## 6 Results and Discussion
\[\overline{R}=\frac{1}{2}n(m+1)=\frac{1}{2}14(10+1)=77,\]3 Correlation of the normalized weights \(\omega_{i}\) of the criteria calculated by different methods with the rank averages \(\overline{R}_{i}\): (**a**) ARTIW-L; (**b**) ARTIW-N; (**c**) DPW; (**d**) AHP. The research results show that \(\Delta\omega_{i}\) varies from 0.0135 (criterion F) to 0.0519 (criterion D). The correlation dependence between \(\overline{\omega}_{i}\) and \(\Delta\omega_{i}\) is shown by the regression curve (Figure 5b) and a sufficiently large coefficient of determination \(R2\) = 0.9458. The smallest weight difference \(\Delta\omega_{i}\) is for criteria E and F; that is, those criteria whose average weight \(\overline{\omega}_{i}\) is close to the average weight of all criteria (in the study \(\overline{\omega}_{i}\) = 0.1000). As the weight of the criteria \(\overline{\omega}_{i}\) increases to one side and the other from 1.0000, the weight difference \(\Delta\omega_{i}\) increases unevenly (asymmetrically): the most important criteria G, J, D increase faster. The maximum difference is the smallest for those criteria whose average weight is closest to the average weight of all criteria of 1.000. As the average of criteria weights moves away from one in the decreasing direction, the width of the sample increases slightly, and as it moves away in the increasing direction (of the most important criteria), it increases significantly (Figure 5b). The average of the weights of each criterion \(\overline{\omega}_{i}\) determined from Formula (18) by four MCDM methods is taken as the result of solving the task (Table 7). The priority of criterions weights calculated by ARTIW-L, ARTIW-N, DPW, and AHP methods (Tables 4-6) coincides with the priority determined from the weight averages \(\overline{\omega}_{i}\) (Table 7). It is likely that the average weight of the criteria \(\overline{\omega}_{i}\) is closer to the average of the population of experts than the weight \(\omega_{i}\) of the \(i\)-th criterion determined by any one method. The weight of each \(i\)-th criterion calculated by the four methods is different. The difference between the weights of the highest \(\omega_{\text{imax}}\) and lowest \(\omega_{\text{imin}}\) criteria indicate the width of variation (sample width), which is used to calculate the approximate standard deviation \(s^{\prime}_{\Delta\omega}\) (Table 8). After dividing the standard deviation of the criteria weights \(s^{\prime}_{\Delta\omega}\), calculated from the sample width and the standard deviation \(s_{\omega i}\), calculated from all values of four methods, from the weight averages \(\overline{\omega}_{i}\), the percentage coefficient of variation in the criteria weights \(V^{\prime}_{uri}\) and \(V_{wi}\) was determined (Table 8). This characteristic showing the relative degree of data variation is calculated from Formulas (22) and (23). The dispersion of trait variants is considered low if the coefficient of variation reaches from 0 to 10%, medium--from 10 to 20%, and high--more than 20% [52]. Six out of ten criteria (B, E, F, G, I, J) have low  
\[\overline{R}=\frac{1}{2}n(m+1)=\frac{1}{2}14(10+1)=77,\]4 (**a**) Differences in criteria weights \(\Delta\omega_{k}=\omega_{\text{imax}}-\omega_{\text{imin}}\) calculated by different MCDM methods and (**b**) correlation between the differences in criteria weights \(\Delta\omega_{i}\) and weight averages \(\overline{\omega}_{i}\) calculated by four MCDM methods. dispersion, and four criteria (A, C, D, H) have medium dispersion. The coefficients of variation \(V_{i}^{\prime}\) of almost all criteria are slightly higher (ranging from 6.3% to 20.6%) than the coefficients of variation \(V_{i}\) (ranging from 5.6% to 18.1%) and they are linked by a linear positive correlation. Therefore, any of them can be taken with sufficient accuracy for practice to determine the criteria weights of the MCDM methods used in the study.