  Mathematical modeling of production systems is the foundation of all
model-based approaches for production system analysis, design, improvement, and
control. To construct such a model for the stochastic process of the production
system more efficiently, a new modeling approach has been proposed that
reversely identifies the model parameters using system performance metrics
(e.g., production rate, work-in-process, etc.) derived from the parts flow
data. This paper extends this performance metrics-based modeling approach to
non-exponential serial production lines. Since no analytical expressions of
performance metrics are available for non-exponential systems, we use neural
network surrogate models to calculate those performance metrics as functions in
terms of the system parameters. Then, based on the surrogate models and given
performance metrics, the machine parameters are estimated by solving a
constrained optimization problem that minimizes the mean square error of the
performance metrics resulting from the estimated parameters compared to the
true ones. With the designed multi-start particle swarm optimization algorithm,
we find that multiple non-unique combinations of machine parameters can lead to
practically the same system performance metrics and a linear relationship of
the reliability parameters from these obtained estimations is observed.
Besides, model sensitivity analysis is implemented to verify the robustness of
the different combinations of machine parameters even under the potential
improvement scenarios.
