MODELING A MASS SERVICE SYSTEM FOR COVID-19 VACCINES

Authors

  • Petya Stoyanova “Todor Kableshkov” University of Transport, Bulgaria

Keywords:

mass service system simulation, GPSS models, COVID-19

Abstract

This report presents a simulation model of a multi-channel mass service (MSS) system using the GPSS
World computer simulation system. The model is presented for an 8-hour working day of service staff in a hospital
during the administration of vaccines against COVID-19. Three seats are placed for patients waiting for service,
taking into account the fact that if all the seats are occupied when the next patient arrives, he will be refused entry to
the hospital (system). The flow of patients (service requests) is Poisson. Patient service times are exponentially
distributed. Input data are provided for the values of the mean time interval between the arrival of patients at the
entrance of the hospital or system and the mean time of their service. The implementation of the model has been
carried out when the system operates with two serving devices (medical staff), with three seats available. In cases
where the operation of the system with two medical personnel is modeled, it is assumed that the average service
times are equal in value. Model time is in minutes, with 1 unit of model time equal to 1 minute. As a result, it is
determined: the number of patients served; the number of patients denied service; the workload of medical personnel
(the service unit); the average time to service patients for the administration of vaccines against COVID-19.

References

Карагьозов, К., „Приложение на теория на масовото обслужване на моделиране на работата на комплексни технологични системи“, Годишник на ВТУ „Тодор Каблешков“, ISSN 1314-362X, бр. 5, 2014 г., монография, https://stg.vtu.bg/almanac-5-2014/;

Карагьозов, К. Ст., Димитров, С. Д., "Комплексен имитационен модел на отворени мрежи от системи за масово обслужване с произволна структура", Годишник на ВТУ „Тодор Каблешков“, ISSN 1314-362X, бр. 5, 2014 г., студия, https://www.vtu.bg/wp-content/uploads/2021/12/1_Studia_kompleksen-imitacionen-model_2014.pdf;

Трендафилов З., Симулационен модел за анализ на адаптивното управление на светофарни уредби., сп. Механика транспорт комуникации, София том.16, бр.3/1,2018, ISSN 1312-3823 , 2018г.;

Томашевский В., Жданова E., Имитационное моделирование в среде GPSS. – М.:Бестселлер, 2003. – 416 c. ISBN 5-98158-004-6;

Borisov A, Velyova V., (2020), Analysis of logistics chains, serviced by railway transport and approaches for technological design of processes, XIX Scientific-expert conference on railways RAILCON '20, Ms, Serbia, 2020, Proceedings XIX Scientific-expert conference on railways RAILCON '20, ISBN 978-86-6055-134-6, pp. 157-160;

Dimitrov, S., and Ceder, A. (2018). Modelling and simulation of high-frequency autonomous public-transport service. International Scientific Journal "Mathematical Modeling", ISSN 2535-0986 (Print), ISSN 2603-2929 (Online), Vol. 2 (2018), Issue 2, 73-80, http://stumejournals.com/journals/mm/2018/2/73;

Dimitrov S., Ceder, A., Mathieson, G., and Victor, R. (2018). An application of a network science tool for examining and analysing the structure and topological properties of public-transport networks: a case study. International Scientific Journal "Trans Motauto World", ISSN 2367-8399 (Print), ISSN 2534-8493 (Online), Vol. 3 (2018), Issue 2, 78-83, http://stumejournals.com/journals/tm/2018/2/78;

Marshall, D.A.; Burgos-Liz, L.; Ijzerman, M.J.; Osgood, N.D.; Padula, W. V.; Higashi, M.K.; Wong, P.K.; Pasupathy, K.S.; Crown,W. (2015), Applying dynamic simulation modeling methods in health care delivery research—The SIMULATE checklist: Report of theISPOR simulation modeling emerging good practices task force. Value Heal. 2015, 18, 5–16, doi:10.1016/j.jval.2014.12.001., (PDF) Discrete-Event Simulation Modeling in Healthcare: A Comprehensive Review. Available from: https://www.researchgate.net/publication/356499771_DiscreteEvent_Simulation_Modeling_in_Healthcare_A_Comprehensive_Review [accessed Dec 02 2022].

Schriber T., (1991), An introduction to simulation and GPSS. New York: John Wiley and Sons, Inc.

Schriber, T.J.; Brunner, D.T. (2002), Inside Discrete-Event Simulation Software How it works and why it matters. In Proceedings of the 2002 Winter Simulation Conference, San Diego, CA, USA, 8–11 December 2002; Yücesan, E., Chen C.H., Snowdon, J.L., CharnesJ.M., Eds.; Winter Simulation Conference: San Diego, CA, USA, pp. 32–58, (PDF) Optimizing Emergency Medical Service Structures Using a Rule-Based Discrete Event Simulation—A Practitioner’s Point of View. Available from: https://www.researchgate.net/publication/349893046_Optimizing_Emergency_Medical_Service_Structures_Using_a_Rule-Based_Discrete_Event_Simulation-A_Practitioner's_Point_of_View [accessed Dec 02 2022].

http://www.minutemansoftware.com/simulation.htm;

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Published

2022-12-16

How to Cite

Stoyanova, P. (2022). MODELING A MASS SERVICE SYSTEM FOR COVID-19 VACCINES. KNOWLEDGE - International Journal , 55(4), 777–782. Retrieved from http://ikm.mk/ojs/index.php/kij/article/view/5829