  Initial development and subsequent calibration of discrete event simulation
models for complex systems require accurate identification of dynamically
changing process characteristics. Existing data driven change point methods
(DD-CPD) assume changes are extraneous to the system, thus cannot utilize
available process knowledge. This work proposes a unified framework for
process-driven multi-variate change point detection (PD-CPD) by combining
change point detection models with machine learning and process-driven
simulation modeling. The PD-CPD, after initializing with DD-CPD's change
point(s), uses simulation models to generate system level outputs as
time-series data streams which are then used to train neural network models to
predict system characteristics and change points. The accuracy of the
predictive models measures the likelihood that the actual process data conforms
to the simulated change points in system characteristics. PD-CPD iteratively
optimizes change points by repeating simulation and predictive model building
steps until the set of change point(s) with the maximum likelihood is
identified. Using an emergency department case study, we show that PD-CPD
significantly improves change point detection accuracy over DD-CPD estimates
and is able to detect actual change points.
