Sensitivity analysis for transient single server queuing models using an interpolation approach

Simulation is an essential tool for performance evaluation of many practical systems where planners typically want to know how the system will perform under various parameter settings. Since large-scale simulation may require great amount of computer time and storage, appropriate statistical analysis can become quite costly. In this paper, we develop an interpolation technique as an effective tool for estimating system response to parametric perturbations in simulation. We also analyze the usefulness of the continuous-time Markov chains framework to find the likelihood ratio (RadonNikodym derivative) for Markovian single server queueing models. We provide numerical experiments that demonstrate how the interpolation technique significantly outperform the likelihood ratio performance extrapolation technique in the context of the Markovian queueing models in transient analysis.