Fuzzy Logic Systems and Decision Making
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Abstract
Classical Boolean logic, classifying variables into binary, rigid states, has been important in the traditional decision-making models of computational models and operations research. Nonetheless, the real world systems, especially those in dynamic computing environments, network structures and multi-criteria assessments, are beyond doubt, imprecise, and ambiguous. This makes crisp bivalent logic insufficient to real-time complex modeling. The presented research paper is a discussion on mathematical basis of Fuzzy Logic Systems (FLS) and their strict incorporation in Multi-Criteria Decision making (MCDM). Fuzzy sets can be used to provide partial levels of truth by filling the gap the gap between subjective human thinking and objective computing implementation, which is suitable to making optimal decisions in uncertain conditions. The paper explains mathematical functions of Triangular norms (T-norms) and conforms, the structure of Mamdani and Sugeno Fuzzy Inference Systems and the calculation of the matrices based on the Fuzzy AHP and Fuzzy TOPSIS. A working example of such mathematical constructs is given through a case study of computational models of dynamic CPU load balancing in a distributed architecture which shows how fuzzy mathematical models can be translated into executable programmatic logic via algorithm.
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