Mathematical model of data preparation for operational planning in heavy engineering enterprises

This article is devoted to solving the problem of preparing data for the operational planning of heat treatment operations using the example of industrial enterprises in St. Petersburg. Solving the problem of planning heat treatment operations and, accordingly, preparing data for this, is relevant, since in rare cases, operational planning is used at enterprises of the Russian Federation. But even if it is employed, it is only used for drawing up a planned schedule of work, for the implementation of which it is necessary to restructure the existing organization of the production process. Planning is often done “manually” using Microsoft Project or Microsoft Excel. The mathematical model presented in the work has a number of assumptions to reduce the computational complexity of the problem under consideration. The aim of the study is to develop a model and an algorithm for operational scheduling of the production process. To solve the problem, an algorithm is proposed that allows to form orders in such a way that the necessary restrictions and conditions for their simultaneous processing are fulfilled. The proposed algorithm was tested on realistic data in the 1C: MES “Operational production management” information system (hereinafter 1C: MES), which made it possible to test the developed algorithm for the correctness and implementation of the specified restrictions. The developed mathematical model and algorithm for preparing data for planning are implemented in 1C: MES and are used at one of the heavy engineering enterprises in St. Petersburg. With the help of the obtained algorithm, the enterprise managed to increase the efficiency of the existing production equipment, in particular, thermal furnaces. Also, the information on groups of blanks for performing heat treatment operations is used to plan the loading of other equipment in the workshops. The developed model and algorithm can be used at other enterprises with heat-treatment furnaces and queues of blanks for processing in furnaces. This will reduce the storage of blanks and increase the efficiency of the use of warehouse or workshop premises. The directions of further research can be the study of the applicability of the developed tools for other technological operations at the enterprise and the integration of the developed algorithm into the general system of operational scheduling of production.