Understanding Manufacturing Systems with a Learning Historian for User-Directed Experimentation

This paper describes a learning historian to improve user-directed experimentation withdiscrete event simulation models of manufacturing systems. In user-directed
experimentation, an analyst conducts simulation runs to estimate system performance.
Then the analyst modifies the simulation model to evaluate other possibilities. An
important characteristic is the ad hoc nature of the experimentation, as the analyst forms
and runs new trials based on the results from previous trials. Through user-directed
experimentation designers compare alternatives and students learn the relationships
between input parameters and performance measures. Recording and reviewing previous
trials while using simulation models enhances their benefits, transforming trial-and-error
into learning. The learning historian combines a graphical user interface, a discrete event
simulation model, and dynamic data visualization. Usability studies indicate that the
learning historian is a usable and useful tool because it allows users to concentrate more
on understanding system behavior than on operating simulation software.