Reuven Y. Rubinstein

TL;DR: Variance reduction techniques (VRT) are needed, even though computer speed has been increasing dramatically, ever since the introduction of computers, to reduce excessively long runtimes of simulation experiments.

TL;DR: In this article, the score function method was combined with the standard crude Monte Carlo and experimental design approaches, in order to evaluate the expected performance of a discrete event system and its associated gradient simultaneously for different scenarios (combinations of parameter values), as well as to optimize the expected expected performance with respect to two parameter sets, which represent parameters of the underlying probability law (for the systems evolution) and parameters of sample performance measure, respectively.

TL;DR: Variance reduction techniques (VRT) are needed, even though computer speed has been increasing dramatically, ever since the introduction of computers as discussed by the authors, but the net result has not been faster execution of simulation experiments; e.g., some modern simulation models need hours or days for a single ’run’ (one replication of one scenario or combination of simulation input values).

TL;DR: This paper surveys some recent results on the score function (SF) method, suitable for performance evaluation, sensitivity analysis, and optimization of complex discrete-event systems such as non-Markovian queueing systems.

TL;DR: An enhanced version of the splitting method, called the smoothed splitting method (SSM), for counting associated with complex sets, such as the set defined by the constraints of an integer program and in particular for counting the number of satisfiability assignments.