Stochastic Analysis of Single Queue Single Server versus Single Queue Multiple Servers Models: A Case Study Of Post Bank and Kenya Commercial Bank

Abstract

Banks play signi cant roles in a country's economy. For this reason many studies have been done on the management and general organization of banks. One such area is on queue management. It is common practice to see long queues of customers waiting to be served within the banking halls. Customers arrive at banking facilities randomly. Moreover, service time is also a random phenomenon. Currently, many institutions are moving away from single queue single server model to single queue-multiple servers model, Presumably, because the waiting time in the latter model is lower but is this always the case? In our study we compared single queue single server to single queue multiple server: A case study of Post Bank Kisumu and Kenya Commercial Bank Kisumu. In both models we have assumed that the arrival times follow a Poisson process while service times follow an exponential distribution. Our main parameter of interest is the waiting time.We have used M=M=1 and M=M=r to study the two models and determine the preferable model for any speci c situation. In our study we found that although the average waiting time in Post Bank is greater than that in the Kenya Commercial Bank, the equivalence of the KCB average waiting time to the Post Bank is higher. Further, the di erence between the means in the waiting times in the two banks is signi cant at 5% signi cance level.