Analysis of a Queuing System in a National Museum using Monte Carlo simulation.

Statistics and Actuarial Science

The research describes the visitor’s flow in a system’s model and ways in which the system is constrained by the existing counters, personnel, and equipment. Despite the fact that several predictive algorithms have been developed in an effort to reduce museum crowding. Such predictive models included Model Predictive Control, which was established by Stephen Boyd and Yang Wang aimed at controlling the queuing systems. The model was not effective since it’s slow in predicting elements in a queue. But, this study has combined simulating concepts with queuing system to generate an appropriate queuing model, simulating concepts looked into the simulating concepts which handles problems that are difficult to be solved analytically. The study found that NMK is using a single multi-server queuing system having 4 cashiers and the mean arrival rate of visitors is 2 minutes per visitor while the service rate is 3 minutes per visitor. On simulation, holding the rates of arrival constant while varying cashiers’ numbers, the study showed that with the presence of 5 cashiers the cumulative visitor waiting to receive the service have reduced such that in the presence of 6 cashiers the cumulative visitor at some durations is zero. The simulation showed that the required number of cashiers to enhance the quality of services NMK offers has to be increased from 4 to 5.

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