Self-Teaching Archetype for Binary Ranking in Production Management

The problem of managing a two-level organizational and production system is considered, at the upper level of which there is a decision maker, and at the lower level there is an active element that produces products. The case is analyzed when, in order to interest an element in producing the maximum possible volumes of products in the event of random interference, the decision maker assigns one of two ranks to the active element. The situation was studied when the decision maker does not know these volumes, and the element can achieve better results for itself in such ranking by manipulating product output. The game of the decision maker and the active element choosing the output in such a way as to increase its own objective function is studied. Procedures for estimating the parameter and generating release plans, as well as assigning ranks, are presented. An archetype of self-teaching for binary ranking of production has been formed, including a set of assessment, production planning and ranking procedures. It has been proven that the use of this archetype is enough to interest the active element in increasing production volumes in the event of any random interference. In this case, estimates of the output parameter converge to the optimal estimate. The application of the self-teaching archetype for binary ranking of product output is considered using the example of managing the repair of passenger cars in a car repair corporation.