Human resource allocation in an emergency department: A metamodel-based simulation optimization

Abstract

Purpose

The complexity and interdisciplinarity of healthcare industry problems make this industry one of the attention centers of computer-based simulation studies to provide a proper tool for interaction between decision-makers and experts. The purpose of this study is to present a metamodel-based simulation optimization in an emergency department (ED) to allocate human resources in the best way to minimize door to doctor time subject to the problem constraints which are capacity and budget.

Design/methodology/approach

To obtain the objective of this research, first the data are collected from a public hospital ED in Brazil, and then an agent-based simulation is designed and constructed. Afterwards, three machine-learning approaches, namely, adaptive neuro-fuzzy inference system (ANFIS), feed forward neural network (FNN) and recurrent neural network (RNN), are used to build an ensemble metamodel through adaptive boosting. Finally, the results from the metamodel are applied in a discrete imperialist competitive algorithm (ICA) for optimization.

Findings

Analyzing the results shows that the yellow zone section is considered as a potential bottleneck of the ED. After 100 executions of the algorithm, the results show a reduction of 24.82 per cent in the door to doctor time with a success rate of 59 per cent.

Originality/value

This study fulfils an identified need to optimize human resources in an ED with less computational time.

Citation

Yousefi, M. and Yousefi, M. (2020), "Human resource allocation in an emergency department: A metamodel-based simulation optimization", Kybernetes, Vol. 49 No. 3, pp. 779-796. https://doi.org/10.1108/K-12-2018-0675