Application of descent algorithms with Armijo stepsizes to simulation-based optimization of queueing networks

Gradient descent algorithms with Armijo stepsizes are adapted to simulation-based optimization of queuing networks. The cost function was evaluated by Monte Carlo simulation, and its gradient was estimated by infinitesimal perturbation analysis. The Armijo stepsize routine requires multiple function evaluations, which are simultaneously performed by finite perturbation analysis (FPA) in one simulation run. Two kinds of FPA estimators are considered: one is precise, but time consuming, and the other is approximate, but faster. Numerical examples show the validity of the proposed algorithm