Excessive wait times for admission to inpatient wards can lead to overcrowding in the departments where patients await admission. This issue is particularly concerning in emergency departments (ED) as it can prolong overall inpatient stays, higher mortality rates, and other undesirable events due to extended wait times. To gain insights into reducing these wait times and provide effective management strategies for hospital administrators, we conducted a real-world study examining varying wait times across different service departments within the hospital. This research focuses on wait times, specifically examining the average time patients wait in line until admission to a bed in the steady-state (nonzero wait time) for each service department. We aim to optimize bed allocation in the hospital to minimize total wait times across all departments using a heuristic method. In this research, queueing theory and simulation modeling were employed to analyze the hospital's wait times. Our findings revealed that: (1) the metrics results of simulation models for dynamic arrival rates with minimal variations (within 10%) closely matched the results of queueing theory with static arrival rates, and (2) the optimal bed allocation solutions from both methods using the simulated annealing algorithm were not exactly identical. This study demonstrates that both queueing theory and simulation models are effective in capturing inpatient flow and producing comparable metrics for dynamic arrival rates with minimal variation. By understanding these methodologies, healthcare organizations can determine which approach to utilize in similar real-life scenarios, based on their desired level of analysis and computational expedience.