# Simulation-Based Performance Analysis for a Novel AVS/RS Technology with Movable Lifts
## 4 Analyses of the Novel AVS/RS/ML
### Design Scenarios and Performance Metrics of the Systems  
In this research, we propose a novel AVS/RS/ML--an AVS/RS with movable lifts (MLs). To investigate the system properly, some critical performance metrics are observed from the system. Performance metrics are defined to be throughput rate per month, average flow time of a transaction, average operating time of an AGV per transaction, and average utilization of devices (e.g., MLs and AGVs). Flow time of a transaction is defined to be the time from arrival of a transaction (storage/retrieval) at the system until completion of its process. Hence, that flow time metric also includes waiting time of transactions in queues. Operating time of an AGV is defined to be the time between when an AGV is seized by a transaction until it returns to its dwell point. For instance, when there is no waiting time for a transaction in the system, the flow time performance metric would always be smaller than the AGV's operating time. This is because AGV's travel time also includes travel time to its dwell point.  
Average utilization of an AGV and an ML are obtained by total busy time divided by total simulation time (i.e., one month). Table 1 shows the notations of the performance metrics and their considered units.  
These performance metrics are observed in all described system designs of AVS/RS/ML subject to different physical configurations. All these scenarios are detailed in the following sections. The physical configuration parameters are considered to be the number of tiers (\(T\)), number of storage columns (\(SC\)), capacity of warehouse (\(W\)), and the number of AGVs (\(N\)); see Table 2.  
All experimented scenarios along with their data considered are given in Table 3. In the first design scenario, as mentioned, two designs based on I/O point locations are denoted as \"system design\". We also denote two warehouse capacity scenarios (\(W\)) as small and large. In small and larger capacity cases, we consider that there are 900 and 1800 storage locations, respectively. The number of tiers (\(T\)) and the number of storage columns (\(SC\)) are predefined by dividing \(W\) by \(2\cdot T\) because there are racks on both sides of each aisle. There can be a larger number of AGVs in the AVS/RS/ML system. However, we model two and three AGVs in the single I/O point designs and two AGVs in the two I/O points design. Finally, we have 12 scenarios to analyse as given in Table 3. The simulated experimental results are also given in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Notation** & **Definition** & **Unit** \\ \hline \(t_{f}\) & Average flow time per transaction & seconds \\ \(t_{o}\) & Average operating time of an AGV per transaction & seconds \\ \(Util_{AGV}\) & Average utilizations of an AGV & \% \\ \(Util_{ML}\) & Average utilization of an ML & \% \\ \(\lambda\) & Average throughput rate & Number of transactions/month \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notation and definition of performance metrics.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Definition** \\ \hline \(T\) & Number of tiers in an aisle \\ \(SC\) & Number of storage columns in an aisle \\ \(W\) & Warehouse capacity of an aisle \\ \(N\) & Number of AGVs in an aisle \\ \hline \hline \end{tabular}
\end{table}
Table 2: Notations and definitions of parameters.