Fundamentals of queueing theory (2nd ed.).
Fundamentals of queueing theory (2nd ed.).
The score function approach for sensitivity analysis of computer simulation models
Mathematics and Computers in Simulation
A unified view of the IPA, SF, and LR gradient estimation techniques
Management Science
Likelilood ratio gradient estimation: an overview
WSC '87 Proceedings of the 19th conference on Winter simulation
Theory, Volume 1, Queueing Systems
Theory, Volume 1, Queueing Systems
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In designing, analyzing and operating real-life complex systems, we are interested, however, not only in performance evaluation but in sensitivity analysis and optimization as well. Since most systems of practical interest are too complex to allow the analytical solution of totally realistic models, these systems must be studied by means of Monte-Carlo simulation. One problem with Monte Carlo analysis is its expensive use of computer time. To address this problem, we propose an efficient technique for estimating the expected performance of a stochastic system for various values of the parameters from a single simulation of the nominal system. This technique is based on the likelihood ratio performance extrapolation (LRPE). We provide numerical experiments that demonstrate how the proposed technique significantly outperform the likelihood ratio performance extrapolation technique in the context of the Markovian queueing models in transient analysis.