A Reinforcement Learning Solution for Queue Management in Public Utility Services

This paper presents a reinforcement learning-based approach for optimizing queue management in public utility service environments. Using one year of real operational data from a utility office, a simulation model is developed to replicate daily service dynamics. A Q-learning agent is trained to allocate service types to counters dynamically, aiming to minimize client waiting time. The model treats the environment as a Markov Decision Process and uses an epsilon-greedy policy for learning optimal actions. Experimental results across multiple counter configurations demonstrate significant reductions in average waiting times, confirming the effectiveness and adaptability of the proposed method in dynamic service environments.