## 4 Computational Experiments and Analyses
### Experiments with Different Optimization Objectives  
The method presented in this paper was applied to three models with different optimization objectives, namely, minimum total cost, minimum carbon emission, and minimum freshness loss, and other conditions of the model remained unchanged. Twelve calculation examples with 25 customer sizes were selected from the example database for testing. The experimental results are shown in Table 3, where TC represents the total cost (unit: yuan), CE represents the cost of carbon emission (unit: yuan), and FL represents the cost of freshness loss (unit: yuan).  
It can be seen from Table 3 from both the cost value and the proportion of each cost in the total cost, the method in this paper can get better results when faced with different optimization objectives. First, a longitudinal comparison was made: (1) compared with other types of data, the proportion of carbon emissions in the total cost of C type data was lower; (2) compared with other types of data, the proportion of freshness loss in the total cost of R type data was lower than others; (3) when the optimization objective is the total cost or carbon emission, carbon emission cost was the lowest among all costs. For horizontal comparison: (1) the TC value of total cost minimization was compared with the TC value of carbon emission minimization, and the average reduction was 29.5%, among which the C-type data had the largest reduction. Compared with the TC value that minimizes the total cost and the TC value that minimizes the loss of freshness, the average reduction was 54.2%, and each type of data had a significant reduction. (2) Compared with the CE value of the total cost minimization, the CE value of the carbon emission minimization reduced by 7.3% on average. However, the CE value of the carbon emission minimization reduced by 61.6% on average, compared with the CE value of the freshness loss minimization. (3) Compared with the FL value with the minimum total cost, the FL value with the minimum freshness loss decreased by an average of 11%, and the FL value with the minimum freshness loss decreased by an average of 17.1% compared with the FL value with the minimum carbon emission.  
According to the experimental results in the literature [6], the geographical distribution of customers has a significant impact on delivery cost, while the relationship between vehicle speed and delivery cost is not significant. On this basis, the experiment further concludes that the geographical distribution of customers will significantly affect the proportion of each cost in the total cost. Therefore, when choosing optimization objectives, enterprises need to make decisions according to the types of customer distribution and actual needs. Taking the lowest carbon emission and the lowest freshness loss as the optimization objective cannot minimize the total cost, but choosing the minimum total cost as the optimization objective can minimize the overall cost. At the same time, for each item cost, taking the total cost as the optimization goal can reduce the maximum cost compared with other optimization targets. The above analysis can show that the method in this paper is effective and that the selection of optimization objectives is reasonable.