# Dynamic Bottleneck Identification of Manufacturing Resources in Complex Manufacturing System
## 1 Introduction  
The fusion of multiple technologies (manufacturing IoT [1], digital twins [2], big data [3], neural networks [4], etc.) has brought great opportunities and challenges to the transformation of manufacturing [5]. The new industrial revolution, commonly known as Industry 4.0, originated in Germany has arrived [6]. Increasing productivity is one of the explicit goals set forth by Industry 4.0 originated in Germany [7]. In addition, the deteriorating new crown epidemic has dramatically damaged the production capacity of the global manufacturing industry, so there is an urgent need to increase the output of the current manufacturing system. Higher productivity is ranked as the number one technology investment priority for manufacturing companies for the next decade [7]. The change in manufacturing mode and the need to improve manufacturing capabilities have significantly increased the complexity of manufacturing, which has brought considerable challenges to the production management of the workshop [8]. In this situation, the CMS has been widely used in manufacturing enterprises [7]. CMS allows a rapid response to fluctuations inside and outside the workshop and comprehensive utilization of existing manufacturing resources to increase system productivity. A typical CMS is a hybrid process workshop (HFS) consisting of several serial stages with parallel machines. The job must be processed at every stage on one machine. According to the theory of constraints (TOC) [9], there is a stage that limits the entire production capacity or significantly limits the throughput of the workshop, which is called the bottleneck stage [3]. The bottleneck is defined as the equipment that has the mostsignificant impact on the overall performance of the production system. In the case of limited resources, priority is given to improving the production of bottleneck equipment or allocating resources. For example, defining the maintenance of bottleneck equipment as the highest priority can effectively improve the overall performance of the CMS. However, there are many challenges in effectively identifying bottlenecks in complex systems. First, the strongly coupled elements of the CMS [10] can increase the difficulty of bottleneck identification. Second, disturbances in the production process lead to a constant drift of bottlenecks [8]. Third, the lack of accurate bottleneck quantification methods makes it difficult to determine the degree of different bottlenecks restricting the system [11]. To deal with the above problems, this paper proposes a simulation and data hybrid-driven method (DBI-BS) to identify the dynamic bottlenecks in CMS. In addition, the DBI-BS method proposed in this paper is verified in a discrete simulation system of authentic CMS processes. The contributions of this paper are summarized as:  
1. The effective buffers are used to decouple CMS to avoid the coupling between the system's various elements, causing bottleneck misjudgments. The definition and identification method of the effective buffer zone are also given. 2. The data-driven method is used to identify the dynamic bottleneck, and a data-driven dynamic bottleneck model is established. The equipment operating state is further divided into fine-grained divisions to improve identification accuracy, and it is judged whether the state is effective. 3. Using the actual production data of the workshop to guide the simulation model, the production logic relationship between the manufacturing entity and the simulation agent is clarified, and the simulation model is closer to the actual production. The rest of this paper is organized as follows. Section 2 reviews the literature of bottleneck identification. Section 3 introduces the CMS and its decoupling methods, and the proposed dynamic bottleneck identification method (DBI-BS) is also presented in this section.