Optimization of discrete event systems via simultaneous perturbation stochastic approximation

We investigate the use of simultaneous perturbation stochastic approximation for the optimization of discrete-event systems via simulation. Application of stochastic approximation to simulation optimization is basically a gradient-based method, so much recent research has focused on obtaining direct gradients. However, such procedures are still not as universally applicable as finite-difference methods. On the other hand, traditional finite-difference-based stochastic approximation schemes require a large number of simulation replications when the number of parameters of interest is large, whereas the simultaneous perturbation method is a finite-difference-like method that requires only two simulations per gradient estimate, regardless of the number of parameters of interest. This can result in substantial computational savings for large-dimensional systems. We report simulation experiments conducted on a variety of discrete-event systems: a single-server queue, a queueing network, and a bus transit network. For the single-server queue, we also compare our work with algorithms based on finite differences and perturbation analysis.