August 22, 2025MDPIOpen Access

A Reinforcement Learning Solution for Queue Management in Public Utility Services

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Average waiting times significantly decreased using a reinforcement learning approach, indicating improved service efficiency.
A Q-learning agent optimized service type allocation dynamically, demonstrating effective queue management.
The simulation model replicated daily service dynamics using real operational data over one year, showcasing its practical application.
The method treats the queue management challenge as a Markov Decision Process, emphasizing its robust decision-making framework.

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Reinforcement learning reduces waiting time in public utility services, highlighting its adaptability and efficiency.

Dobrev et al. (2025) studied this question.

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