# An Optimization Route Selection Method of Urban Oversize Cargo Transportation
## 3 Materials and Methods
### Cloud Model Optimization
#### 3.3.3 Backward Cloud Generator  
The backward cloud generator (BCG), as shown in Figure 2, is the process of transforming a certain number of accurate numerical values into qualitative relations \(C(Ex,En,He)\), and further analysis and calculations are carried out according to the three descriptive statistics, \(Ex\), \(En\), and \(He\), of the generated cloud model. This is the basic transformation principle of BCG. In this case, we input the exact score, analyze the discreteness of the score through BCG from \(Ex\), \(En\), and \(He\), and use the result of the analysis as an important reference for weights. Our research uses the backward cloud generator as the tool of weight optimization. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Factor** & **Information Entropy** & **Weight** \\ \hline FC & 0.676 & 0.129 \\ TC & 0.750 & 0.100 \\ TM & 0.658 & 0.137 \\ LG & 0.587 & 0.165 \\ RW & 0.686 & 0.125 \\ RN & 0.707 & 0.117 \\ BN & 0.432 & 0.227 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Information entropy and weight. Our research invites Suzhou oversize cargo logistics practitioners to give scores to factors and use a backward cloud generator to analyze the score table. The mean of the data is calculated as:  
\[Ex=\frac{1}{n-1}\sum_{i=1}^{n}x_{i} \tag{5}\]  
The center distance of the data is calculated as:  
\[\frac{1}{n}\sum_{i=1}^{n}|x_{i}-Ex| \tag{6}\]  
The variance of the data is calculated as:  
\[S^{2}=\frac{1}{n-1}\sum_{i=1}^{n}(x_{i}-Ex)^{2} \tag{7}\]  
The cloud entropy of the data is calculated as:  
\[En=\sqrt{\frac{\pi}{2}}\times\frac{1}{n}\sum_{i=1}^{n}|x_{i}-Ex| \tag{8}\]  
According to the calculation result of cloud entropy, combined with the entropy weight method, calculate the optimized weight according to Formula (9). The mean values \(Ex\) are processed on the basis of the degree of dispersion \(En\), and the weights of the factors in this part are calculated and adjusted, and then the result is combined with \(w_{i}\) in Section 3.2. \[W_{i}=\frac{1}{2}\cdot\frac{Ex_{i}}{\ln(1+En_{i})+1}\cdot\frac{1}{\sum\limits_ {i=1}^{n}\frac{Ex_{i}}{\ln(1+En_{i})+1}}\cdot w_{i} \tag{9}\]  
The judgement is based on two aspects: the mean value and the cloud entropy. The larger the mean cloud value, the more important a factor is. If the cloud entropy is larger, it means that the practitioners have greater disagreement on the factor, and the weight of the factor should be reduced. The smaller the cloud entropy, the smaller the disagreement among practitioners on this factor, and the weight of the factor should be increased. The subjective scores of oversize cargo transportation practitioners in a Suzhou logistics company are shown in Table 4. The research conducted this investigation with practitioners and asked them to score the impact of factors on the actual transportation process based on work experience and actual cases. The score range is 0-100, and the score is an integer multiple of 5. The higher the importance, the higher the score. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Practitioners Number** & **FC** & **TC** & **TM** & **LG** & **RW** & **RN** & **BN** \\ \hline
1 & 80 & 85 & 60 & 75 & 85 & 50 & 90 \\
2 & 90 & 85 & 70 & 80 & 90 & 60 & 85 \\
3 & 80 & 80 & 65 & 75 & 95 & 65 & 85 \\
4 & 75 & 70 & 50 & 70 & 85 & 50 & 85 \\
5 & 85 & 90 & 60 & 70 & 95 & 70 & 95 \\
6 & 70 & 75 & 50 & 65 & 90 & 65 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The scores given by practitioners. Figure 2: Backward cloud generator. Figure 8: Cloud image of RN. Figure 6: Cloud image of LG. Figure 7: Cloud image of RW. The degree of dispersion of the droplets in Figures 3-9 indicates the inconsistency of individual scores for this indicator. It can be seen from the figures that the degree of dispersion of RN, LG, TC, and BN is significantly smaller, indicating that the score trends for these indicators are consistent. The middle line of the images in the figures represents the mean value of the indicators.