Ponente
Descripción
Arrival times in queueing systems are known to exhibit seasonal and diurnal patterns. However, even after accounting for these patterns, there remains an autocorrelation structure in the times between successive arrivals. Ignoring this autocorrelation can lead to underestimation of performance measures and suboptimal decisions. In our study, we propose a method for capturing the remaining autocorrelation in arrival times using the autoregressive conditional duration model with generalized gamma distribution and score dynamics. We demonstrate the effectiveness of this method through a simulation study on single and multiple server queueing systems, and show that it can be applied to various types of retail datasets with different characteristics. Our results suggest that accounting for autocorrelation in arrival times is crucial for accurate performance evaluation of queueing systems.
