# Dynamic Bottleneck Identification of Manufacturing Resources in Complex Manufacturing System
## 2 Related Works
However, there are many disturbing factors in the mixed production model of CMS that will make the production system change dynamically and lead to bottleneck shift. To overcome the shortcoming of the fixed bottleneck identification method and identify bottlenecks in dynamic systems timely and accurately, some scholars proposed a dynamic bottleneck identification method based on active machine duration [32] and extended it to unstable discrete manufacturing systems [38]. This method can identify the average bottleneck and the instantaneous bottleneck from the system's point of view [8]. It can identify the bottleneck quickly and accurately in the system. However, there are two main research gaps in this method. The first is the lack of in-depth study of machine states \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**References**} & \multicolumn{6}{c}{**Keywords**} & \multirow{2}{*}{**Data Sources**} & \multirow{2}{*}{**Year**} \\ \cline{2-2} \cline{6-7}  & **1** & & & & & **3** & **4** & **5** \\ \hline
[7] & \(\surd\) & & \(\surd\) & \(\surd\) & \(\surd\) & Automotive powertrain assembly line & 2021 \\
[8] & \(\surd\) & \(\surd\) & & & & & Base case benchmarks & 2020 \\
[11] & \(\surd\) & & & & & & & Base case benchmarks & 2016 \\
[12] & \(\surd\) & & & & & & & OR Library & 2016 \\
[13] & \(\surd\) & & \(\surd\) & \(\surd\) & & & Micro production system & 2019 \\
[14] & \(\surd\) & \(\surd\) & \(\surd\) & & & & Manufacturing execution system & 2019 \\
[15] & \(\surd\) & \(\surd\) & \(\surd\) & & & & Real-world and simulation & 2020 \\
[17] & & & & \(\surd\) & \(\surd\) & & Simulation & 2009 \\
[19] & & & \(\surd\) & \(\surd\) & & & Simulation & 2000 \\
[20] & & & \(\surd\) & \(\surd\) & & & Simulation & 2009 \\
[24] & \(\surd\) & \(\surd\) & \(\surd\) & & & Manufacturing execution system & 2016 \\
[26] & & & & \(\surd\) & \(\surd\) & & - & 2010 \\
[27] & & & & \(\surd\) & \(\surd\) & Simulation & 2015 \\
[28] & \(\surd\) & & & \(\surd\) & & Plant simulation & 2016 \\
[36] & \(\surd\) & & & & & Manufacturing shop & 2009 \\
[37] & \(\surd\) & \(\surd\) & \(\surd\) & & & & Robert Bosch GmbH & 2014 \\ \hline \hline \end{tabular}  
* Notes: 1—Dynamic bottlenecks; 2—machine states; 3—CMS; 4—Buffers; 5—Turn points.  
\end{table} 
Table 1: Summary of bottleneck identification. and that most of the data used in their research come under ideal assumptions that are not close to the actual production. The second, is that the decoupling effect of the buffer zone on the system is not considered, which can easily cause confusion and misjudgment of bottlenecks. A simulation and data hybrid-driven bottleneck identification method is proposed based on a fine-grained machine state and effective buffer (DBI-BS) to bridge these gaps. First, this method forms a fine-grained state of manufacturing resources by dividing the machine's active duration [39] in more detail and building a time series flow model of the manufacturing process to record the fine-grained state transition. Then, an improved bottleneck quantification model is proposed based on the turning point theory. Finally, it establishes a simulation model whose output can serve the DBI-BS according to the actual workshop to verify the proposed method. In addition, effective buffers are used to decouple the CMS to avoid misjudgments of system bottlenecks.