Skip to main content

Advertisement

Log in

An application of multi-objective simulation optimization to medical resource allocation for the emergency department in Taiwan

  • Original Research
  • Published:
Annals of Operations Research Aims and scope Submit manuscript

Abstract

In recent years, the need for emergency resources has dramatically increased and it has caused an overcrowding problem for the emergency department (ED). Solving this problem by increasing medical resources is either impractical or infeasible. Thus, this manuscript develops a multi-objective mathematical model to allocate medical resources for the emergency department (ED). The optimal resource allocation is exploited by using some meta-heuristic algorithms, i.e., fast and elitism non-dominated sorting genetic algorithm (NSGA II), non-dominated sorting particle swarm algorithm (NSPSO), and non-dominated sorting differential evolution (NSDE). Thereafter, a dynamic simulation model, which embeds the solutions from the resource allocation model in the simulation process, is constructed. Each feasible solution from the three meta-heuristic algorithms is simulated to estimate the performance of the resources allocated in terms of the average service level and staff utilization. The results show that the performance of the NSGAII, where the average service level and staff utilization for the current resources are 0.844 and 0.751, respectively, is better than those of NSPSO and NSDE. Besides, the number of medical staff gives a significant effect on the service level and utilization while the number of beds only impacts staff utilization. The simulation model can find out that the best combination of the number of staff and the number of beds is from 1 to 10 staffs and 1–6 beds to maximize the utilization and service level.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  • Ahmed, M. A., & Alkhamis, T. M. (2009). Simulation optimization for an emergency department healthcare unit in Kuwait. European Journal of Operational Research, 198, 936–942.

    Article  Google Scholar 

  • Akcali, E., Coˆté, M. J., & Lin, C. (2006). A network flow approach to optimizing hospital bed capacity decisions. Health Care Management Science, 9, 391–404.

    Article  Google Scholar 

  • Al-Refaie, A., Fouad, R. H., Li, M.-H., & Shurrab, M. (2014). Applying simulation and DEA to improve performance of emergency department in a Jordanian hospital. Simulation Modelling Practice and Theory, 41, 59–72.

    Article  Google Scholar 

  • Baesler, F. F., & Sepúlveda, J. A. (2001). Multi-objective simulation optimization for a cancer treatment center. In Proceeding of the 2001 winter simulation conference (Cat. No. 01CH37304) (pp. 1405–1411). IEEE.

  • Bérard, C., Cloutier, L. M., & Cassivi, L. (2012). Evaluating clinical trial management systems: a simulation approach. Industrial Management and Data Systems.

  • Bharti, A. (2006). Stochastic bed balancing of an obstetrics hospital. Health Care Management Science, 9(1), 31–45.

  • Cabrera, E., Taboada, M., Iglesias, M. L., Epelde, F., & Luque, E. (2012). Simulation optimization for healthcare emergency departments. Procedia Computer Science, 9, 1464–1473.

    Article  Google Scholar 

  • Chelouah, R., & Siarry, P. (2000). A continuous genetic algorithm designed for the global optimization of multimodal functions. Journal of Heuristics, 6, 191–213. https://doi.org/10.1023/a:1009626110229

    Article  Google Scholar 

  • Chen, T., & Wang, C. (2016). Multi-objective simulation optimization for medical capacity allocation in emergency department. Journal of Simulation, 10, 50–68.

    Article  Google Scholar 

  • Cochran, J. K., & Roche, K. T. (2009). A multi-class queuing network analysis methodology for improving hospital emergency department performance. Computers and Operations Research, 36, 1497–1512.

    Article  Google Scholar 

  • Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(182), 197.

    Google Scholar 

  • Dehghanimohammadabadi, M., Keyser, T. K., & Cheraghi, S. H. (2017). A novel Iterative Optimization-based Simulation (IOS) framework: An effective tool to optimize system’s performance. Computers and Industrial Engineering, 111, 1–17. https://doi.org/10.1016/j.cie.2017.06.037

    Article  Google Scholar 

  • Feng, Y.-Y., Wu, I.-C., & Chen, T.-L. (2017). Stochastic resource allocation in emergency departments with a multi-objective simulation optimization algorithm. Health Care Management Science, 20, 55–75.

