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Initial development and subsequent calibration of discrete event simulationmodels for complex systems require accurate identification of dynamicallychanging process characteristics. Existing data driven change point methods(DD-CPD) assume changes are extraneous to the system, thus cannot utilizeavailable process knowledge. This work proposes a unified framework forprocess-driven multi-variate change point detection (PD-CPD) by combiningchange point detection models with machine learning and process-drivensimulation modeling. The PD-CPD, after initializing with DD-CPD's changepoint(s), uses simulation models to generate system level outputs astime-series data streams which are then used to train neural network models topredict system characteristics and change points. The accuracy of thepredictive models measures the likelihood that the actual process data conformsto the simulated change points in system characteristics. PD-CPD iterativelyoptimizes change points by repeating simulation and predictive model buildingsteps until the set of change point(s) with the maximum likelihood isidentified. Using an emergency department case study, we show that PD-CPDsignificantly improves change point detection accuracy over DD-CPD estimatesand is able to detect actual change points.

Process Knowledge Driven Change Point Detection for Automated Calibration of Discrete Event Simulation Models Using Machine Learning