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Multi-objective simulation optimization for medical capacity allocation in emergency department

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Journal of Simulation

Abstract

This study aims to investigate medical resource allocation problems by multi-objective simulation optimization to address the long-term overcrowding situations experienced in hospital emergency departments (EDs). With resource constraints considered, decision makers at EDs must determine the number of doctors, nurses, lab technicians, and other medical equipment to allocate efficiently these medical resources and simultaneously minimize the average patient length of stay in the system and the medical resource wasted cost. Therefore, this study first proposes a multi-objective stochastic optimization model to identify the optimal number of all medical resources at the EDs. In addition, a multi-objective simulation optimization algorithm by integrating non-dominated sorting particle swarm optimization (NSPSO) with multi-objective computing budget allocation (MOCBA) and an ED simulation model is developed and constructed to address this problem, respectively. Specifically, NSPSO searches for potential solutions to medical resource allocation problems. MOCBA identifies effective sets of feasible Pareto medical resource allocation solutions and effective allocation of simulation replications. An ED simulation model based on the operation flows of EDs in Taiwan was constructed to estimate the expected performance value of each resource allocation solution generated by NSPSO. The effectiveness and performance of integrated NSPSO and MOCBA was verified by computational experiments.

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Acknowledgements

The authors gratefully acknowledge the National Science Council, Taiwan, R.O.C., for support under contract NSC101-2221-E-030-014.

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Authors and Affiliations

  1. Fu Jen Catholic University, New Taipei City, ROC, Taiwan

    T-L Chen & C-C Wang

Authors
  1. T-L Chen
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  2. C-C Wang
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Corresponding author

Correspondence to T-L Chen.

Appendices

Appendix A

A.1. Simulation data

Tables A1, A2 and A3 list all input data and simulation parameters for the emergency department simulation model.

Table A1 Minimum and maximum limits of all medical resources
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Table A2 Cost information of all medical resources
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Table A3 Simulation parameters
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Appendix B

B.1. Evaluation performance metrics of Pareto-optimal front

  • Coverage metric: the coverage metric used to compare Pareto front E 1 and Pareto front E 2. When C(E 1, E 2)=1, E 1 dominates E 2. Conversely, if none of the solutions in E 2 can be dominated by any points in E 2, then C(E 1, E 2)=0. Notice that the C-metric is asymmetrical, that is, C(E 1, E 2)≠1−C(E 2, E 1). The definition of the coverage metric shows as follows.

  • Maximum spread metric: a maximum spread metric used to measure the percentage of range that the solution set covers the optimal Pareto front. Higher values are better. The definition of the maximum spread metric is shown as follows.

  • Tan’s spacing metric: a spacing metric used to measure how evenly the solutions are distributed. A small metric value means that the solutions are uniformly distributed and the Pareto front is better. The definition of the Tan’s spacing metric is shown as follows.

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Chen, TL., Wang, CC. Multi-objective simulation optimization for medical capacity allocation in emergency department. J Simulation 10, 50–68 (2016). https://doi.org/10.1057/jos.2014.39

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  • DOI: https://doi.org/10.1057/jos.2014.39

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