Queue is an act of
joining a line to be served and it is part of our everyday human involvement.
The objectives of the study focused on using a mathematical model to determine
the waiting time of two selected banks as well as compare the average waiting
time between the banks. The study uncovered the extent of usage of queuing
models in achieving customer satisfaction as well as permitting to make better
decisions relating to potential waiting times for customers. The study adopted
a case study and observational research with the source of data being primary.
Purposive sampling technique was used to select the two banks under study with
the target population comprising of all the customers who intended to transact
businesses with the banks within the period of 11 am to 12 pm. The sample sizes
for the first, second and third day of the first bank are twenty-eight (28),
seventeen (17) and twenty (20) respectively with three servers on each day
whereas that for the first, second and third day of the second bank is twenty
(20), nine (9) and seventeen (17) with two servers on each day. A multiple
server (M/M/s) Model was adopted, and Tora Software was the statistical tool
used for the analysis. Findings of the study revealed that the second bank had
a higher utilization factor than the first bank. Also, the number of customers
in the banking hall of the second bank was higher than that of the first bank
during the entire period of observation. Finally, it takes customers of the
first bank lesser minutes to complete their transaction than the second bank.
In conclusion, the three days observations revealed different banking
situations faced by customers in both banks which had effect on waiting time of
customer service. The waiting time of customer service has effect on the number
of customers in the queue and system, the probability associated with the
emptiness of the system and the utilization factor. Based on the results, the
study recommended, interalia, that the management of the second bank
should adopt a three-server (M/M/3) model.
References
[1]
Banks, J., Carson, J.S., Nelson, B.L. and Nicol, D.M. (2001) Discrete-Event System Simulation. 3rd Edition, Prentice Hall, Hoboken, 24-37.
[2]
Kozlowski, D. and Worthington, D. (2015) Use of Queue Modelling in the Analysis of Elective Patient Treatment Governed by a Maximum Waiting Time Policy. European Journal of Operations Research, 24, 331-338.
https://doi.org/10.1016/j.ejor.2015.01.024
[3]
Olaniyi, T.A. (2004) An Appraisal of Techniques for Minimizing Cost of Customers Waiting in First Bank of Nigeria, Plc. Ilorin. University of Ilorin, Ilorin.
[4]
Sherman, P.J. (2015) Queuing Theory and Practice: A Source of Competitive Advantage.
[5]
Nafees, A. (2007) Analysis of the Sales Checkout Operation in Ica Supermarket Using Queuing Simulation. Unpublished Master’s Thesis, University of Dalarna, Falun.
[6]
Hillier, F.S. and Lieberman, G. J. (2007) Introduction to Operations Research. 8th Edition, Tata McGraw Hill, New York.
[7]
Maister, D.H. (2009) The Psychology of Waiting Lines.
https://www.davidmaister.com
[8]
Ogunsakin, R.E., Babalola, B.T. and Adedara, M.T. (2013) Comparison of Service Delivery by ATM in Two Banks: Application of Queuing Theory. IOSR Journal of Mathematics, 9, 52-56.
[9]
Zhang, F. and Liu, B. (2013) Equilibrium Balking Strategies in Markovian Queues with Working Vacations. Applied Mathematical Modelling, 37, 8264-8282.
[10]
Seigha, G., Gordon, M.B. and Mobolaji, H.O. (2017) Application of Queuing Theory to a Fast-Food Outfit. Independent Journal of Management & Production, 8, 441-458. https://doi.org/10.14807/ijmp.v8i2.576
[11]
Little, J.D.C. (1961) A Proof for the Queuing Formula: L = λW. Operations Research, 9, 383-387. https://doi.org/10.1287/opre.9.3.383
[12]
Little, J.D.C. and Graves, S.C. (2008) Little’s Law. In: Chhajed, D. and Lowe, T.J., Eds., Building Intuition. Vol. 115, Springer, Boston, 81-100.
https://doi.org/10.1007/978-0-387-73699-0_5
[13]
Martino, A., Guatteri, G. and Paganoni, A.M. (2020) Hidden Markov Models for Multivariate Functional Data. Statistics & Probability Letters, 167, Article ID: 108917.
https://doi.org/10.1016/j.spl.2020.108917
[14]
Lyu, X., Xiao, F. and Fan, X. (2021) Application of Queuing Model in Library Service. Procedia Computer Science, 188, 69-77.
https://doi.org/10.1016/j.procs.2021.05.054
[15]
Sharma, A.K., Kumar, R. and Sharma, G.K. (2013) Queuing Theory Approach with Queuing Model. International Journal of Engineering Science Invention, 2, 1-11.
[16]
Hu, Q. (2012) Stochastic Operations Research. Tsinghua University Press, Beijing.