Queue analysis of public healthcare system to reduce waiting time using flexsim 6.0

Authors

  • Putri Amalia Department of Industrial Engineering, Universitas Islam Indonesia, Yogyakarta
  • Nur Cahyati Department of Industrial Engineering, Universitas Islam Indonesia, Yogyakarta

DOI:

https://doi.org/10.12928/ijio.v1i2.2428

Keywords:

Healthcare, Queue system, Simulation, FlexSim

Abstract

Public healthcare is a health service facility from the government at a low cost. The problem is the long queue, which makes long patients’ waiting times. The patients are waiting for a maximum of more than 3 hours in the general polyclinic. Besides, the registration counter is almost busy all the time. The utilization is about 96.96%. Therefore, the objective of this research is to reduce the patients’ waiting time using the simulation method. Flexsim 6.0 software is employed to develop the public healthcare system and also develop some alternatives to improve the problem. The simulation model has been verified and validated. The result shows the waiting time is decreased by more than 80% by adding the resource in the registration counter. For managerial insight, this research could help the public healthcare system in satisfying the patients.

References

Ahmed, R., Shah, M., & Umar, M. (2016). Concepts of simulation model size and complexity. International Journal of Simulation Modelling. 15(2), 213–222.

Alice, R., Giuditta, P., & Cavalieri, S. (2018). Quantitative assessment of service delivery process: application of hybrid simulation modelling. IFAC-PapersOnLine, 51(11), 1113–1118. https://doi.org/10.1016/j.ifacol.2018.08.454

Antonelli, D., Litwin, P., & Stadnicka, D. (2018). Multiple System Dynamics and Discrete Event Simulation for manufacturing system performance evaluation. Procedia CIRP, 78, 178–183. https://doi.org/10.1016/j.procir.2018.08.312

Burnetas, A., Economou, A., & Vasiliadis, G. (2017). Strategic customer behavior in a queueing system with delayed observations. Queueing Systems, 86(3–4), 389–418.

Dalinger, T., Thomas, K. B., Stansberry, S., & Xiu, Y. (2020). A mixed reality simulation offers strategic practice for pre-service teachers. Computers and Education, 144(September 2019), 103696. https://doi.org/10.1016/j.compedu.2019.103696

Ďutková, S., Achimský, K., & Hoštáková, D. (2019). Simulation of queuing system of post office. Transportation Research Procedia, 40, 1037–1044. https://doi.org/10.1016/j.trpro.2019.07.145

Ghaleb, M. A., Suryahatmaja, U. S., & Alharkan, I. M. (2015). Modeling and simulation of queuing systems using arena software: A case study. IEOM 2015 - 5th international conference on industrial engineering and operations management, proceeding (pp. 1–7). Institute of Electrical and Electronics Engineers.

Iqbal, Q., Whitman, L. E., & Malzahn, D. (2012). Reducing customer wait time at a fast food restaurant on campus. Journal of Foodservice Business Research, 15(4), 319–334.

Kambli, A., Sinha, A. A., & Srinivas, S. (2020). Improving campus dining operations using capacity and queue management: A simulation-based case study. Journal of Hospitality and Tourism Management, 43(January), 62–70. https://doi.org/10.1016/j.jhtm.2020.02.008

Kaya, O., Teymourifar, A., & Ozturk, G. (2020a). Analysis of different public policies through simulation to increase total social utility in a healthcare system. Socio-Economic Planning Sciences, 70(September 2019), 100742. https://doi.org/10.1016/j.seps.2019.100742

Kaya, O., Teymourifar, A., & Ozturk, G. (2020b). Public and private healthcare coordination: An analysis of contract mechanisms based on subsidy payments. Computers and Industrial Engineering, 146(June), 106526. https://doi.org/10.1016/j.cie.2020.106526

Kim, Y. J., & Yoo, J. H. (2020). The utilization of debriefing for simulation in healthcare: A literature review. Nurse Education in Practice, 43(September 2019), 102698. https://doi.org/10.1016/j.nepr.2020.102698

Kisliakovskii, I., Balakhontceva, M., Kovalchuk, S., Zvartau, N., & Konradi, A. (2017). Towards a simulation-based framework for decision support in healthcare quality assessment. Procedia Computer Science, 119(2017), 207–214. https://doi.org/10.1016/j.procs.2017.11.178

Lynch, H. M., Jamieson, K., Roussin, C., & Bae, D. (2018). Increasing healthcare value through simulation: Cost savings from reductions in cast saw injuries after simulation-based education of orthopaedic trainees. Pediatrics, 141 626–626.

Makarova, I., Shubenkova, K., Pashkevich, A., Buyvol, P., Mavrin, V., & Abeshev, K. (2020). Improvement of Automotive Service Management by Means of Computer Simulation. Transportation Research Procedia, 44(2019), 160–167. https://doi.org/10.1016/j.trpro.2020.02.023

McCormack, R., & Coates, G. (2015). A simulation model to enable the optimization of ambulance fleet allocation and base station location for increased patient survival. European Journal of Operational Research, 247(1), 294e309.

Meleddu, M., Pulina, M., & Scuderi, R. (2020). Public and private healthcare services: What drives the choice? Socio-Economic Planning Sciences, 70(September 2019), 100739. https://doi.org/10.1016/j.seps.2019.100739

Pawlewski, P. (2018). Using PFEP for Simulation Modeling of Production Systems. Procedia Manufacturing, 17, 811–818. https://doi.org/10.1016/j.promfg.2018.10.132

Persson, J. (2017). A review of the design and development processes of simulation for training in healthcare – A technology-centered versus a human-centered perspective. Applied Ergonomics, 58, 314–326. https://doi.org/10.1016/j.apergo.2016.07.007

Pongjetanapong, K., O’Sullivan, M., Walker, C., & Furian, N. (2018). Implementing complex task allocation in a cytology lab via HCCM using Flexsim HC. Simulation Modelling Practice and Theory, 86(May), 139–154. https://doi.org/10.1016/j.simpat.2018.05.007

Pullman, M., & Rodgers, S. (2010). Capacity management for hospitality and tourism: A review of current approaches. International Journal of Hospitality Management, 29(1), 177–187.

Velumani, S., Pitchiah, S., & Tang, H. (2017). A study of service wait time and its improvement in acafeteria using discrete event simulation. International Journal of Research in Engineering and Technology, 6(10), 30–39.

Wijewickrama, A. K. A. (2006). Simulation analysis for reducing queues in mixed-patients’ outpatient department. International Journal of Simulation Modelling, 5(2), 56–68.

Yang, W., Su, Q., Huang, S. H., Wang, Q., Zhu, Y., & Zhou, M. (2019). Simulation modeling and optimization for ambulance allocation considering spatiotemporal stochastic demand. Journal of Management Science and Engineering, 4(4), 252–265. https://doi.org/10.1016/j.jmse.2020.01.004

Yavari, E., & Roeder, T. (2012). Model Enrichment: Concept, Measurement, and Application. Journal of Simulation, 6, 125–140.

Zhu, X., Zhang, R., Chu, F., He, Z., & Li, J. (2014). A flexsim-based optimization for the operation process of cold-chain logistics distribution centre. Journal of Applied Research and Technology, 12(2), 270–278. https://doi.org/10.1016/S1665-6423(14)72343-0

Downloads

Published

2020-08-21

How to Cite

Amalia, P., & Cahyati, N. (2020). Queue analysis of public healthcare system to reduce waiting time using flexsim 6.0. International Journal of Industrial Optimization, 1(2), 101–110. https://doi.org/10.12928/ijio.v1i2.2428

Issue

Section

Articles