## 4 Numerical Experiments and Results
### Example and Parameter Setting
#### 4.1.1 Example Setting
Here, all elements in the coefficient matrix of the congestion degree of the no congestion scenario are 0. All elements in the coefficient matrix of the congestion degree of the normal congestion scenario are random numbers within \([0,3]\). All elements in the coefficient matrix of the congestion degree of the very congested scenario are 3. On the other hand, it is assumed that there are five mixed carbon policy scenarios, namely, S1, S2, S3, S4 and S5. The specific explanation is as follows. \(w_{1}:w_{2}\) is the weight ratio of the carbon tax cost and carbon trading cost in the mixed carbon emission cost. The simulation of five mixed carbon policy scenarios is realized by changing the weight ratio. The weight ratios and meanings of the five scenarios are shown in Table 1.  
In addition, the historical method is used to calculate the carbon quota of a single distribution activity of the distribution center, as shown in Equation (26). \(\mu\) is the carbon quota. \(\tau\) represents the carbon emission intensity in the historical base year (tons CO\({}_{2}\)/100 million ton-km freight turnover). \(\chi\) represents the control coefficient (dimensionless). \(\gamma\) represents the freight turnover in the approved year (100 million ton-km freight turnover), which can be calculated according to Equation (27). In the simulation example vrpnc1-S2, \(\
u\) represents the average of the total transportation distance of multiple running tests, and \(\delta\) represents the total demand. Here, the emissions control coefficient is 99.5% [49]. According to the road freight data in the Galaxy Transportation and China Statistical Yearbook (2018), the carbon emissions intensity is 0.0000793 kgCO\({}_{2}\)/kg-km freight turnover. For additional information regarding the concepts of carbon intensity and freight turnover, please refer to the method [49]. \[\mu=\tau\cdot\chi\cdot\gamma \tag{26}\]  
\[\gamma=\
u\cdot\delta \tag{27}\]  
Furthermore, in Sections 4.1.2 and 4.2, other variables and procedures are maintained unchanged, and the results of five runs under different coefficients or strategies are obtained. The maximum number of iterations in the re-optimization stage is 500. To facilitate comparisons with research results in the literature and to analyze the effectiveness of the designed algorithm, the total distribution distance is used as the objective function value, and five improved neighborhood structures are used to form the neighborhood structure set. In Section 4.3, the relevant impacts of different traffic congestion scenarios and different mixed carbon policy scenarios on the multi-compartment vehicle path planning are examined and compared. In each scenario, the maximum number of iterations in the re-optimization stage is 100, and the results of five runs are obtained. The total cost, including the mixed carbon emission cost, vehicle management and usage cost and transportation cost, is used as the objective function value. The integrated-icross-insert neighborhood and integrated-icross-exchange neighborhood are used to form the neighborhood structure set. In this way, a comparative analysis can be performed while appropriately reducing the running time. In addition, the related indicators of the objective function value and running time of the results of multiple runs are adopted for comparison, such as the optimal value, worst value, average value, standard variance, range and range percentage. MATLAB R2016b was used for the calculations, and the operating environment was a Windows 10 Professional Edition, Intel(R)Core(TM)i5-4210H CPU @ 2.90 GHz processor, 4 G memory and 64-bit operating system.