Abstract
The problem of optimizing the selection of a rational set of construction machinery for performing works under specific production conditions is considered. The formation of a machinery set is considered as a multi-criteria task that takes into account technical compatibility, productivity, life cycle cost, and the specific characteristics of the construction site. The study analyzes traditional regulatory-reference, expert, technological, and economic approaches, as well as modern methods based on mathematical modeling, information systems, and digital technologies. Special attention is paid to domestic research developments aimed at systematizing the principles of integrated mechanization, calculating machinery productivity, and assessing the economic efficiency of equipment operation. The results of foreign studies confirm the universal nature of the problem and demonstrate the application of multi-criteria optimization methods and machine learning algorithms. The proposed approach makes it possible to reduce total costs by 10-15%, increase equipment utilization rates, and shorten decision- making time. The integration of digital technologies and artificial intelligence elements is identified as the most promising direction, enabling the forecasting of production indicators and the adaptation of decisions to changing construction conditions