INTEGRATION OF HYBRID SYSTEM ANALYSIS METHODS TO IMPROVE DECISION-MAKING EFFICIENCY

The article discusses the integration of hybrid methods of system analysis to improve the efficiency of decision-making under uncertainty and multiparameter constraints. Modern approaches to solving complex control and optimization problems are analyzed. The use of hybrid algorithms that combine classical and modern analysis methods, such as genetic algorithms, the Monte Carlo method, and neural networks is proposed. The use of these models allows improving the accuracy and stability of decisions, as well as reducing time and computational costs. The article discusses modern approaches to the integration of hybrid methods of system analysis and optimization algorithms to improve the efficiency of decision-making in complex systems. The main concepts of system analysis combined with optimization algorithms, including methods of evolutionary modeling, multicriteria optimization and machine learning, are considered. The purpose of the work is to demonstrate the advantages of a hybrid approach in solving problems in such areas as management, energy, logistics and information systems.