Connection of MCDM Under Imperfect Information With Intelligent Engineering Systems

Annotatsiya

Decision-making in contemporary engineering, economics, industry, and management frequently involves evaluating multiple criteria under conditions of imperfect information. In practical applications, complete, accurate, and reliable information is rarely available due to incomplete datasets, fuzzy values, and low-reliability measurements. This challenge is highly evident in intelligent engineering systems (IES), where automation, prediction, and control depend on the quality of input data. Imperfect information emerges from sensor noise, subjective expert judgment, insufficient historical records, and rapidly changing environments. Multi-criteria decision-making under imperfect information (MCDM-II) provides a mathematical and analytical basis for improving decision stability, reducing uncertainty, and increasing reliability in such environments. By integrating fuzzy logic, probabilistic models, interval methods, and expert-based evaluations, MCDM approaches enable IES to operate more effectively across fields such as energy, transportation, medicine, environment, industry, and security. The application of MCDM-II strengthens intelligent systems and supports robust decision-making in real-world scenarios