Deep learning methods for transient performance analysis and optimization of non-Markovian nonstationary queueing systems
(English) Empirical studies show that real-world service queueing systems, such as contact centers and healthcare facilities, often exhibit non-Markovian behavior, including non-exponential service and abandonment times, as well as nonstationary dynamics driven by time-varying arrivals and staffing....
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| Tipo de recurso: | tesis doctoral |
| Fecha de publicación: | 2026 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/457092 |
| Acceso en línea: | https://hdl.handle.net/2117/457092 https://dx.doi.org/10.5821/dissertation-2117-457092 |
| Access Level: | acceso abierto |
| Palabra clave: | non-Markovian queues nonstationary queues optimal staffing in queues transfer learning deep learning simulation queueing theory 004 - Informàtica Àrees temàtiques de la UPC::Informàtica |
| Sumario: | (English) Empirical studies show that real-world service queueing systems, such as contact centers and healthcare facilities, often exhibit non-Markovian behavior, including non-exponential service and abandonment times, as well as nonstationary dynamics driven by time-varying arrivals and staffing. These complexities make performance analysis and capacity planning particularly challenging. Traditional methods face notable limitations: Monte Carlo simulation, while flexible, can be computationally intensive for large systems or real-time decisions; and heavy-traffic analysis is a model-specific approximation that applies only to certain queueing structures, depends on the service-level metric of interest, and its accuracy may deteriorate for small-scale systems. To address these challenges, this thesis develops machine learning frameworks for analyzing and optimizing non-Markovian, nonstationary queueing systems. Trained on simulated data, the models learn the spatio-temporal structure of complex service systems and provide extremely fast inference, enabling real-time decision support. They are effective and robust across problem scales, and their design and training are universal, in contrast to heavy-traffic formulas that must be re-derived for each model and performance measure. By integrating accurate prediction with real-time optimization, this work advances queueing theory by introducing modern data-driven tools for dynamic capacity management in complex service operations. The thesis consists of three main contributions: (1) a neural network framework for performance analysis of non-Markovian nonstationary queueing systems with fixed capacity; (2) a deep learning framework based on a transfer learning approach that enables model adaptation to queueing systems with time-varying server capacity; (3) an optimization framework for staffing in non-Markovian nonstationary environments. All of these methods are accompanied by accuracy, robustness, and computational runtime experiments, demonstrating the effectiveness of the proposed approaches compared to traditional methods. |
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