## 4 Example Experiments
### Algorithm Performance Testing
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{\(|R|\)} & \multirow{2}{*}{\(|U|\)} & \multirow{2}{*}{\(|D|\)} & \multicolumn{2}{c}{**GA\_GA**} & \multicolumn{2}{c}{**CPLEX**} & \multicolumn{4}{c}{**IG+ECTT**} \\ \cline{5-12}  & & & & **Obj (h)** & **Cpu (s)** & **Obj (h)** & **Cpu (s)** & **Gap (\%)** & **Obj (h)** & **Cpu (s)** & **Gap (\%)** \\ \hline test1 & 5 & 2 & 2 & 384.39 & 15.81 & 384.39 & 1.02 & 0.00 & 384.39 & 1.95 & 0.00 \\ test2 & 5 & 3 & 2 & 384.39 & 20.95 & 384.39 & 1.63 & 0.00 & 384.39 & 2.31 & 0.00 \\ test3 & 6 & 2 & 2 & 436.38 & 31.64 & 436.38 & 3.22 & 0.00 & 436.38 & 2.97 & 0.00 \\ test4 & 6 & 3 & 2 & 436.38 & 48.38 & 436.38 & 4.52 & 0.00 & 436.38 & 3.32 & 0.00 \\ test5 & 7 & 2 & 2 & 528.71 & 62.52 & 528.71 & 12.23 & 0.00 & 530.21 & 4.07 & 0.28 \\ test6 & 7 & 3 & 2 & 528.71 & 79.85 & 528.71 & 18.91 & 0.00 & 530.48 & 4.68 & 0.33 \\ test7 & 8 & 2 & 2 & 581.49 & 107.44 & 579.31 & 91.74 & \(-0.38\) & 583.79 & 5.15 & 0.39 \\ test8 & 8 & 3 & 2 & 582.92 & 116.25 & 579.05 & 209.56 & \(-0.67\) & 585.74 & 5.72 & 0.48 \\ test9 & 9 & 2 & 2 & 661.01 & 154.65 & 655.92 & 1452.97 & \(-0.78\) & 664.81 & 6.57 & 0.57 \\ test10 & 9 & 3 & 2 & 659.45 & 179.72 & 654.16 & 3600.00 & \(-0.81\) & 663.78 & 7.25 & 0.65 \\ test11 & 10 & 3 & 2 & 737.88 & 214.61 & 731.64 & 3600.00 & \(-0.85\) & 743.47 & 7.88 & 0.75 \\ test12 & 10 & 4 & 2 & 739.29 & 227.49 & 732.46 & 3600.00 & \(-0.93\) & 745.89 & 8.94 & 0.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: The calculation results of small-scale examples. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{\(|R|\)} & \multirow{2}{*}{\(|U|\)} & \multicolumn{2}{c}{**GA\_GA**} & \multicolumn{2}{c}{**CPLEX**} & \multicolumn{2}{c}{**IG+ECTT**} \\ \cline{5-12}  & & & **Obj (h)** & **Cpu (s)** & **Obj (h)** & **Cpu (s)** & **Gap (\%)** & **Obj (h)** & **Cpu (s)** & **Gap (\%)** \\ \hline test13 & 14 & 4 & 2 & 1274.88 & 722.58 & 1292.28 & 3600.00 & 1.35 & 1290.25 & 14.32 & 1.19 \\ test14 & 15 & 4 & 2 & 1325.28 & 767.06 & 1363.14 & 3600.00 & 2.78 & 1345.21 & 16.02 & 1.48 \\ test15 & 16 & 4 & 2 & 1404.82 & 804.95 & 1460.61 & 3600.00 & 3.82 & 1433.72 & 17.76 & 2.02 \\ test16 & 17 & 5 & 2 & 1495.54 & 832.39 & 1575.78 & 3600.00 & 5.09 & 1532.95 & 19.89 & 2.44 \\ test17 & 18 & 5 & 3 & 1278.45 & 865.27 & 1364.58 & 3600.00 & 6.31 & 1316.40 & 22.13 & 2.88 \\ test18 & 18 & 6 & 3 & 1276.62 & 872.62 & 1379.03 & 3600.00 & 7.43 & 1311.19 & 24.21 & 2.64 \\ test19 & 19 & 6 & 3 & 1329.03 & 913.29 & 1464.12 & 3600.00 & 9.23 & 1373.97 & 26.64 & 3.27 \\ test20 & 19 & 7 & 3 & 1331.71 & 932.11 & 1495.65 & 3600.00 & 10.96 & 1380.37 & 28.88 & 3.53 \\ test21 & 20 & 7 & 3 & 1345.71 & 945.86 & 1558.73 & 3600.00 & 13.67 & 1400.22 & 31.48 & 3.89 \\ test22 & 20 & 8 & 3 & 1340.85 & 962.61 & 1578.81 & 3600.00 & 15.07 & 1397.36 & 33.76 & 4.04 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: The calculation results of large-scale examples. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Upstream Station**} & \multicolumn{2}{c}{**Efficiency**} \\ \hline NJ & 2 & KM & 5 \\ FY & 2 & HEB & 15 \\ ZZ & 5 & TL & 15 \\ YT & 5 & LZ & 30 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The transshipment efficiencies of upstream stations (unit/h). IG+ECTT. Based on Framework 1, proposed in Ref. [40], the above examples were solved with transshipment sequence selection as the outer layer and hub allocation as the inner layer. It should be noted that although Ref. [40] designed seven different solution methods, its experimental results showed that the combination of the iterated greedy algorithm and the earliest completion time with transport (IG+ECTT) is the most effective. Therefore, we compare IG+ECTT with GA_GA in our experiments. The results are shown in columns 10 to 12 of Tables 11 and 12. 3. Other algorithms.