## 1 Introduction  
Emergency Departments (EDs) play a pivotal role in enhancing the quality of healthcare services within hospitals [1; 2]. However, effectively allocating essential resources such as doctors, nurses, and equipment in the unpredictable and often chaotic environment of overcrowded EDs remains a complex challenge [3]. Traditional mathematical approaches often fall short in dynamically describing the relationships among resources and adapting to changes, making them a major cause of extended patient resident time (length of stay: LoS) [4; 5; 6]. Patient wait time and LoS are critically important in urgent medical situations, such as surgeries [7; 8], and are influenced by various factors including treatment time, hospitalization duration, and decision-making processes [9]. The LoS to receive treatment directly impacts the overall duration of their stay in the ED: the shorter the wait times, the shorter the total stay [10; 11]. Thus, there is a demand for scheduling strategies that can effectively reduce LoS. Simulation has proven to be an effective tool for improving complex systems such as medical processes and EDs, effectively addressing the challenges of resource management and irregular patient arrivals [12; 13; 14]. Research predominantly focuses on utilizing Discrete Event Simulation (DES) and advances in the field of data science [6; 15; 16; 17; 18]. Most studies aim to strategically determine how many additional resources are needed to alleviate the bottlenecks caused by patient congestion [19; 20]. However, realistically, increasing resources is challenging due to financial constraints [21], necessitating research focused on optimizing existing personnel and equipment resources. Studies on constrained resource scenarios are increasingly focusing on personnel resource scheduling [22]. Research on scheduling medical staff in EDs remains limited. This study emphasizes utilizing existing physician resources to address scheduling challenges. Moreover,traditional resource scheduling often relies on seasonal or past data [23], which is insufficient due to the unpredictable nature of patient demand. Therefore, flexible research that considers real-time ED conditions is required. Differences in physicians' experience can influence treatment times, offering a potential to reduce wait times. Consequently, a resource scheduling approach based on the sudden influx of patients and the varying experience of physicians is needed. Moreover, optimizing scheduling requires machine learning-based strategies that utilize data from randomly arriving patients. This research aims to develop a real-time resource utilization scheduling method based on admissions, patient, and physician information within the ED environment, and to build a simulation model that is applicable to various studies. The objectives include: developing an ED simulation using DES, providing six scheduling policies based on physician experience, and employing machine learning to select the most appropriate scheduling policy. This study aims to develop a generic simulation model that can be applied not just to specific hospitals but across various reusable systems [24]. Using comprehensive ED visit data from the Korean National Emergency Department Information System (NEDIS), we design and conduct experiments to validate our model, ultimately facilitating optimal scheduling policy that improves the LoS. LoSs are a critical factor directly influencing the quality and efficiency of healthcare services. Shorter wait times are correlated with enhanced patient satisfaction and improved treatment outcomes, positively impacting key healthcare quality indicators. These indicators include patient satisfaction, treatment outcomes, and rates of readmission, all of which tend to improve as wait times decrease. Additionally, efficiency metrics such as medical staff productivity, patient processing time, and resource utilization also benefit from reduced wait times, thereby enhancing the overall operational efficiency of healthcare systems [7; 9; 10; 14].