from the conferences organized by TANGER Ltd.
The paper deals with the issue of decision support in maintenance management and its optimization. Today, there are many modern tools and methods to optimize operational operations and maintenance itself. The aim of the paper is to describe the creation of a technical and economic model of metallurgical equipment for the optimization of preventive maintenance. At present, attention is paid mainly to technical parameters, in contrast to economic indicators. Maintenance costs make up a large part of the operating costs of most manufacturing companies. Maintenance is often considered an economic burden, which only consumes considerable funds and does not create any on its own. Based on the reliability analysis of the data obtained in this way, a reliability model of the given production unit will be created, enabling in the on-line mode the prediction of the technical condition of the metallurgical equipment and possibly their defined machine nodes. The application of artificial intelligence methods will create software tools for computer support of organization and maintenance management, for operational production management in connection with systems for spare parts warehouse management, etc. The principle of the model will be demonstrated on the strategy of periodic preventive maintenance. The so-called combined approach will be applied for the solution of the model and the subsequent optimization of maintenance, which means that not only artificial intelligence methods are used to solve a certain problem, but a connection with some other method.
Keywords: Metallurgy, maintenance control, neural-genetic system,© This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.