OPTIMIZATION OF DISCRETE-EVENT SYSTEMS VIA SIMULTANEOUS PERTURBATION STOCHASTIC-APPROXIMATION

We investigate the use of simultaneous perturbation stochastic approxi mation 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 sch emes require a large number of simulation replications when the number of parameters of interest is large, whereas the simultaneous perturba tion method is a finite-difference-like method that requires only two simulations per gradient estimate, regardless of the number of paramet ers of interest. This can result in substantial computational savings for large-dimensional systems. We report simulation experiments conduc ted on a variety of discrete-event systems: a single-server queue, a q ueueing network, and a bus transit network. For the single-server queu e, we also compare our work with algorithms based on finite difference s and perturbation analysis.