Service Level Prediction in Non-Markovian Nonstationary Queues: A Simulation-Based Deep Learning Approach

Empirical studies have shown that real-life queueing systems, such as contact centers, exhibit non-Markovian and nonstationary behaviors. Consequently, analyzing their performance poses significant challenges. In this paper, we propose a simulation-based autoregressive deep learning algorithm (SADLA) for predicting service levels in non-Markovian, nonstationary queueing systems. Our method leverages modern recurrent neural networks, which are trained on synthetic data to capture the intrinsic spatio-temporal characteristics of queueing systems. Our findings demonstrate that SADLA achieves high prediction accuracy while reducing computational complexity by six orders of magnitude compared to traditional simulation methods. The implications of our research extend beyond accurate queue performance analysis; by embracing the learning capabilities of neural networks, our approach contributes to the advancement of the overall performance and resilience of real-life service systems.