Infinitesimal perturbation analysis for general discrete event systems

Abstract

A rigorous extension of the recent perturbation analysis approach to more general discrete event systems is given. First, a general class of systems and performance measures is defined, and some basic reprsentational and linearity properties are derived. Next a sample gradient of performance with respect to a parameter of the system is defined. Then, for certain parameters of such systems, an infinitesimal perturbation analysis algorithm is derived. It is proved that this algorithm gives exact values for the sample gradients of performance with respect to the parameters, by observing only one sample path of the DEDS. (However, the sample gradient may or may not be a good estimate of the gradient of the performance measure; this point is elaborated in the body of the paper.) The computational complexity of this algorithm is bound to be linear in the number of events. These results offer the potential for very efficient calculation of the gradients—a fact that can be used for design/operation of computer systems, communication networks, manufacturing systems, and many other real-world systems, particularly since restrictive assumptions (e.g., exponential distributions) are not required of the system.