This paper investigates the likelihood ratio method for estimating derivatives of finite-time performance measures in generalized semi-Markov processes (GSMPs). We develop readily verifiable conditions for the applicability of this method. Our conditions mainly place restrictions on the basic...

We present a general framework for applying simulation to optimize the behavior of discrete event systems. Our approach involves modeling the discrete event system under study as a general state space Markov chain whose distribution depends on the decision parameters. We then show how simulation...

We discuss the estimation of derivatives of a performance measure using the likelihood ratio method in simulations of highly reliable Markovian systems. We compare the difficulties of estimating the performance measure and of estimating its partial derivatives with respect to component failure...

Approaches like finite differences with common random numbers, infinitesimal perturbation analysis, and the likelihood ratio method have drawn a great deal of attention recently as ways of estimating the gradient of a performance measure with respect to continuous parameters in a dynamic...

This paper gives numerical illustrations of the behavior of stochastic approximation, combined with different derivative estimation techniques, to optimize a steady-state system. It is a companion paper to L'Ecuyer and Glynn (1993), which gives convergence proofs for most of the variants...