## 4 Results and Discussion
### Evaluation Processes  
To measure subjective weights, experts were asked to estimate relative preferences for criteria and sub-criteria based on their expert knowledge. Table 4 provides pairwise comparisons among the three core quality condition criteria. The estimations for the relative preferences for sub-criteria are presented in Tables 5-7 as follows. Thereafter, based on the previous data from the pairwise comparison matrix, then get global weights for each sub-criterion are shown in in Tables 8-10. In this study, four experts were selected to serve as evaluators in order to estimate subjective weights. The criteria for selecting the experts were the following: middle to senior professionals of a heavy-duty manufacturing company, with industrial experience not less than ten years in a respective field of production, quality, or maintenance. \begin{table}
\begin{tabular}{c c c c} \hline  & \(\epsilon_{1}\) & \(\epsilon_{2}\) & \(\epsilon_{3}\) \\ \hline \(c_{1}\) & 1 & 5 & 3 \\ \(c_{2}\) & \(1/5\) & 1 & 3 \\ \(c_{3}\) & \(1/3\) & 1/3 & 1 \\ \hline \end{tabular}
\end{table} 
Table 4: Matrix for pairwise comparisons for the main criteria. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \(c_{\bf 21}\) & \(c_{\bf 22}\) & \(c_{\bf 23}\) & \(c_{\bf 24}\) \\ \hline \(c_{21}\) & \(1\) & \(1/3\) & \(5\) & \(5\) \\ \(c_{22}\) & \(3\) & \(1\) & \(1/3\) & \(5\) \\ \(c_{23}\) & \(1/5\) & \(3\) & \(1\) & \(5\) \\ \(c_{24}\) & \(1/5\) & \(1/5\) & \(1/5\) & \(1\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Matrix for pairwise comparison of the sub-criteria of the physical condition. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(c_{\bf 31}\) & \(c_{\bf 32}\) & \(c_{\bf 33}\) \\ \hline \(c_{31}\) & \(1\) & \(1\) & \(3\) \\ \(c_{32}\) & \(1\) & \(1\) & \(7\) \\ \(c_{33}\) & \(1/3\) & \(1/7\) & \(1\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Matrix for pairwise comparison of the sub-criteria of the usage condition. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Technological Condition, \(c_{1}\) (Weight 0.62)**} \\ \hline Sub-criteria & \(c_{11}\) & \(c_{12}\) & \(c_{13}\) & \(c_{14}\) \\ \hline Local weights & 0.10 & 0.13 & 0.62 & 0.16 \\ Global weights & 0.062 & 0.080 & 0.384 & 0.099 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Weights of sub-criteria related to the technological condition. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(c_{\bf 11}\) & \(c_{\bf 12}\) & \(c_{\bf 13}\) & \(c_{\bf 14}\) \\ \hline \(c_{11}\) & \(1\) & \(1/5\) & \(1/9\) & \(3\) \\ \(c_{12}\) & \(5\) & \(1\) & \(1/7\) & \(1/7\) \\ \(c_{13}\) & \(9\) & \(7\) & \(1\) & \(9\) \\ \(c_{14}\) & \(1/3\) & \(7\) & \(1/9\) & \(1\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Matrix for pairwise comparison of the sub-criteria of the technological conditions. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \(c_{\bf 11}\) & \(c_{\bf 12}\) & \(c_{\bf 13}\) & \(c_{\bf 14}\) \\ \hline \(c_{11}\) & \(1\) & \(1/5\) & \(1/9\) & \(3\) \\ \(c_{12}\) & \(5\) & \(1\) & \(1/7\) & \(1/7\) \\ \(c_{13}\) & \(9\) & \(7\) &on visual/mechanical inspection, along with a full core refund for the best quality class, partial core refund for the middle quality class, and no core refund for the worst quality class. These classes are in line with the recommendations of previous research. For example, [PERSON] et al. [50] suggested three different quality categories for product reuse, recovery, and waste management. The classification for the \(s^{th}\) core into the \(j^{th}\) grey class according to the observed value of the \(s^{th}\) core judged against the \(r^{th}\) criterion is denoted by \(X_{sr}\). Due to the high uncertainty in the core conditions, sometimes it is very difficult to determine the technical index quantitatively for each criterion due to the complexity and difficulty.