Optimization of discrete event system parameters using SFM-based infinitesimal perturbation analysis estimates

This paper deals with the problem of optimizing the performance of a discrete event system (DES) using infinitesimal perturbation analysis (IPA) estimates obtained from a stochastic fluid model (SFM). In order to better approximate the behavior of the DES, we propose a special case of SFM where the arrival and service processes are modeled by piecewise constant ON/OFF sources. The proposed SFM however violates some of the assumptions made in Cassandras, C. G., et al (2002) and as a result the sample derivatives no longer exist. However, using the proposed SFM, we obtain the left and right sided sample derivative estimates. This paper investigates the implementation of various IPA estimates that have been derived based on a stochastic fluid model (SFM) for the optimization of parameters of a discrete event system (DES). In this paper we investigate gradient based and subgradient optimization methods. As shown in this paper, for many scenarios all algorithms have comparable results however, in some cases subgradient optimization produces better results.