Parameter Tolerance in Capacity Planning Models

Abstract

In capacity planning for a service operation, analytical models based on queueing theory allow the user to quickly estimate the capacity required and to easily experiment with different system designs or configurations, for a given set of input parameters. An input parameter of the model could be inaccurate or may not be known beyond a good guess. In order to determine if the analysis results (and hence the system design) are robust to parameter estimation errors, sensitivity analysis can be performed. We study an alternative approach that involves specifying a tolerance range of a system performance measure and calculating a feasible region of the uncertain parameters for which the performance measure will be within the tolerance range. We illustrate this approach using basic exponential queueing models as well as a model of an order fulfillment operation in a distribution center.