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 signicant 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 specic 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 dierence between the means in the waiting

times in the two banks is signicant at 5% signicance level.

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