    Article  Google Scholar 

  • Ghanes, K., Diakogiannis, A., Jouini, O., Jemai, Z., & Wargon, M. (2014). Key performance indicators for emergency departments: A survey from an operations management perspective under revision. In IIE Transactions on Healthcare Systems Engineering.

  • Gonsalves, T., & Itoh, K. (2009). Service optimization with patient satisfaction in healthcare systems Journal of. SIMULATION, 3, 150–162.

    Article  Google Scholar 

  • Gul, M., Fuat Guneri, A., & Gunal, M. M. (2020). Emergency department network under disaster conditions: The case of possible major Istanbul earthquake. Journal of the Operational Research Society, 71, 733–747.

    Article  Google Scholar 

  • Gul, M., & Guneri, A. F. (2012). A computer simulation model to reduce patient length of stay and to improve resource utilization rate in an emergency department service system International. Journal of Industrial Engineering, 19, 221–231.

    Google Scholar 

  • Gul, M., & Guneri, A. F. (2015). A comprehensive review of emergency department simulation applications for normal and disaster conditions. Computers and Industrial Engineering, 83, 327–344. https://doi.org/10.1016/j.cie.2015.02.018

    Article  Google Scholar 

  • Hoot, N. R., & Aronsky, D. (2008). Systematic review of emergency department crowding: Causes, effects, and solutions. Annals of Emergency Medicine, 52, 126–136.

    Article  Google Scholar 

  • Ishibuchi, H., Tsukamoto, N., & Nojima, Y. (2008)/ Evolutionary many-objective optimization. In 2008 3rd International Workshop on Genetic and Evolving Systems, 2008, GEFS 2008 (pp. 47–52). IEEE.

  • Jiang, S., Ong, Y.-S., Zhang, J., & Feng, L. (2014). Consistencies and contradictions of performance metrics in multiobjective optimization IEEE Trans. Cybernetics, 44, 2391–2404.

    Google Scholar 

  • Lin, R.-C., Sir, M. Y., & Pasupathy, K. S. (2013). Multi-objective simulation optimization using data envelopment analysis and genetic algorithm: Specific application to determining optimal resource levels in surgical services. Omega, 41, 881–892.

    Article  Google Scholar 

  • Niska R, Bhuiya F, Xu J (2010) National hospital ambulatory medical care survey: 2007 emergency department summary Natl Health Stat Report 26:1–31

  • Sprivulis, P. C., Da Silva, J. A., Jacobs, I. G., Jelinek, G. A., & Frazer, A. R. (2006). The association between hospital overcrowding and mortality among patients admitted via Western Australian emergency departments. Medical Journal of Australia, 184, 208–212.

    Article  Google Scholar 

  • Van Nieuwenhuyse I (2014) improving patient flow in emergency departments with OR techniques: A literature overview

  • Yeh, J.-Y., & Lin, W.-S. (2007). Using simulation technique and genetic algorithm to improve the quality care of a hospital emergency department. Expert Systems with Applications, 32, 1073–1083. https://doi.org/10.1016/j.eswa.2006.02.017

    Article  Google Scholar 

  • Yousefi, M., Yousefi, M., & Fogliatto, F. S. (2020). Simulation-based optimization methods applied in hospital emergency departments: A systematic review. Simulation, 96, 791–806.

    Article  Google Scholar 

  • Zeinali, F., Mahootchi, M., & Sepehri, M. M. (2015). Resource planning in the emergency departments: A simulation-based metamodeling approach. Simulation Modelling Practice and Theory, 53, 123–138.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Thi Phuong Quyen Nguyen.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kuo, R.J., Song, P.F., Nguyen, T.P.Q. et al. An application of multi-objective simulation optimization to medical resource allocation for the emergency department in Taiwan. Ann Oper Res 326, 199–221 (2023). https://doi.org/10.1007/s10479-023-05374-7

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-023-05374-7

Keywords

Navigation