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Simulation of a Bank Queue

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Contents Problem Definition and Goals 3 About the Bank 3 Logical Model 4 Data Gathering and Analysis 6 Arena Model Implementation 18 Step by Step implementation 18 Time Logic 18 Cutoff Logic 18 Verification and Validation 19 Prediction Function / Conclusion 23 Appendices 27 Appendix 1 : side Results 27 Problem Definition and Goals The purpose of this simulation to obtain important parameters of a bank queue, including bank information, queue length, waiting time in queue and expected wait time of customers in a bank. To obtain the appropriate function for predicting the queue waiting time we need a large numbers of observations. To obtain this data without the need to collect them directly, we simulate the bank work flow and …show more content…

To guarantee our result we draw the distribution diagram of service time and number of people in the queue. The scattered diagram shows that we have different patterns of the distribution in different queue lines which supports our result. Next to determine if servers give service with the same distribution, we use Kruskal Wallis test again, we choose three different servers and do the test again. Kruskal-Wallis Test: service times versus server Kruskal-Wallis Test on service times server N Median Ave Rank Z 3 19 6.000 50.6 -0.07 4 24 7.500 54.3 0.64 6 7 5.000 42.3 -0.82 7 30 5.500 45.0 -1.34 8 21 8.000 59.0 1.41 Overall 101 51.0 H = 3.78 DF = 4 P = 0.437 H = 3.81 DF = 4 P = 0.432 (adjusted for ties) The p-value shows that there is no significant difference between the service times offered by the servers. For now we know that there is no significant between the service time in different days and service time offered by different servers so we choose two different days we check these data altogether. The p-value that we have for the mixture doesn’t

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