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Kriging-based simulation optimization: An emergency medical system application

Author

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  • Guilherme F. Coelho
  • Luiz R. Pinto

Abstract

Metamodeling is a common subject in simulation optimization literature. It aims to estimate the actual value (simulated) even before the point is evaluated by a simulation model. However, most publications do not apply metamodeling to models with real world complexity and size. This paper sought to apply Kriging to minimize the average response time of a Medical Emergency System by allocating ambulances throughout several city bases. Kriging is considered the state-of-art technique in metamodeling as it provides, in addition to the new point estimation, the level of prediction uncertainty. The optimization process followed the Efficient Global Optimization algorithm (EGO) and the Reinterpolation Procedure to deal with a stochastic simulation model. Finally, EGO was used to obtain a curve that reflected the relationship between the minimum response time and the total number of ambulances allocated to the city, representing significant information for healthcare public systems managers.

Suggested Citation

  • Guilherme F. Coelho & Luiz R. Pinto, 2018. "Kriging-based simulation optimization: An emergency medical system application," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 69(12), pages 2006-2020, December.
    • Handle: RePEc:taf:tjorxx:v:69:y:2018:i:12:p:2006-2020
    DOI: 10.1080/01605682.2017.1418149
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    Cited by:

    1. Zhen, Lu & Wu, Jingwen & Chen, Fengli & Wang, Shuaian, 2024. "Traffic emergency vehicle deployment and dispatch under uncertainty," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 183(C).
    2. Marco Boresta & Tommaso Giovannelli & Massimo Roma, 2024. "Managing low–acuity patients in an Emergency Department through simulation–based multiobjective optimization using a neural network metamodel," Health Care Management Science, Springer, vol. 27(3), pages 415-435, September.

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