# A Study on the Design and Control of the Overhead Hoist Railway-Based Transportation System
## 7 Discussion  
Table 19 provides a comprehensive summary of the research that has been published. [PERSON], [PERSON], and [PERSON] (2007) [23] and [PERSON] et al. (2015) [24] employed a Petri nets (PNs) framework to analyze the behavior of OHT (overhead hoist transport) vehicles within a 300 mm OHT loop. An integer programming methodology was employed by the researchers to effectively capture the occurrence of congestion and the predetermined routes of OHTs. In their study, [PERSON], [PERSON], and [PERSON] (2007) [25] employed a genetic algorithm (GA) to identify a blended rule for lot dispatching. In addition, they utilized the Markov decision model to optimize the routing of OHT vehicles. Despite demonstrating dynamic routing algorithms, the researchers used a separate overhead hoist transport (OHT) system and only accounted for a limited number of OHTs. It should be noted that under a segregated overhead hoist transport (OHT) system, the rails are physically partitioned into various loops, with each loop having a restricted number of OHTs. As a result, research on segregated overhead transportation (OHT) systems usually examines a sample size of less than 20 OHTs.  
[PERSON] et al. (2008) [27] introduced a novel approach to dynamic routing in a unified overhead hoist transport (OHT) system within the context of 300 mm wafer fabs. By adapting to the dynamic traffic environment, the dynamic routing method reduces traffic congestion and achieves fast lot delivery. The MOGPRG and dynamic routing method improve mean cycle time and tardy rate, demonstrating their effectiveness in fan performance. In their study, [PERSON] et al. (2014) [28] presented a congestion-aware dynamic routing strategy that was demonstrated in automated material handling systems  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Reference** & **Track Layout** & **Methodology** & **Number of OHT** \\ \hline [PERSON], [PERSON], and [PERSON] [23] & Segregated & Petri nets and integer programming & 20 \\ [PERSON], [PERSON], and [PERSON] [25] & Segregated & Markov decision model & 10 \\ [PERSON] et al. [27] & Unified & K-shortest path algorithm & 20 \\ [PERSON] et al. [28] & Dijkstra with dynamic edge weight & 250 programming & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 19: Summary of studies on dynamic routing of OHT vehicles in semiconductor fabs [26].  
Figure 61: Shuttle 2 runs after waiting for Shuttle 1 to enter Station 3.  
Figure 62: Shuttle 2 arrives in the unloading station.  
(AMHS) for semiconductor manufacturing wafer transport. [PERSON] et al. (2023) [29] had also developed a look-ahead dispatching algorithm considering arrival times of future requests and vehicle availability. A Mixed integer linear programming (MILP) model and discrete-event simulation model are tested, showing proactive dispatch improves AMHS management by decreasing lot waiting time.By rerouting vehicles efficiently when the congestion status changes, this strategy improves the steady-state routing performance, reduces heavy congestion frequency, and recovers from vehicle breakdowns more efficiently. The algorithm employed in this study involves regularly updating the predicted traversal time of an edge each time a vehicle travels over it. Moreover, a Dijkstra algorithm is used to determine the optimum path at each intersection encountered by OHT cars. The researchers conducted high-fidelity simulation studies on a practical-scale track network and observed a significant decrease in the occurrence of heavy congestion.