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This systematic literature review examines queue system simulation in hospitals across four key service areas: emergency departments, outpatient clinics, laboratories, and pharmacies. Following PRISMA methodology, 72 relevant studies (2015-2020) were analyzed to identify simulation models, software tools, performance parameters, and emerging trends. Findings reveal Discrete-Event Simulation dominance (59.7%), with increasing hybrid model adoption integrating System Dynamics and Agent-Based approaches. Emergency departments remain the primary application focus (52.8%), while Arena and AnyLogic emerged as predominant simulation platforms. Patient waiting time (91.7%) and resource utilization (77.8%) constitute the most evaluated performance metrics. Technological convergence trends demonstrate integration of real-time data analytics, machine learning, and digital twin concepts into traditional simulation frameworks. This review contributes methodological insights for optimizing hospital queueing systems while identifying research gaps in cross-departmental model interoperability and comprehensive value-based performance evaluation within contemporary healthcare systems.  

The problem in the industrial sector that is often encountered is the problem of queuing. Queues occur when there is an imbalance between services and services offered, causing consumers to wait to get service. Laboratory queue simulation using Arena software. This research was carried out with the aim of analyzing the queuing conditions that occurred during the material A testing process at the XYZ Laboratory through simulation using Arena software. After carrying out a simulation with the Arena software, it was discovered that there were no queues for the material A testing queue activity at the XYZ Laboratory. From the results of the simulation data processing for material A testing queues in the XYZ laboratory, there are no queues and service times are optimal, so there is no need for corrective solutions. Based on the results of verification and validation, H0 is accepted because the value 0 is in the range μ1 – μ2 so it can be said to be valid. This states that the total time in the real situation (real system) is not significantly different from the output results in the Arena simulation that has been created.