# A Dynamic Route-Planning System Based on Industry 4.0 Technology
## 6 Dynamic Route-Planning System and Computational Results with Managerial Insights
### Computational Results and Managerial Insight
#### 6.2.2 Computational Results from a Layout with 18 Storage Racks and 67 Segments  
Next, using the data collected from a distribution center (DC), an internal layout with 18 storage racks was created, as shown in Figure 12. There were 67 path segments after combining the segments \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline
**Sample** & **x1** & **x2** & **x3** & **x4** & **x5** & **x6** & **x7** & **x8** & **x9** & **x10** & **x11** & **x12** & **x13** & **x14** & **x15** & **y** \\ \hline
1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 4 \\
3 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 1 & 0 & 0 & 0 & 2 \\
4 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 1 \\
5 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 5 \\... &... &... &... &... &... &... &... &... &... &... &... &... &... &... &... \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The effect of the number of training sample and ANN configuration on the testing accuracy for a sample layout with 15 segments. Figure 11: An example of encoded input and output data for an ANN model. at the corners. From an observation of an actual operation at the DC, the number of vehicles (folk lifts) working in the DC varies from one to five, with an average of four. Figure 13 shows the distribution of training samples for different numbers of obstacles according to the considered layout. The samples with four obstacles had the highest percentages (39%), while the percentage of samples with two, three, or five obstacles was 19%. The lowest percentage was 3% for samples with one obstacle. Table 4 summarizes the effect of the number of training samples and ANN configuration on the testing accuracy when a layout from a retail warehouse was used. There were 67 variables in the input layer, corresponding to 67 segments of the layout, and 21 nodes, which represented 21 types of paths generated by the output layer. The number of hidden layers and the number of nodes in each layer were varied to achieve the configuration with the best accuracy. The training accuracy reached more than 90% for all cases, but the testing percentages were different between ANN configurations as a function of the number of samples used for training the ANN. As shown in Table 4, on average, the training accuracy percentages for cases with 1448 and 1000 samples were at the same level for all ANN configurations. However, the average testing accuracy for the case with 1448 samples (95.48%) was higher than that for the 1000-sample case (94.72%). Figure 12: Internal layout of a DC with 18 storage racks. Figure 13: Distribution of training samples for different numbers of obstacles. In addition to the number of samples, the ANN configuration, defined by the number of hidden layers and nodes, also affects the accuracy. Using an inadequate number of nodes in the hidden layers results in an underfitting issue. Underfitting occurs when an ANN is not able to construct an adequate configuration from a dataset. By providing a sufficient number of nodes in each hidden layer, the accuracy percentage can be improved. Hence, as shown in Table 4, the accuracy of an ANN with configuration (67-67-21) was higher than the percentage of an ANN with configuration (67-67-21-21). Similarly, the accuracy percentage of an ANN with configuration (67-67-21-21), which was the ANN configuration with two hidden layers that had the highest accuracy percentage (97.93%), was higher than the accuracy percentage of an ANN with configuration (67-70-21), which was the best ANN configuration (96.90%) with one hidden layer. However, having too many nodes in the hidden layers can cause an overfitting issue, where an ANN is not able to generalize the model to identify an output for new input data. Hence, the testing accuracy is deteriorated. For example, when the number of nodes in the hidden layer of an ANN configuration (67-70-21) was increased from 70 to 73 (ANN configuration (67-73-21)), the testing accuracy dropped from 96.79% to 93.79%.