## 5 Examples and Results  
### Experimental Scene Setup  
The HDA-CR proposed in Section 3 is an improvement on the coding repair and non-determined sorting-based differential evolution (ERNS-DE) algorithm proposed in literature [40], which aims to solve the problem of multi-dimensional allocation problem of hazardous materials. The parameters of HDA-CR are set as shown in Table 2.  
Considering the lack of relevant hazardous materials transfer risk data, in order to verify the effectiveness of the model and algorithm as much as possible, this example contains two different scale experimental environment settings, two hazardous materials supply and demand scenarios, and two periods settings, including long-term allocation and short-term allocation, as well as a total of eight difference experimental scenarios, comparing HDA-CR with the classical multi-objective algorithm and the ERNS-DE algorithm in different scenarios.  
Environment 1: Consider the smaller area hazardous material allocation problem, four hazardous material types, seven reserve points, and three consuming points, and the locations of the reserve points and consuming points are randomly generated according to the generalized distance, as shown in Figure 6a. The numbers in Figure 6 represent the numbering of consumption points and reserve points respectively.  
Environment 2: Consider the larger area hazardous material allocation problem, 7 hazardous material types, 30 reserve points, and 7 consumption points, and the locations of reserve and consumption points are randomly generated according to the generalized distance, as shown in Figure 6b.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Population Size** & **Self-Mutation** & **Scaling Factor** & **Hierarchical** & **Crossover** & **Maximum Number of** \\  & **Probability** & **Coefficient** & **Probability** & **Iterations** \\ \hline
30 & (0.1 0.5 0.8) & 1 & (0.3 0.6) & 0.5 & 500 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The parameter setting of HDA-CR.  
Figure 6: Distribution of reserve points and consumption points in different environments.  
Case 1: Consider hazardous materials allocation problem under resource-rich. The sum of the quantities of hazardous materials available at all reserve points is far greater than the sum of the demands at all consumption points in all periods.  
\[\sum_{i=1}^{T}y_{i}^{k}\gg\sum_{t=1}^{T}c_{t}^{k},k=1,2,\ldots K \tag{24}\]  
Case 2: Consider the hazardous materials allocation problem under resource constraints. The sum of the quantities of hazardous materials available at all reserve points is exactly equal to the sum of the demands of all consumption points in all periods.  
\[\sum_{i=1}^{T}y_{i}^{k}=\sum_{t=1}^{T}c_{t}^{k},k=1,2,\ldots K \tag{25}\]  
Period parameter setting: Consider the short-term allocation problem of hazardous materials, and let the number of periods \(T=5\); consider the long-term allocation problem of hazardous materials, and let the number of periods \(T=24\). The specific experimental scenarios are set up as shown in Table 3.  
In this experiment, the traffic accident probabilities \(pj_{ij}^{l}\) and \(pj_{ij}^{u}\), population density \(pd_{ij}^{l}\) and \(pd_{ij}^{u}\), the influence range \(s^{l}\) and \(s^{u}\) of hazardous materials, and the membership values \(\omega_{ij}^{l}\) and \(\omega_{ij}^{u}\) of upper and lower layers on each road section are randomly selected from the interval \([0.001\ 0.009]\), \([0.01\ 0.02]\), \([90\ 110]\), \([140\ 160]\), \([200\ 500]\), \([1500\ 2000]\), \([0\ 0.5]\), and \([0.5\ 1]\), respectively. Additionally, \(\theta_{R}=100\) is adopted as the allocation risk threshold.