A simulation-based optimization framework for calibration of a digital twin in a sleep-disordered breathing unit

Sleep-disordered breathing, and obstructive sleep apnea in particular, places sustained demand on specialized units, leading to prolonged waiting lists that in some settings exceed one year for diagnostic tests such as respiratory polygraphy (PLG). In a previous study, a discrete-event simulation (DES) model was developed and validated as a digital twin of the patient flow in the Sleep-Disordered Breathing Unit of the Pulmonology Department at Hospital Álvaro Cunqueiro (Vigo, Spain), yielding a weighted Relative Percent Difference (RPD) of 3.6% for the mean number of patients in queue and 34.6% for the mean waiting time relative to real-world data. However, the transition probabilities governing patient routing across care stages were manually calibrated through a time-consuming, iterative process based on expert judgment, which limits the reproducibility and optimality of the solution. To address this limitation, the present study proposes and evaluates a simulation-based optimization (SBO) framework coupled to the DES model, implemented in MATLAB © , with the goal of automating the calibration of the state-transition matrix. In the main experiment, aimed at calibrating the mean number of patients in queue, the optimal solution reduced the global weighted RPD for this indicator from 3.6% under manual calibration to 1.11%. Complementary experiments that incorporated waiting time into the objective function or redistributed stage weights toward PLG confirmed the ability of the SBO framework to prioritize clinically relevant stages, at the potential cost of increased aggregate error in the queue indicator. To the best of our knowledge, this is the first application of SBO to the automated calibration of a state-transition matrix in a DES model of a sleep-disordered breathing unit, offering a quantitatively effective alternative to manual parameter tuning.