<![CDATA[The increasing number of motor vehicles each year has led to a growing demand for fuel at Public Fuel Filling Stations (SPBU). This condition causes long queues, especially when the service capacity is not balanced with the rate of vehicle arrivals. This study aims to analyze the efficiency of the SPBU service system using a multi-channel queueing model (M/M/c) combined with Monte Carlo simulation to estimate vehicle waiting times stochastically. Data were obtained through direct observation, including arrival times, service times, and the number of active service channels. Modeling and simulation were carried out using Python with the NumPy, Pandas, and Matplotlib libraries. The results show that the actual average waiting time was 81.92 minutes, while the Monte Carlo simulation produced an average of 87.94 minutes, with a difference of only 7.35%, indicating a high level of accuracy. The system utilization value (ρ) of 1.05 indicates an overloaded condition that leads to increased customer waiting times. The simulation results also show that adding one service channel can reduce the average waiting time by up to 30%. Thus, the combination of the M/M/c model and Monte Carlo simulation proves effective in describing the stochastic behavior of the queueing system at SPBUs and can serve as a basis for improving service efficiency.]]>
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