DECISION-MAKING MODELS FOR COMPLEX THE ECO-ENERGY-ECONOMIC MONITORING SYSTEM

This article presents the development and implementation of a decision support system in a web application for complex eco-energy-economic monitoring. The study focuses on the analysis and development of decision-making methods, including decision tables, decision trees and expert systems, which ensure efficient and accurate problem-solving in complex environments. Decision tables are used for the systematic analysis of possible alternatives and select the best option based on specified criteria. Experts may use different criteria, including Wald, Bayesian-Laplace, Savage, or Hurwitz, to assess risks, averages, potential losses, or to integrate pessimistic and optimistic approaches. Decision trees provide a convenient way to model decision sequences and visualise scenarios. This method assesses the risks of each option, facilitating informed decision-making through analysis of potential future scenarios. The expert system is designed to accumulate and use knowledge in the form of rules containing conditions and actions. Knowledge engineers, working on the basis of expert experience, create a knowledge base that can be used to solve similar problems in the future. The developed decision support system allows experts to send their proposals to an analyst, who analyses the data in depth, assesses the available alternatives and formulates action programmes. The integration of decision tables, decision trees and expert systems into a single platform ensures high speed, accuracy and balanced decision making, which is essential for monitoring tasks. The system has significant practical value, providing analysts with tools for comprehensive data analysis, process optimisation and development of action strategies. Its implementation helps to improve the efficiency of management, particularly in the areas of the environment, energy and the economy, which is important for ensuring sustainable development and improving the health and quality of life of the population.