## 3 Manufacturing System Bottleneck Research
### Bottleneck Identifications
#### 3.1.2 Bottleneck Identification Based on Simulations  
In the face of complex manufacturing systems, traditional bottleneck identification methods often suffer from inaccuracies and inefficiencies. Therefore, [PERSON] and [PERSON] have proposed using simulation software to model and identify bottlenecks in manufacturing systems [63,64]. This approach has been widely applied in engineering practice and has achieved significant results. Simulation-based identification methods possess the capability to quickly and accurately identify system bottlenecks, aiming to provide more efficient solutions for the design, analysis, and management of systems, thereby optimizing overall production performance. [PERSON] and [PERSON] proposed simulation-based identification methods that use external characteristics of the production line, such as starvation time [65], equipment utilization [66], buffer capacity [67], load-to-capacity ratio [68], throughput [26], and waiting time [69], to identify bottlenecks in manufacturing systems [70,71]. [PERSON], [PERSON], and [PERSON], who work in Toyota Central R&D Labs, were the first to propose using GAROPS Analyzer simulation software to model production scenarios in discrete manufacturing workshops [72]. [PERSON] further developed this approach by utilizing the C-MORE system to concentrate on improving General Motors' manufacturing operations, achieving a more effective identification of bottlenecks in the manufacturing network [63]. The development of software technology has significantly advanced the in-depth research on bottleneck identification in simulation systems. Currently, simulation software such as AnyLogic, Arena, and Flexsim has become an important tool in bottleneck identification research. [PERSON] used the simulation software AnyLogic to construct a one-to-one mapping of discrete workshop production processes, integrating equipment status to identify dynamic bottlenecks in the workshop [49]. [PERSON] and [PERSON] performed a detailed analysis of order duration and inventory levels based on AnyLogic, and they established a simulation-based model for identifying manufacturing system bottlenecks [73]. The AnyLogic software established by [PERSON] is developed with Java as the underlying logic and possesses powerful discrete event simulation capabilities, making it widely used for bottleneck identification and analyses in discrete manufacturing systems [74]. [PERSON], [PERSON], and [PERSON] used Arena simulation software in conjunction with constraint theory to identify bottlenecks and validate the feasibility of simulation-based bottleneck identification methods. [PERSON] and his research group members proposed the concept of a global system based on the simulation software Flexsim and introduced a combined optimization method for shifting bottleneck factors to determine the optimal bottleneck location to maximize system benefits [75].  
Compared with the static model, the main advantage of simulation-based bottleneck identification is that it can solve the bottleneck identification problem in complex production lines to a certain extent. However, this approach still has limitations, because the accuracy of the results depends on the software performance and its degree of fit with the actual manufacturing system conditions. [PERSON] believes that in complex systems, the causes of bottlenecks are often the result of multiple interacting factors, and a singular simulation-based identification method may lead to erroneous simulation outcomes, thereby failing to accurately identify the bottlenecks [76].