# Dynamic Bottleneck Identification of Manufacturing Resources in Complex Manufacturing System
## 2 Related Works  
In the past decades, bottlenecks, as one of the critical characteristics of the production system, have attracted the attention of many researchers, as shown in Table 1. However, most bottleneck studies were combined with other studies, such as balancing production lines based on bottleneck predictions [10], bottleneck-based shop scheduling [11], shifting bottleneck heuristics to re-optimize scheduled machines [12], a future bottleneck-based dispatching method [13], and a parallel gated recurrent units (P-GRUs) network and a data-driven prediction algorithm (using active period bottleneck analysis theory) [14] were developed for shifting bottleneck prediction [15]. These studies focus on the work after identifying the bottleneck and pay less attention to the research of the bottleneck itself. This leads to a lack of in-depth analysis into the bottleneck and its impact on the entire production system. It is worth noting that the accurate identification of bottlenecks is the key to improving the system's bottleneck stage and overall performance [16]. Rapid and accurate identification of bottleneck locations helps leverage limited manufacturing resources to increase productivity during the bottleneck phase, increasing system throughput and minimizing total production costs [17]. Previous studies on bottleneck identification can be divided into three categories: analytics-based, simulation-based, and data-driven methods. The analytics-based methods must make a series of assumptions and approximations, and the results are calculated by mathematical formulas [18], such as using the Bernoulli model [19]. However, analytical methods have many limitations in complex system analysis [19] and are limited to long-term steady-state bottleneck detection [20]. Many disturbances [21] and uncertainties [22] make the analytics-based methods less effective in identifying bottlenecks. In addition,analytics-based methods employ stochastic models and static statistics, lack flexibility, and cannot respond to system dynamics in a timely manner [16]. Therefore, the reliability of the bottleneck obtained using the analysis method needs to be further improved. The data-driven approach can respond to system dynamics [23]. Therefore, some scholars proposed some data-driven methods to overcome the shortcomings of analytics-based methods and identify the bottlenecks in dynamic systems timely and accurately. For instance, using real-time machine data provided by MES [24], a shifting bottleneck-driven heuristic algorithm [25], a turning point method [17], and its improvement [7] were proposed, which used machine states (blockage and starvation) and buffered content records to identify bottlenecks. The turning point method can detect the slowest machine in the system [26] and inspire maintenance strategies [27]. However, this method is dramatically impacted by buffering and easily misjudges bottlenecks, and it is unsuitable for a production system without buffering [24]. The simulation-based method can also make up for the deficiency of the analytics-based method [28]. The simulation model is closer to the actual production because the production states of equipment can influence each other, and static data can be changed into dynamic data by using some empirical distribution [29], such as downtime following the exponential distribution [30]. It can detect CMS bottlenecks and efficiently perform sensitivity-based analysis [31]. The simulation-based methods identify bottlenecks mainly by external characteristics (queue length [32], waiting time [33], output [34], blocking and starvation time [17], machine utilization [35], capacity to load ratio [36], buffer level [37], etc.) in the production line. These methods only consider workstations independently and ignore the workstation's impact on the whole system. These methods can only be used for fixed bottleneck identification and cannot identify a shifting bottleneck after the system has changed dynamically.