  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.
