ADOPTION OF DECISIONS FOR CONTROL OF COMPLEX MULTI-STAGE TECHNOLOGICAL SYSTEMS UNDER CONDITIONS OF UNCERTAINTY

Abstract

Today metallurgical production from the point of view of management and multistage character of production is complex, big system with various features of functioning of subsystems and elements. Traditional methods 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 limitations on changes in the values of technological parameters. One of the problems arising in the management of complex technological systems of metallurgical production is the heterogeneity of a large amount of data, which complicates the process of making effective and operational decisions in the management of production. Adequate decision-making by the expert is connected with the need to aggregate various kinds of information at different levels of the hierarchy. In addition, the operation of real complex systems of metallurgical production takes place in conditions of uncertainty of information, and to implement effective management, to organize decision support, to ensure the efficiency and accuracy of information to improve the quality of metal products and technical and economic indicators of production in this case is not possible. In this connection, the paper proposes a model of integration of heterogeneous information under uncertainty, which will take into account the measure of importance not only of individual values of technological parameters at a certain stage of production, but also a set of such parameters through the use of fuzzy measures in the integration of data. This model will improve the accuracy of determining the required values of technological parameters by taking into account all stages of production, technological operations, as well as through the use of data aggregation at each stage. The peculiarity of the developed model is the possibility of applying corrective procedures for the sequential adaptation of membership functions of fuzzy parameters.