio
and communication, 1993. - 278 p.
Kabulova Evgeniya Georgievna
Candidate of Technical Sciences, Associate Professor
Email: evgenia791@mail.ru
Stary Oskol Technological Institute named after A.A. Ugarov (branch) NUST «MISIS»
Stary Oskol, Russian Federation
