Computer Simulation System for Queueing Coronavirus 2019 (COVID-19) Patients in a Governmental Emergency Unit Using Queueing Theory

Elsayed, Howaida; Alghobiri, Mohamed; Elsayed, Waleed

doi:10.21608/jsfc.2021.248607

	Computer Simulation System for Queueing Coronavirus 2019 (COVID-19) Patients in a Governmental Emergency Unit Using Queueing Theory
المجلة العلمية لقطاع کليات التجارة بجامعة الأزهر
Article 5, Volume 26, Issue 1, June 2021, Page 73-97 PDF (2.41 MB)
Document Type: المقالة الأصلية
DOI: 10.21608/jsfc.2021.248607
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Authors
Howaida Elsayed^* ¹; Mohamed Alghobiri^* ²; Waleed Elsayed³
¹Statistics Department, Business College, King Khalid University, ABHA, Saudi Arabia
²Computer Science Department, Business College, King Khalid University, ABHA, Saudi Arabia
³Administration Department, Commerce College, Benha University, Benha, Egypt
Abstract
This paper addresses the problem of potential overcrowding by patients in the emergency unit of the Asir governmental hospital located in Abha city, Saudi Arabia. This issue affects the patient waiting time, the level of service provided, the quality of service, and the level of pressure on the medical staff; thus, waiting lines are formed, for various reasons. The main objective of this paper is to reduce the waiting time of patients and to increase the efficiency of the emergency unit. This paper presents an attempt to apply queuing theory to an emergency unit; the calculations made in this paper are based upon actual observed data collected from the emergency unit in the Asir governmental hospital to identify the appropriate distributions for access and service times. The research serves as the basis for a computer simulation model to accurately represent the current reality of the system, to assist decision-makers in overcoming the wait time problem to reduce patient waiting time to zero minutes. Actual data and simulation results revealed the waiting time and number of patients in the queue decreased upon the application of queueing theory.
Keywords
Queueing Theory; Arrival Rate; Service Time Rate; Exponential Distribution; Poisson Distribution; Quality of Health Service; Simulation


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Computer Simulation System for Queueing Coronavirus 2019 (COVID-19) Patients in a Governmental Emergency Unit Using Queueing Theory

(2-1) 1. A.K. Erlang, “The Theory of Probabilities and Telephone Conversations,” Nyt Tidsskrift for Matematik B, vol.20, pp. 33-39, 1909.

(2-1) 2. Allen A., “Probability, Statistics, and Queuing Theory with Computer Science Applications,” 2nd ed., Academic Press, INC, 1990.

(2-1) 3. BhatU. Narayan, “An Introduction to Queueing Theory: Modeling and Analysis in Applications,” Birkhäuser, 2015.

(2-1) 4. Cooper B. R., “Introduction to Queueing Theory,” 2nd ed., North Holland, New York, 1981.

(2-1) 5. Daldoul D., et al., “A stochastic model to minimize patient waiting time in an emergency department,” Operations Research for Health Care, vol.18, pp.16-25, 2018.

(2-1) 6. Feldman M. R., Flores V. C., “Applied Probability and Stochastic Processes,”2nd ed., Springer, Verlag BerlinHeidelberg, 2010.

(2-1) 7. Green V. L., et al., “Using queueing theory to increase the effectiveness of emergency department provider staffing, ” Academic Emergency Medicine, vol.13, no.7, pp.61-68, 2006.

(2-1) 9. Hu X., et al., “Applying queueing theory to the study of emergency department operations: a survey and a discussion of comparable simulation studies,” International Transactions in Operational Research, vol.25, pp. 7-49, 2018.

(2-1) 10. Joseph W. J., “Queuing Theory and Modeling Emergency Department Resource Utilization,” Emergency Medicine Clinics of North America, vol. 38, no. 3, pp.563-572,2020.

(2-1) 11. Kalashnikov V. V., “Mathematical Methods in Queuing Theory,” Springer, Netherlands, 1994.

(2-1) 12. Kendall, D.G., “Stochastic Processes Occurring in the Theory of Queues and their Analysis by the Method of the Imbedded Markov Chain,” The Annals of Mathematical Statistics, vol.24, no.3, pp. 338-354,1953.

(2-1) 13.Miyazawa M., et al., “Advances in Queueing Theory and Network Applications,” Springer, Verlag New York, 2009.

(2-1) 14. Patil P., Thakur S., “Patient waiting time prediction in hospital queuing system using improved random forest in big data,” International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT), IEEE, 2019.

(2-1) 15. Shortle, J.F., et al., “Fundamentals of queueing theory,” John Wiley & Sons Hoboken, NJ, 2018.

(2-1) 16. Taha H. A., “Operations Research: An Introduction,” 8th ed., Pearson Prentice Hall, 2007.

(2-1) 17. Thomopoulos T.N., “Statistical Distributions Applications and Parameter Estimates,” Springer, 2017.

(2-1) 2. Allen A., “Probability, Statistics, and Queuing Theory with Computer Science Applications,” 2^nded., Academic Press, INC, 1990.

(2-1) 4. Cooper B. R., “Introduction to Queueing Theory,” 2^nd ed., North Holland, New York, 1981.

(2-1) 6. Feldman M. R., Flores V. C., “Applied Probability and Stochastic Processes,”2^nd ed., Springer, Verlag BerlinHeidelberg, 2010.

(2-1) 16. Taha H. A., “Operations Research: An Introduction,” 8^th ed., Pearson Prentice Hall, 2007.