A stochastic optimization algorithm for revenue maximization in a service system with balking customers
This paper proposes a stochastic gradient descent algorithm that dynamically maximizes revenue in a single-server queue with balking customers by using a novel Infinitesimal Perturbation Analysis procedure to estimate effective arrival rates based solely on observable joining behavior, thereby converging to the optimal price under mild regularity conditions.