## 1 Introduction  
Factory automation and logistics optimization are crucial for enhancing the efficiency and productivity of factories. Logistics automation is a dynamic area of research [1]. Advanced technologies such as intelligent stockers have been introduced to automatelogistics in factories and improve the efficiency of movement and storage of goods in factories. Optimizing the route selection, goods placement, storage volume, and work order of the robot is crucial for effectively using these technologies [2]. Reinforcement learning (RL) is an effective approach for solving complex problems by learning through trial and error. In particular, using RL in factory simulations is highly promising in optimizing logistics automation [3]. Considering industry trends that focus on streamlining production processes for new products and automating logistics is critical. These trends indicate that logistics systems in factories are becoming increasingly organized and automation technologies are advancing [4]. Logistics automation is a critical component of factory operations, and an efficient logistics system considerably affects the productivity and competitiveness of a factory. Studies have focused on optimizing logistics automation systems using RL based on artificial intelligence [5]. RL algorithms can improve and optimize logistics automation processes in factory simulations. However, automating logistics in factories can be expensive. The optimization of suitable requirements, the implementation of real-time autonomous judgment and autonomous driving technologies, and the use of RL, a sequential action decision technique, are crucial methodologies for addressing these concerns. However, challenges such as insufficient training data, the high cost of trial and error, and potential risk of accidents resulting from trial and error remain [6]. These systems can increase the efficiency of the logistics system of a factory. However, many factories have yet to use them properly [7]. This methodology for optimizing storage in logistics automation combines factory simulation with artificial intelligence (AI) technology. The method predicts various problems that may occur on the production line to improve efficiency [8]. An RL model simulates various scenarios and continuously adjusts to determine the optimal solution and optimize storage. Thus, analyses of the data required for logistics operations can be performed to determine the optimal response. The RL model analyzes the quantity, type, and quality of goods in the production line. Based on this result, data related to the operational patterns of logistics automation robots are collected and continuously adjusted. RL models can be used to determine optimized routes that efficiently use various resources required for logistics tasks, reduce work time, and prevent problems from occurring on the production line [9]. Complex processes from real factory operations can be modeled in a digital environment by combining factory simulation with RL to improve work efficiency and logistics processes within the factory [10]. This approach can improve overall factory productivity. Moreover, optimizing logistics automation can save labor and time and cut down expenses. In particular, in logistics automation, we propose optimizing the total capacity of the stocker, a storage device, which is expected to show savings of approximately 30% or more and increase the overall efficiency of the logistics system. The contributions of this paper are as follows. Optimizing logistics automation is critical to increasing productivity and getting products to market. Reinforcement learning (RL) algorithms can improve and optimize the logistics automation process in factory simulations because they use trial and error until the optimal solution for logistics automation is determined. This can be used as a guideline for developing effective simulation solutions to improve logistics efficiency. The remainder of this paper is organized as follows.