## 4 Results & Discussion
### Computational Results
We compared the convergency of the SA algorithm with other metaheuristics algorithms, in this case a Genetic Algorithm (GA). The instances are selected from the large size dataset which has 70 customers and 92 drop box facilities. Figure 5 shows the convergency of the SA and GA algorithm in finding a solution at a predetermined time, from 0 s to 3000 s. From 0 to 300 s, it is shown that both the SA and GA algorithm is able to search for minimal solutions. For SA, there is a drastic change in the solution at 240, with a total objective of 618,378 and 616,670 in 630 s. Later in the iteration, the SA algorithm made good improvements, but progressing slowly as it approached steady-state. At the final part of the iteration, the SA algorithm shows the same solution until the end of the experiment and shows a convergence property. Meanwhile, GA shows a slower process than SA. The improvement of the solution in GA is shown at 840 and 1890 s with the objective value of 620,123 and 619,556, respectively. At the final part of the iteration, GA also shows the same solution until the end of the experiment and shows a convergence property. The parameters required by GA are population size, crossover rate, and mutation rate, which are 2000, 0.6, and 0.01, respectively. This means that the probability of population crossover mechanism is 60% and the chance of mutation mechanism is 1%. The comparison of total objective of SA and GA is shown in Table 5. Both SA and GA demonstrate a good optimization process in finding a minimum solution with the 2 EVRP-DF model. SA gives an average total objective of 887,762.69, while GA provides 889,652.29. Although the average difference in the objective values looks small, in solving the 2 EVRP-DF model SA outperforms GA by 0.21%, which is in line with several previous studies where SA was able to outperform other metaheuristics algorithms, especially in VRP problems [35; 36; 37; 38]. In a further analysis regarding emission costs, the computational results from the 2 EVRP-DF model were promising compared to common practice. Table 6 shows the detailed emission calculations for the 2 EVRP-DF model and common practice. The 2 EVRP-DF model shows that the average total emissions produced by vehicles in the first and second echelon are 47.80 (GUROBI) and 47.56 (GUROBI), where the total emission in common practice averages 133.60. Furthermore, Figure 6 shows that, as more customers are added to the system, the amount of carbon emissions also increases. However, when compared to the 2 EVRP-DF model, common practice generates much higher emission costs compared to the 2 EVRP-DF. For this reason, it can be concluded that the 2 EVRP-DF model can have a positive impact on the environment by reducing the amount of carbon emissions released by vehicles. Figure 5: Convergency analysis of SA during the searching process. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c c} \hline \multicolumn{12}{c}{\multirow{3}{*}{**Category**}} & \multicolumn{1}{c}{\multirow{3}{*}{**Total**}} & \multicolumn{1}{c}{\multirow{3}{*}{**Deep Box**}} & \multicolumn{1}{c}{\multirow{3}{*}{**E12**}} & \multicolumn{1}{c}{\multirow{3}{*}{**E13**}} & \multicolumn{1}{c}{\multirow{3}{*}{**E14**}} & \multicolumn{1}{c}{\multirow{3}{*}{**E15**}} & \multicolumn{1}{c}{\multirow{3}{*}{**E16**}} & \multicolumn{1}{c}{\multirow{3}{*}{**E17**}} & \multicolumn{1}{c}{\multirow{3}{*}{**E18**}} & \multicolumn{1}{c}{\multirow{3}{*}{**E19