## 4 Simulation and Results
### Results  
To evaluate the performance of the model trained in this experiment, we compared the conventional FIFO method of distributing items with the method of distributing items using the trained RL model. We subsequently assessed the performance evaluation and metrics. Figure 18 shows the simulation results by type after 10 iterations using FIFO. Figure 19 illustrates the results of a type-specific simulation with 10 iterations using RL. Table 2 gives the results by type for a typical simulation using the FIFO method. The average latency for each type was similar. Figure 19: RL application type. Figure 18: FIFO type. Table 3 shows the result for FIFO to be 7304 inputs per day, with an average waiting time of 3348 s. The average waiting quantity was 272 pieces. Next, we applied RL and evaluated the outcomes after 10 iterations. Tables 2 and 3 display the daily production, average lead time, stocker average wait time, and stocker average wait time when items were distributed using the FIFO method. Table 4 shows the simulation results by type using RL. The application of RL resulted in a reduction of more than 30% in the average waiting time for each type. Table 5 shows the results of applying RL, which yielded 7058 inputs per day and an average waiting time of 145 s. The average number of items in the queue was 12. Tables 4 and 5 display the daily production, average lead time, average waiting time for stockers, and average waiting quantity for stockers when items were allocated using the trained RL method. Upon comparing Tables 2 and 3 with Tables 4 and 5, the daily production increased as the average waiting time for the stockers and the average waiting quantity decreased when the model with the RL method was used.Therefore, the performance evaluation metrics improved in the model that applied RL to allocate items compared with the model in which FIFO was used. When using RL to manage the supply of products from the stocker to the router, the average production of items increased and the lead time decreased compared with the conventional FIFO method. On average, the number of queues in the stocker was approximately 273 in the FIFO scenario. However, this number reduced to approximately 12 queues when using RL. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Item**} & \multirow{2}{*}{**Input**} & \multirow{2}{*}{**Output**} & \multicolumn{2}{c}{**Average**} \\ \cline{4-5}  & & & **Lead Time (s)** & **Wait Time (s)** \\ \hline Type 1 & 1767 & 1767 & 159 & 106 \\ Type 2 & 1760 & 1767 & 233 & 176 \\ Type 3 & 1756 & 1756 & 204 & 152 \\ Type 4 & 1766 & 1766 & 198 & 148 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results for RL application type. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Item**} & \multirow{2}{*}{**Input**} & \multirow{2}{*}{**Output**} & \multicolumn{2}{c}{**Average**} \\ \cline{4-5}  & & & **Lead Time (s)** & **Wait Time (s)** \\ \hline Type 1 & 1766 & 1757 & 3351 & 3347 \\ Type 2 & 1760 & 1754 & 3406 & 3349 \\ Type 3 & 1756 & 1749 & 3400 & 3348 \\ Type 4 & 1754 & 1748 & 3400 & 3349 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results by FIFO. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Item**} & \multirow{2}{*}{**Input**} & \multirow{2}{*}{**Output**} & \multicolumn{2}{c}{**Average**} \\ \cline{4-5}  & & & **Lead Time (s)** & **Wait Time (s)** \\ \hline Type 1 & 1766 & 1757 & 3351 & 3347 \\ Type 2 & 1760 & 1754 & 3406 & 3349 \\ Type 3 & 1756 & 1749 & 3400 & 3348 \\ Type 4 & 1754 & 1748 & 3400 & 3349 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: FIFO average results. In the process of applying reinforcement learning, the metrics for convergence training were a key part of the study.