Sensitivity to Serial Dependency of Input Processes: A Robust Approach

Procedures in assessing the impact of serial dependency on performance analysis are usually built on parametrically specified models. In this paper, we propose a robust, nonparametric approach to carry out this assessment, by computing the worst-case deviation of the performance measure due to arbitrary dependence. The approach is based on optimizations, posited on the model space, that have constraints specifying the level of dependency measured by a nonparametric distance to some nominal independent and identically distributed input model. We study approximation methods for these optimizations via simulation and analysis of variance. Numerical experiments demonstrate how the proposed approach can discover the hidden impacts of dependency beyond those revealed by conventional parametric modeling and correlation studies.

The online appendix is available at https://doi.org/10.1287/mnsc.2016.2667.

This paper was accepted by Assaf Zeevi, stochastic models and simulation.