The relevance of modern information technologies and data mining methods as the main tools for implementing advanced maintenance strategies that take into account the actual condition of machines in agricultural production has been demonstrated. However, the methods for constructing and adjusting maintenance schedules have not been sufficiently researched for the common combination of planned and condition-based maintenance strategies. The timing of preventive maintenance was determined based on information about the residual resources and technical condition parameters, as well as the potential losses from machine downtime during field operations. The use of a genetic algorithm as an intelligent optimization method for solving complex management tasks has been justified. The purpose of the study is to develop a method for managing the maintenance of agricultural machinery to combine a planned strategy and a state-based strategy based on an automated approach using a genetic algorithm. The proposed genetic algorithm is a mathematical model of the evolution of artificial individuals, from which the most adapted individuals to the external environment are selected. The service strategies were considered as individuals, and their adaptability was evaluated based on reliability indicators and economic criteria. The main operators of the algorithm, such as twopoint crossover and mutation, have been described. The implementation of this algorithm will allow for the automation of the heterogeneous information processing from diagnostic systems and the field work performed by the machine, and its use in the management of the enterprise's service processes. This will significantly reduce the time spent by specialists on tasks related to the creation and adjustment of machine maintenance schedules, and will also contribute to the development of an autonomous intelligent system for managing the reliability of the enterprise's equipment, eliminating the human factor in the formation of management decisions.
agricultural machinery, maintenance strategy, machine reliability, automation control, genetic algorithm, software
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