Robust Method for Sensitivity Analysis in Simulation Model/a Comparison Study

AbstractKleijnen proposed using Ordinary Least Squares method combining with experimental design to estimate polynomial regression metamodels, but I/O data violates some classical assumptions of OLS as the correlation between output which due to common random numbers and Heterogeneous variances which caused by using different factor combinations. Thus Kleijnen and David referred to using repeated OLS (OLSR) or Generalized Least Squares (GLS) as a robust methods instead of OLS. In this study we compare these two methods using a simulation model M/M/1 which represented by a Queuing model in the repair and maintenance fields. We validated the estimated first order polynomial regression meta-model using adjusted R2 and Relative average absolute error, Our results demonstrate that As a result, OLSR is more efficient and more validation than GLS method