INTEGRATION OF FUZZY LOGIC AT THE SYSTEM ANALYSIS STAGE IN INTELLIGENT DECISION SUPPORT SYSTEMS

Abstract

In the context of modern management characterized by a high degree of uncertainty 
and complexity of decision-making processes, Intelligent Decision-Making Support Sys
tems (IDMSS) play a crucial role. This work is devoted to the integration of the ASSA 
methodology (Automation of System Analysis Stages) into IDMSS with a focus on intro
ducing new technologies that enhance the system analysis processes. Special attention is 
given to the application of fuzzy logic, which allows for more effective handling of uncer
tainties and ambiguities in decision-making. 
The study analyzes the key stages of IDMSS, highlighting two sub-stages of system 
analysis: decision scenario preparation and alternative selection. The introduction of fuzzy 
logic at these stages provides flexibility and adaptability to the system, enabling more well
founded and precise decisions. Fuzzy logic, based on dynamic fuzzy knowledge bases, ena
bles the consideration of vague and imprecise data, which is especially relevant in rapidly 
changing environments. 
Additionally, the paper considers the integration of machine learning and data analysis 
methods, which improve the accuracy of forecasts and the quality of decision-making. 
These methods help manage the diversity and volume of information, ensuring the reliabil
ity and relevance of decisions. The implementation of these technologies not only enhances 
the quality of decisions but also accelerates the data analysis process. 
This work demonstrates how the integration of ASSA and the use of fuzzy logic can 
significantly improve the quality and speed of decision-making in organizations. The re
search results in opening new perspectives for the further development of IDMSS and the 
improvement of management methods under the conditions of uncertainty.