2025/06/25 by Sanne van Kempen, van Kempen, Sanne, Jaron Sanders +5
Computer Science · #60K25 #93E35 #Context-Aware Activity Recognition Systems #FOS: Computer and information sciences #FOS: Mathematics #IoT and Edge/Fog Computing #Machine Learning (cs.LG) #Network Traffic and Congestion Control #Optimization and Control (math.OC)
paper · pdf · doi:10.48550/arxiv.2506.20543
openalex publication_date 2025/06/25 · openalex created_date 2025/10/09 · openalex updated_date 2026/07/28
This paper is about optimally controlling skill-based queueing systems such as data centers, cloud computing networks, and service systems. By means of a case study using a real-world data set, we investigate the practical implementation of a recently developed reinforcement learning algorithm for optimal customer routing. Our experiments show that the algorithm efficiently learns and adapts to changing environments and outperforms static benchmark policies, indicating its potential for live implementation. We also augment the real-world applicability of this algorithm by introducing a new heuristic routing rule to reduce delays. Moreover, we show that the algorithm can optimize for multiple objectives: next to payoff maximization, secondary objectives such as server load fairness and customer waiting time reduction can be incorporated. Tuning parameters are used for balancing inherent performance trade--offs. Lastly, we investigate the sensitivity to estimation errors and parameter tuning, providing valuable insights for implementing adaptive routing algorithms in complex real-world queueing systems.