BENCHMARK STUDY OF RE-IDENTIFICATION METHODS BASED ON STOCHASTIC FUZZY NORMALIZATION AND THEIR APPLICATION TO DECISION-MAKING PROBLEMS IN ENGINEERING

In Multi-Criteria Decision Analysis (MCDA), data normalization is essential for ensuring the comparability of heterogeneous and often conflicting evaluation criteria. Conventional normalization techniques, although methodologically straightforward, are predominantly tailored for monotonic criteria, rendering them ineffective for non-monotonic criteria characterized by extrema within the interval rather than at its boundaries. This limitation significantly undermines their applicability in the re-identification of decision models, as they fail to adequately account for the complexity and variability inherent in non-monotonic evaluation approaches. This paper presents a study on the application of stochastic fuzzy normalization (STFN) in combination with popular MCDA methods such as VIKOR, TOPSIS, and MABAC in addressing engineering problems. The study evaluates the effectiveness of this approach in re-identifying decision models, emphasizing its capability to manage nonlinearities and nonmonotonic criteria, mitigate rank reversal phenomena, and adapt to dynamic decision-making scenarios. In this work, the Fuzzy Reference Model (FRM) is leveraged as a robust simulation framework to evaluate the performance of STFN in re-identifying decision models, enabling comprehensive benchmarking of MCDA techniques by providing detailed preference information for each decision option. Through a practical case study involving the selection of an optimal energy source for an industrial plant, the study illustrates how fuzzy normalization supports reliable re-identification of decision models. These comparative analyses reveal potential outcomes and highlight notable differences when STFN is applied in conjunction with various MCDA methods, demonstrating the value of this approach in decision-making contexts.