## 4 Results & Discussion
### Computational Results  
The computational experiment was divided into four parts. The first part provides an analysis of the convergence property of the SA algorithm. The second part is the experiment with small size instances of 2 EVRPD-DF. The third part is the experiment with large size instances of 2 EVRPD-DF. In the second and third part of the experiment, the results obtained by GUROBI, SA, and common practice are compared. Finally, the last part provides a sensitivity analysis of the impact of the increasing number of customers on the emission cost, considering the use of 2 EVRPD-DF (result of GUROBI and SA) compared to common practice. For the first part of the experiment, the small size instance was used, containing 5 to 30 customers and 6 to 25 drop box facilities. The solution comparison of the 2 EVRP-DF problem is between AMPL and the proposed SA algorithm. The numerical results are shown in Table 3 Columns 1 & 2 specify the number of customers, and the number of drop box facilities at each instance. Columns 3 to 9 are the results of the calculation of the 2 EVRP-DF model, solved using the GUROBI. Columns 3 and 4, respectively, contain the total of the distances in the first and second echelon. Columns 5 and 6, respectively, contain the total emissions generated from the first and second echelon. Column 7 contains the total transportation costs obtained from the sum of the total distances of the first and second echelon, which is then multiplied by the transportation fare. Column 8 contains the total emission costs obtained from the total emissions produced in the first and second echelon,  
Figure 4: Analysis of the impact of parameters T0 (**a**), Tf (**b**), Alpha (**c**), and liter (**d**) on objective value and computational time. which is then multiplied by the carbon tax. Column 9 is the total cost of transportation and emission costs. The next seven columns are the solutions generated by the SA, each of which has the same definition as the result of the GUROBI solver calculation. The last column is the difference between the total cost generated by the GUROBI solver and the solution generated by SA. The numerical results using the GUROBI solver have shown optimal solution. Furthermore, the numerical results of the proposed SA algorithm have also demonstrated the same results as GUROBI, where the difference in total costs between GUROBI and SA is 0. This indicates that the developed SA algorithm has been validated, because the results given are not smaller or larger than the optimal solution generated by GUROBI. The second part of the experiment was conducted with the large size instance which has a number of customers of from 70 to 150 and a number of drop box facilities of as many as 92. Each customer has a demand at random value between 1-15 kg, and each drop box facility has a static capacity of 180 kg. Table 4 shows a comparison of the total costs between the 2 EVRP-DF model (generated from the GUROBI solver and SA algorithm), and common practice where customers go directly to the depot to drop their items. The total cost generated from the 2 EVRP-DF model provides better results than common practice, with an average of 71.95% and 72.09%. This result shows that using the 2 EVRP-DF model can provide an average cost advantage of 72.02% compared to the common practice. Furthermore, the SA algorithm gives slightly better results of total costs compared to GUROBI with an average difference of only 0.51%. This is possible because the calculations performed by the GUROBI solver are near optimal. On the other hand, from the computational time point of view, the SA algorithm gives better results than GUROBI with an average computation time of 298.24 s, equivalent to 4.97 min, where the average computational time of GUROBI reaches 23,409.36 s, equivalent to approximately 6.5 h.  
In the third part of the experiment, a convergency analysis of the SA algorithm is similar to that of previous studies [33; 34].