## 1 Introduction  
In recent years, advances in science and technology have increasingly shifted the manufacturing industry toward'multiple varieties, small batches, and customization' [1]. Enterprises need to enhance production efficiency to reduce costs effectively. [PERSON] et al. developed an intelligent scheduling framework addressing complex issues such as optimizing lock progress, ship entry times, and ship positioning within lock cabins [2]. [PERSON] et al. introduced a reinforcement learning (RL) model to improve scheduling performance in flexible job shop problems (FJSPs) [3]. [PERSON] et al. proposed a deep reinforcement learning-based scheduling method to address workshop production challenges posed by crane complexity and dynamics [4]. [PERSON] et al. proposed an intelligent scheduling reconfiguration (ISR) method leveraging machine learning to automate reconfiguration processes [5]. [PERSON] et al. developed an AGV scheduling application utilizing the Spring framework [6]. [PERSON] et al. designed a model-free, end-to-end scheduling agent capable of interacting with cloud environments, outputting virtual machine task details, and handling cloud platform tasks [7]. [PERSON] et al. implemented an intelligent scheduling model and optimization algorithm in mining operations, achieving autonomous vehicle path planning [8]. [PERSON] et al. proposed an intelligent scheduling system. By using the DSSM algorithm [9]. [PERSON] et al. developed an intelligent model for engine manufacturing and scheduling [10]. [PERSON] et al. introduced deep reinforcement learning to schedule automated production lines, avoiding manual feature extraction and overcoming the lack of structured data sets [11]. [PERSON] et al. developed an intelligent job scheduling method that can quickly and effectively generate process plans by combining the advantages of digital twins and hypernetworks [12]. [PERSON] et al. proposed a bulk cargo loading scheduling efficiency optimization method based on deep reinforcement learning [13]. [PERSON] et al. proposed an intelligent scheduling method for the energy-saving operation of multiple trains based on a genetic algorithm and regenerative kinetic energy [14]. The pulsating assembly flow shop represents a significant approach to enhancing assembly efficiency in modern manufacturing. It is characterized by advanced industrialization, robust production rhythms, and high levels of digitization. This method is widely utilized in modern manufacturing, particularly within the aerospace industry. In 2000, Boeing pioneered the first pulsating assembly line, employing it for the production of Apache helicopters. The workflow of a pulsating assembly flow shop, illustrated in Figure 1, encompasses several stages. These stages include preliminary preparation, pulsation assembly line design, and continuous improvement [15]. The scheduling problem for pulsating assembly flow shops is a critical aspect of production planning. However, in practical scenarios, the complexity of assembly and resource constraints often result in assembly line imbalances. Assembly line balancing represents a typical NP-combinatorial optimization problem. This challenge has been extensively studied by researchers. [PERSON] et al. developed a mixed-integer linear programming method to enhance class E assembly line efficiency by optimizing station numbers and cycle times [16]. Similarly, [PERSON] et al. investigated welding process assembly lines, addressing multiple identical tasks in simple assembly line balancing problems through a mixed-integer linear programming model and branch-and-bound approach [17]. [PERSON] et al. studied the multi-AGV scheduling problem with unloading safety detection in a matrix manufacturing workshop [18]. [PERSON] et al. proposed an indoor AGV positioning method combining vision and ultra wideband [19]. [PERSON] et al. designed an intelligent manufacturing assembly line layout optimization model considering AGV path planning [20]. [PERSON] et al.