# Designing Automated Logistics Warehouse Stackable Bidirectional Infinite-Loop Modules
## 5 Results
### Experimental Results Using Virtual Bottleneck Phenomenon in Inventory Logistics  
The results of experiments conducted with a virtual bottleneck phenomenon are presented in Table 5. For experiments where a high input rate occurred, 70% of the total logistics volume was allocated for input, while for experiments with a low input rate, 30% of the total logistics volume was allocated for input, based on a maximum total volume of 1800 PLT. According to the experimental findings, there was an approximate 73.31% difference in time-based probability between the to-be stacker crane and as-is fork lift models. Additionally, a time-based probability difference of around 38.39% was observed between the to-be stacker crane and as-is stacker crane models.  
Figure 7: Comparison of average response time for calls at equal ratios of specified logistics quantities between models.  
Figure 8 visually represents the results of the experiments on the call durations of logistics using a simulated bottleneck phenomenon. In Figure 8a, the results are represented based on the total logistics volume of 100, 300, 600, 1000, and 1800 PLT, with each scenario allocating 30% of the total logistics volume for input. It is evident that the to-be stacker crane model consumed less time compared to the as-is stacker crane and as-is fork lift models. Figure 8b illustrates the results when 70% of the total logistics volume, based on 100, 300, 600, 1000, and 1800 PLT, is allocated for input. Again, the to-be stacker crane model exhibits lower time consumption compared to the as-is stacker crane and as-is fork lift models. Through these observations, it is confirmed that the developed model approach is highly time efficient.  
Figure 8: (**a**) Comparison graph of logistics call lead time using bottleneck phenomena: experimental results (outbound 7); (**b**) comparison graph of logistics call lead time using bottleneck phenomena: experimental results (outbound 3).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Logistics Shipment Quantity (PLT)**} & \multicolumn{4}{c}{**Average Time (h)**} \\ \cline{2-5}  & **3:7** & **7:3** & **5:5** \\ \hline \multirow{4}{*}{100} & As-is fork lift & 4.56 & 10.64 & 7.58 \\ \cline{2-5}  & As-is stacker crane & 1.97 & 4.61 & 3.25 \\ \cline{2-5}  & To-be stacker crane & 1.21 & 2.84 & 2.03 \\ \hline \multirow{4}{*}{300} & As-is fork lift & 13.75 & 32.09 & 22.82 \\ \cline{2-5}  & As-is stacker crane & 5.95 & 13.88 & 9.73 \\ \cline{2-5}  & To-be stacker crane & 3.63 & 8.49 & 6.09 \\ \hline \multirow{4}{*}{600} & As-is fork lift & 27.57 & 64.33 & 45.69 \\ \cline{2-5}  & As-is stacker crane & 11.89 & 27.76 & 19.43 \\ \cline{2-5}  & To-be stacker crane & 7.29 & 17.02 & 12.15 \\ \hline \multirow{4}{*}{1000} & As-is fork lift & 45.79 & 106.84 & 76.32 \\ \cline{2-5}  & As-is stacker crane & 19.87 & 46.37 & 33.12 \\ \cline{2-5}  & To-be stacker crane & 12.17 & 28.40 & 20.28 \\ \hline \multirow{4}{*}{1800} & As-is fork lift & 82.39 & 192.25 & 137.32 \\ \cline{2-5}  & As-is stacker crane & 35.73 & 83.37 & 59.55 \\ \cline{2-5}  & To-be stacker crane & 21.91 & 51.14 & 36.53 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Experimental results on logistics call lead time using bottleneck phenomena.