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Application of Queuing Theory in The Optimization of Waiting Time and Cost in A Teaching Hospital
Subject area: Science,Engineering and Technology · Area of research: Statistics
Abstract
The health care system constitutes an essential part of the service sector. Over the years, hospitals have become increasing successful in deploying medical and technical innovations in order to deliver more effective clinical treatment. However, they are still confronted with pressure, delay, congestion and inefficiency presenting ground for research in numerous scientific fields. Providing too much service capacity to operate a system involves excessive cost, but not providing enough service capacity results in long waiting time and cost. Primary source of data collection was adopted using counting process and descriptive observation at the eye clinic of Jos University Teaching Hospital (JUTH). The statistical model was multi-server queuing model and First come, First served queuing discipline. The data was analysed using TORA optimization software as well as using descriptive analysis. The results revealed that the average number of patients in the queue, average number of patients in the system, expected time a patient spent in the queue, expected time a patient spent in the system and the utilization factor could be reduced at an optimal server level and at a minimum expected total cost as against their present server level with maximum expected total cost which include waiting and service costs. Therefore, this call for serious attention in order to improve the overall patient care and satisfaction.
Keywords
Queue, Hospital, Satisfaction, Waiting Time, Cost.
References
[1] Adele, M. & Barry, S. (2005). Modelling patient’s flow in hospital using queuing theory. unpublished manuscript.
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[3] Bunday, B. D. (1996). An Introduction to queuing theory. New York: Halsted press.
[4] Davis, M. & Vollman, T. E. (1990). A frame work for relating time waiting and customer’s satisfaction in a service operation. Journal services marketing 4(1): 61-69.
[5] Fink, R., & Gillett, J. (2006). Queuing Theory and the Taguchi Loss Function: The cost of customer’s dissatisfaction in waiting lines. International Journal of strategic cost management. Spring. 3(5): 1-9.
[6] Gross, D., Shortle, J. F., Thompson, J. M., & Harris, C. M. (2018). Fundamentals of queuing theory (5 th ed.) Wiley.
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[8] Kembe, M. M., Onah, E. S & Lorkegh, S. A. (2012). A study of waiting and service cost of multi-server queuing model in specialist hospital. International Journal of Scientific and Technology Research 5(2): 2277-8616.
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[10] Medhi, J. (2003). Stochastic models in queuing theory, Amsterdam: Academic press, second edition.
[11] Nosek, A. R., & Wilson, P. J. (2001). Queuing theory and customer’s satisfaction: A review of terminology, trends and application to pharmacy practice. Hospital pharmacy. 36(3): 275-279.
[12] Somani, S. M., Daniels, C. E., Jermstad, R. L. (1982). Patient’s satisfaction with out-patients pharmacy services. AM J Hosp. Pharm. 3(9): 1025-7.
[13] Stewart, W. J., (1946). Probability, Markov Chains, Queuing and Simulation, (1 st Edition): The mathematical of performance modelling, Princeton University press.
How to cite this paper
@article{1715232,
author = {Akanihu, C. N., Ali, H., Sakya M. R.},
title = {Application of Queuing Theory in The Optimization of Waiting Time and Cost in A Teaching Hospital},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {9},
pages = {1376-1385},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1715232.pdf},
abstract = {The health care system constitutes an essential part of the service sector. Over the years, hospitals have become increasing successful in deploying medical and technical innovations in order to deliver more effective clinical treatment. However, they are still confronted with pressure, delay, congestion and inefficiency presenting ground for research in numerous scientific fields. Providing too much service capacity to operate a system involves excessive cost, but not providing enough service capacity results in long waiting time and cost. Primary source of data collection was adopted using counting process and descriptive observation at the eye clinic of Jos University Teaching Hospital (JUTH). The statistical model was multi-server queuing model and First come, First served queuing discipline. The data was analysed using TORA optimization software as well as using descriptive analysis. The results revealed that the average number of patients in the queue, average number of patients in the system, expected time a patient spent in the queue, expected time a patient spent in the system and the utilization factor could be reduced at an optimal server level and at a minimum expected total cost as against their present server level with maximum expected total cost which include waiting and service costs. Therefore, this call for serious attention in order to improve the overall patient care and satisfaction.},
keywords = {Queue, Hospital, Satisfaction, Waiting Time, Cost.},
month = {March},
doi = {https://doi.org/10.64388/IREV9I9-1715232}
}