# A Dynamic Route-Planning System Based on Industry 4.0 Technology
## 7 Conclusions  
In this research, the focus was on applying Industry 4.0 technology, related to big data analytics, to a route-planning operation of automated navigation within a warehouse. Route-planning methodologies for a dynamic routing problem with the consideration of real-time obstacles are proposed. An optimization model and a heuristic methodology based on an A-star algorithm were used to generate the routes. Machine learning models using an ANN were developed on the basis of generated datasets using the internal layout of a distribution warehouse. A simulation environment using Gazebo was developed and used for testing the implementation of the route-planning system.  
Computational results showed that the proposed machine learning methodologies were able to generate routes with testing accuracy up to 98% for a practical internal layout of a warehouse with 18 storage racks and 67 path segments. Managerial insights into how the machine learning configuration  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**ANNs**} & \multicolumn{2}{c}{**1000**} & \multicolumn{2}{c}{**1448**} \\ \cline{2-5}  & **Training** & **Testing** & **Training** & **Testing** \\ \hline
67-60-21 & 99.7 & 94.00 & 99.7 & 94.83 \\ \hline
67-67-21 & 99.7 & 93.50 & 99.7 & 95.52 \\ \hline
67-70-21 & 99.7 & 96.00 & 99.7 & 96.90 \\ \hline
67-73-21 & 99.7 & 93.50 & 99.7 & 93.79 \\ \hline
67-67-21-21 & 99.7 & 95.50 & 99.7 & 97.93 \\ \hline
67-67-30-21 & 99.7 & 96.00 & 99.7 & 97.24 \\ \hline
67-70-30-21 & 99.7 & 96.50 & 99.7 & 97.93 \\ \hline
67-70-40-21 & 99.25 & 96.00 & 99.65 & 96.55 \\ \hline
67-67-21-10-21 & 90.00 & 91.50 & 91.19 & 88.62 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The effect of the number of training samples and ANN configuration on the testing accuracy for an actual layout.  
can be selected were also provided. The route-planning system can be used to generate routes for different types of AGVs in real time. This is beneficial to the transportation industry, since there will be a direct reduction in labor cost and an increase in operational efficiency, which is essential to be competitive in today's business environment. A limitation of our proposed approach is based on the required training datasets for the ANN. If the datasets need to be collected from an actual operation, this might require a long data collection period. However, simulated datasets can also be used, which can help accelerate the ANN training process. A possible future research area includes the consideration of other routing problems such as the vehicle routing problem. In this research, each AGV was assumed to make one stop according to the internal movement within a warehouse. However, in a typical vehicle routing problem, a vehicle has larger capacity and can make multiple stops. This requires the consideration of demands and vehicle capacity in an ANN training process, where suitable data encoding for input and output datasets need to be determined.  
Conceptualization, N.N.; data curation, T.T.H. and D.N.D.; funding acquisition, N.N.; methodology, N.N., T.T.H. and D.N.D.; project administration, D.N.D. and N.N.; software, N.N., T.T.H. and D.N.D.; writing--original draft, T.T.H. and D.N.D.; writing--review and editing, N.N., T.T.H. and D.N.D. All authors have read and agreed to the published version of the manuscript. This research was funded by Thammasat University.  
This study was supported by the Thammasat University Research Fund, Contract No. TUGR 2/33/2562.  
The authors declare no conflict of interest.