APPLICATION OF MULTI-CRITERIA ANALYSIS METHODS FOR MANAGERIAL DECISION-MAKING SUPPORT UNDER UNCERTAINTY

Abstract

Managerial decision-making is complicated by uncertainty and subjective judgments, making single-criterion methods insufficient. This paper proposes a methodical approach for decision-making under uncertainty, using a hybrid application of the Analytic Hierarchy Process (AHP) and the Fuzzy TOPSIS method.

AHP is used to structure the problem and determine criteria weights through expert pairwise comparisons. From these comparisons, a priority vector (the criteria weights) is calculated. The model ensures reliability by verifying the logical consistency (CR) of judgments before they are used.

Fuzzy TOPSIS is then used for the final ranking. Experts use a linguistic scale (e.g., "Medium," "High") to evaluate alternatives, which are converted into Triangular Fuzzy Numbers (TFNs) to model imprecision. These are aggregated, normalized, and combined with the AHP weights. The method defines Fuzzy Positive-Ideal (FPIS) and Negative-Ideal (FNIS) solutions, and alternatives are ranked by a Closeness Coefficient ( ) measuring relative proximity to the ideal. The alternative with the highest  is deemed the optimal choice.

The approach was tested on a case study of selecting an optimal supplier (3 alternatives, 4 criteria). The AHP results identified "Quality" as the most important criterion. The final Fuzzy TOPSIS ranking identified "Supplier Alpha" (A1) as the best alternative (  = 0.696). Notably, "Supplier Beta" (A2), which had the best price, ranked last due to low scores on high-weight criteria like quality and reliability, demonstrating the model's effectiveness.

The developed approach can be used by managers to enhance the validity and quality of strategic decisions. Future research directions include the development of software to automate these calculations and the integration of this approach with risk analysis and forecasting methods.