DEVELOPMENT OF A FUZZY LOGICAL INFERENCE SYSTEM FOR MANAGEMENT DECISION MAKING IN TESTING LABORATORIES

Abstract

The purpose of the article is to develop a fuzzy logic inference system for making management decisions in testing laboratories. In accordance with the requirements of DSTU EN ISO/IEC 17025:2019, the risk and capability management system shall be an element of the laboratory management system. To optimize the risk management process, an expert system based on fuzzy modeling of the risk significance assessment output was used. In the process of analyzing risk factors, indicators were identified that can be defined as risk indicators for testing laboratories: calibration interval of the applied measuring equipment; calibration result of the applied measuring equipment; number of errors in product test protocols; result of participation in comparative tests; time allocated for product testing. The model contains rule base and allows for linguistic analysis of risks for the activities of a testing laboratory. The developed rule base contains 243 rules. The main rule: when one of the risks is high, then the result also has a high risk. The implementation of the rule base modeling process is carried out using the specialized Fuzzy Logic Toolbox package of the MATLAB software. The fuzzy inference is implemented based on the Mamdani algorithm.. The resulting surface of the dependence of the output linguistic variable on two input risk factors: the calibration interval of the applied measuring equipment and the calibration result of this measuring equipment. The surface clearly demonstrates the validity and consistency of the developed system of production rules. The defuzzification procedure was carried out using the "center of gravity" method, the results for several combinations of input data are presented in the table.

The results of this modeling process allow determining the need for preventive measures or corrective actions. The presence of the specified expert system for determining the significance of risks in testing laboratories will ensure constant monitoring of the state of the risk management system. A further perspective for the development of the proposed model is the creation of an adaptive fuzzy production model, which will allow for their reassessment when new risks arise.