# An Integrated Decision Support System for Low-Disturbance Surface Mining
## 3 Overview of Results
### Analysis of Optimal Path Ordering  
The F-TOPSIS method was used to calculate the comparative advantages of the four possible paths, and the relative weights of each category and the comparative advantages  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Norm**} & \multicolumn{4}{c}{**Reality**} & \multicolumn{4}{c}{**Desirability**} \\ \cline{2-10}  & \(\hat{f}_{i}^{A}(x)\) & \(\hat{f}_{i}^{B}(x)\) & \(\hat{f}_{i}^{A}(x)\) & \(\hat{f}_{i}^{A}(x)\) & \(\hat{f}_{i}^{B}(x)\) & \(\hat{f}_{i}^{A}(x)\) & \(\hat{f}_{i}^{B}(x)\) & \(\hat{f}_{i}^{A}(x)\) & \(\hat{f}_{i}^{A}(x)\) & \(\hat{f}_{i}^{A}(x)\) \\ \hline CS1 & - & **0.0618** & 0.0069 & - & - & - & - & 0.0361 & **0.0541** & - \\ CS2 & - & **0.0638** & 0.0425 & - & - & - & - & - & 0.0508 & **0.0763** \\ CS3 & 0.0446 & **0.0669** & - & - & - & - & - & - & 0.0487 & **0.0730** \\ CE1 & - & - & **0.0547** & - & - & - & - & 0.0221 & **0.0331** & - \\ CE2 & - & **0.0772** & 0.0331 & - & - & - & - & 0.0094 & **0.0375** & - \\ CE3 & **0.0377** & 0.0251 & - & - & - & - & - & - & 0.0372 & **0.0558** \\ CE4 & - & **0.0868** & 0.0096 & - & - & - & - & - & **0.0592** & 0.0395 \\ CP1 & - & **0.0454** & 0.0303 & - & - & - & - & - & **0.0558** & 0.0062 \\ CP2 & - & 0.0152 & **0.0228** & - & - & - & - & 0.0108 & **0.0252** \\ CP3 & 0.0138 & **0.0554** & - & - & - & - & - & - & 0.0277 & **0.0416** \\ CP4 & **0.0192** & 0.0123 & - & - & - & - & - & 0.0096 & **0.0223** & - \\ CD1 & - & **0.0504** & 0.0336 & - & - & - & - & 0.0341 & **0.0512** & - \\ CD2 & 0.0183 & **0.0734** & - & - & - & - & - & **0.0661** & 0.0165 \\ \hline \hline \end{tabular} Note: The values bolded in black in Table 2 are the final grey clustering values for each submatrix.  
\end{table}
Table 2: Clustering coefficients of sub-indicators.  
Figure 11: Comparison of LD levels for sub-indicators.  
of the four possible paths involved were arranged in a matrix, and the detailed data for the analysis are presented in Tables 14-18 (in Appendix B) of this paper. The paths are prioritized by comparing the magnitude of the posting progress coefficients for each path, as shown in Table 3. The results show that the optimal sorting path for realizing LDM is \"Optimised design (OD)\" > \"Intelligent dispatch (ID)\" > \"Land reclamation (LR)\" > \"Optimisation of processes (OP)\".  
Prioritization is critical for low-disturbance open pit mining, and the results obtained for each criterion were compared to obtain the proximity coefficients of the paths for each of the challenge criteria, as well as the path order, which is shown in Table 4. As shown in Figure 12, the optimal ordering in terms of \"Control of stripping works (CS)\" is the paths: \"Optimised design (OD)\" > \"Intelligent dispatch (ID)\" > \"Land reclamation (LR)\" > \"Optimisation of processes (OP)\". For \"Control of emission rock (CE)\", the optimal ordering path is: \"Optimised design (OD)\" > \"Land reclamation (LR)\" > \"Intelligent dispatch (ID)\" > \"Optimisation of processes (OP)\". For \"Control of pollution emission (CP)\", the optimal ordering path is: \"Intelligent dispatch (ID)\" > \"Land reclamation (LR)\" > \"Optimisation of processes (OP)\" > \"Optimised design (OD)\". For \"Control of drainage (CD)\", the optimal sorting path is: \"Optimised design (OD)\" > \"Land reclamation (LR)\" > \"Intelligent dispatch (ID)\" > \"Optimisation of processes (OP)\".  
Figure 12: Proximity coefficients and path ordering under prioritized metrics.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Pathway Distance to a Fuzzy Positive Ideal Solution** & **Distance to a Fuzzy Negative Ideal Solution** & **Closeness** & **Prioritizing** \\  & & **Coefficient** & **Order** \\ \hline OD & 0.5624 & 0.7655 & 0.5765 & 1 \\ ID & 0.5848 & 0.7518 & 0.5625 & 2 \\ OP & 0.6680 & 0.6846 & 0.5061 & 4 \\ LP & 0.5967 & 0.7270 & 0.5492 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Final prioritization of paths.