Decision analysis with classic and fuzzy EDAS modifications

In this paper, we introduce L1 metrics in evaluation based on distance from average solution method for multi-criteria decision making. The strength of the proposed modification stems from the following advantages brought by its new distance measures: (1) capability for working with varied statistical data types; (2) increased sensitivity when comparing values of similar magnitudes; and (3) minimized influence of large differences between elements. We also present a variant of this algorithm that is suitable for trapezoidal fuzzy numbers. The merit of the new fuzzy modification is reduced time complexity due to the proposed calculation simplifications. The effectiveness and practicality of these new extensions are illustrated by three data sets for the best alternative selection. The results show that the modifications produce equal or very similar ranking in comparison with original algorithm and other well-known multi-criteria decision-making methods.

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