## 7 Analysis of the Results  
The analysis of the results from the computational experiments has been carried out using R [33]. Tables 2-4 present the results for the different levels of dynamism, from the lowest to the highest. The first column identifies the instances, while the remaining columns show the total time (in seconds) required by an AGV to complete all the assigned tasks for the three considered approaches. First, we report the results obtained for the FIFO approach, where the tasks are completed in a FIFO manner. In the next section of the table, we report the obtained results for the non-reactive approach. The first column shows the total time when the tasks are completed in the order returned by the non-reactive heuristic for dynamic delays, while the second column compares the average FIFO solutions (OBF) with the average non-reactive solutions (OBN). The last section of the table exhibits the obtained results for the reactive approach. The first column shows the total time when the tasks are completed in the order returned by the reactive heuristic, while the second column compares the average FIFO solutions (OBF) with the average reactive solutions (OBR). Finally, the average values are shown in the last row. \begin{table}
\begin{tabular}{l r r r r r} \hline \hline \multirow{2}{*}{**Instance**} & \multicolumn{2}{c}{**FIFO Approach**} & \multicolumn{2}{c}{**Non-Reactive Approach**} & \multicolumn{2}{c}{**Reactive Approach**} \\ \cline{2-5}  & **OBF** & **OBN** & **GAP(\%)** & **OBR** & **GAP(\%)** \\  & **[1]** & **[2]** & **[1–2]** & **[3]** & **[1–3]** \\ \hline Small & 86,735.7 & 82,403.1 & \(-\)5.00 & 82,236.4 & \(-\)5.19 \\ Medium & 173,105.4 & 166,014.1 & \(-\)4.10 & 164,528.6 & \(-\)4.95 \\ Large & 346,699.7 & 335,395.6 & \(-\)3.26 & 328,056.4 & \(-\)5.38 \\ Very large & 691,926.7 & 677,652.9 & \(-\)2.06 & 655,090.7 & \(-\)5.32 \\ \hline Average: & 324,616.9 & 315,366.4 & \(-\)3.6 & 307,478.0 & \(-\)5.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Computational results for the test instances with a low level of dynamism. \begin{table}
\begin{tabular}{l r r r r r} \hline \hline \multirow{2}{*}{**Instance**} & \multicolumn{2}{c}{**FIFO Approach**} & \multicolumn{2}{c}{**Non-Reactive Approach**} & \multicolumn{2}{c}{**Reactive Approach**} \\ \cline{2-5}  & **OBF** & **OBN** & **GAP(\%)** & **OBR** & **GAP(\%)** \\  & **[1]** & **[2]** & **[1–2]** & **[3]** & **[1–3]** \\ \hline Small & 87,921.7 & 83,161.8 & \(-\)5.41 & 82,761.6 & \(-\)5.87 \\ Medium & 175,289.6 & 168,075.4 & \(-\)4.12 & 165,001.2 & \(-\)5.87 \\ Large & 351,553.2 & 338,148.2 & \(-\)3.81 & 329,533.5 & \(-\)6.26 \\ Very large & 699,450.7 & 681,942.1 & \(-\)2.50 & 656,655.8 & \(-\)6.12 \\ \hline Average: & 328,553.8 & 317,831.9 & \(-\)4.0 & 308,488.0 & \(-\)6.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Computational results for the test instances with a medium level of dynamism. As expected, all the percentage gaps are negative, which indicates that the non-reactive and reactive approaches outperform the FIFO approach. The objective function value increases with the size of the instance, as increasing the number of assigned tasks requires more time from the AGV to complete them. The percentage gaps of the solutions obtained with the non-reactive and reactive approaches with respect to the FIFO approach tend to be high in absolute terms. In particular, the percentage gaps of the solutions obtained with the reactive approach with respect to the FIFO approach are the highest. In other words, the reactive approach takes the least time to complete the assigned tasks, so the reactive approach can be considered the most effective approach to solve the described problem. The barplot in Figure 5 shows the average gaps of non-reactive, and reactive approaches with respect to the FIFO approach considering the different levels of dynamism. Notice that the mean gap is always higher, in absolute terms, for the reactive approach. In addition, the average gaps increase as the level of dynamism grows.