A simulation-based optimization approach for the calibration of a discrete event simulation model of an emergency department

TL;DR

This paper presents a simulation-based optimization method to calibrate a discrete event simulation model of an emergency department, improving accuracy by estimating missing data and enhancing model reliability.

Contribution

It introduces a calibration procedure that estimates incomplete ED data using optimization, ensuring more accurate simulation models for better decision-making.

Findings

Calibration successfully recovered missing parameters.

Enhanced model accuracy demonstrated on real Italian ED data.

Improved reliability of ED simulation outcomes.

Abstract

Accurate modeling of the patient flow within an Emergency Department (ED) is required by all studies dealing with the increasing and well-known problem of overcrowding. Since Discrete Event Simulation (DES) models are often adopted with the aim of assessing solutions for reducing the impact of this worldwide phenomenon, an accurate estimation of the service time of the ED processes is necessary to guarantee the reliability of the results. However, simulation models concerning EDs are frequently affected by data quality problems, thus requiring a proper estimation of the missing parameters. In this paper, a simulation-based optimization approach is used to estimate the incomplete data in the patient flow within an ED by adopting a model calibration procedure. The objective function of the resulting minimization problem represents the deviation between simulation output and real data, while the constraints ensure that the response of the simulation is sufficiently accurate according to the precision required. Data from a real case study related to a big ED in Italy is used to test the effectiveness of the proposed approach. The experimental results show that the model calibration allows recovering the missing parameters, thus leading to an accurate DES model.