## 5 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & & & & & \\ \hline \multicolumn{1}{c}{**Potential Improvements for Output and Input Measures**} & & & & & & \\ \hline
**DC** & **Efficiency Score** & **Rank** & **SE** & **GDI** & **SR** & **SM** & **JCFR** & **NE** & **VOR** \\ \hline LJC & 0.582 & 9 & +71\% & +113\% & +192\% & +218\% & +85\% & \(\ldots\) & \(-2\%\) \\ MKK & 0.727 & 8 & +37\% & +39\% & +40\% & +124\% & +37\% & \(\ldots\) & \(\ldots\) \\ PSC & 0.832 & 7 & +20\% & +85\% & +98\% & +62\% & +79\% & \(\ldots\) & \(\ldots\) \\ MKO & 1 & 1 & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) \\ MDR & 1 & 1 & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) \\ RRC & 0.932 & 5 & +7\% & +24\% & +23\% & +73\% & +13\% & \(\ldots\) & \(\ldots\) \\ RBC & 1 & 1 & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) \\ TFC & 1 & 1 & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) \\ MKR & 0.881 & 6 & +13\% & +26\% & +17\% & +150\% & +13\% & \(-65\%\) & \(\ldots\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of the DEA model. A graphical comparison between the two different ranking lists for the nine DCs is shown in Figure 1. Although MKO is ranked first in both (i.e., the PROMETHEE II and DEA models), the efficiency of the remaining DCs reveals interesting changes in their rank on the DEA compared to their prior PROMETHEE II rank. For example, RBC and TFC jump from the sixth and seventh positions in PROMETHEE II to the first position on the DEA list. In contrast, MKK scores the second-lowest efficiency value on the DEA, which is relatively far away from its rank on the PROMETHEE II (third). Such a ranking discrepancy prompted further investigation to determine the extent to which PROMETHEE II results (i.e., effectiveness scores) can be influenced by efficiency scores resulting from the DEA model. In order to conduct such an investigation, the PROMETHEE II results had to be normalized to avoid dealing with negative values. As shown in Table 4, each DC's normalized PROMETHEE II score can be computed by deducting the minimum \(\varphi\left(i\right)\) from the corresponding \(\varphi\left(i\right)\); the resulting value is then divided by the difference between the maximum \(\varphi\left(i\right)\) and the minimum \(\varphi\left(i\right)\). The normalized PROMETHEE II scores are then multiplied by the corresponding DEA scores to get what we termed the final DEA-based PROMETHEE II scores. In other words, the original PROMETHEE II scores (i.e., effectiveness scores) were adjusted/fine-tuned by considering the influence of the efficiency scores generated by DEA to get the final DEA-based PROMETHEE II scores (i.e., productivity scores). Figure 2 illustrates the influence of efficiency by comparing the PROMETHEE II, the DEA, and the DEA-based PROMETHEE II ranking lists. Although the comparison between PROMETHEE II and the DEA-based PROMETHEE II ranking lists reveals that the first two positions and the last three positions are similar in both ranking lists, the remaining DCs show a jumps or drop of one position in the final DEA-based PROMETHEE II ranking list. The employment of the DEA as a fine-tuning instrument to adjust the previously implemented MCDM model, such as PROMETHEE II in the current study, can be considered as a unique contribution within the context of MCDM applications. Although such attempts are not commonly investigated in the MCDM literature, the results presented herein are compatible with two previously published works that assert a similarly unique involvement and employment of DEA with TOPSIS [44] and VIKOR [47] under fuzzy environments. Indubitably, the existence of such applications provides a sort of validation of the outcomes discussed in this paper.