Using Accuracy Measurements to Evaluate Simulation Model Simplification

Abstract

Infineon Technologies Dresden has long used discrete event simulation to optimize production planning for its fully automated front end manufacturing lines. There are needs to reduce maintenance efforts, to increase transparency for validation and verification, and to improve flexibility for the fast simulation of scenarios with a focus on qualitative statements. Less detailed models will be utilized where components could be omitted. This paper considers a simplification of the process flows through operation substitution for constant delays. The main idea is to consider the accuracy measurements used to evaluate simplification. It is shown that standard accuracy measurements (e.g. mean absolute error, correlation coefficient, etc.) produce rather poor performance. It is suggested instead to use measurements based on lot cycle time distributions (e.g. goodness-of-fit tests). Nine types of simplification sieve functions were analyzed, with analyses based on the MIMAC dataset 5 model.