modification of the edas method for controlling outlier data

The purpose of this study is to modify the edas method so that it can control outlier data. in multi-criteria decision making methods, the distances between the data are the criterion for calculating the score, so the existence of significant distance of outlier data from other data, can make the score of the criteria exclusive to outlier data. therefore, in addition to weighting the criteria, which is a kind of valuing the criteria relative to each other, weighting within the criteria by evaluating effective distances will be necessary. despite the special conditions, the data distribution can be approximated to the normal distribution based on the central limit theorem. in this study, the normal distribution mechanism has been used to identify and evaluate effective distances. the proposed technique has been used for about 1950 branches of the agricultural bank of iran and the results have been analyzed while comparing with the classic method.