We review techniques for optimizing stochastic discrete-event systems via simulation. We discuss both the discrete parameter case and the continuous parameter case, but concentrate on the latter which has dominated most of the recent research in the area. For the discrete parameter case, we focus on the techniques for optimization from a flnite set: multiple-comparison procedures and ranking-and-selection procedures. For the continuous parameter case, we focus on gradient-based methods, including perturbation analysis, the likelihood ratio method, and frequency domain experimentation. For illustrative purposes, we compare and contrast the implementation of the techniques for some simple discrete-event systems such as the (s;S) inventory system and the GI=G=1 queue. Finally, we speculate on future directions for the fleld, particularly in the context of the rapid advances being made in parallel computing.

http://users.iems.northwestern.edu/~nocedal/acnw/2005/read/paper.pdf

Optimization via simulation: a review

Simulation optimization can be defined as the process of finding the best input variable values from among all possibilities without explicitly evaluating each possibility. The objective of simulation optimization is to minimize the resources spent while maximizing the information obtained in a simulation experiment. The purpose of this paper is to review the area of simulation optimization. A critical review of the methods employed and applications developed in this relatively new area are presented and notable successes are highlighted. Simulation optimization software tools are discussed. The intended audience is simulation practitioners and theoreticians as well as beginners in the field of simulation.

/pdf/simulation-optimization-methods-and-applications-1jat84lt31.pdf

Simulation optimization: methods and applications

This paper reviews statistical methods for analyzing output data from computer simulations. First, it focuses on the estimation of steady-state system parameters. The estimation techniques include the replication/deletion approach, the regenerative method, the batch means method, and methods based on standardized time series. Second, it reviews recent statistical procedures to find the best system among a set of competing alternatives.

Proceedings of the 2000 winter simulation conference

Discrete event simulation optimization is a problem of significant interest to practitioners interested in extracting useful information about an actual (or yet to be designed) system that can be modeled using discrete event simulation. This paper presents a brief survey of the literature on discrete event simulation optimization over the past decade (1988 to the present). Swisher et al. (2000) provides a more comprehensive review of this topic while Jacobson and Schruben (1989) covers the literature preceding 1988. Optimization of both discrete and continuous input parameters are examined. The continuous input parameter case is separated into gradient and non-gradient based optimization procedures. The discrete input parameter case differentiates techniques appropriate for small and for large numbers of feasible input parameter values.

/pdf/a-survey-of-simulation-optimization-techniques-and-1fbd4b2eyy.pdf

A survey of simulation optimization techniques and procedures

/pdf/proceedings-of-the-1997-winter-simulation-conference-4cfat1wn9s.pdf

Proceedings of the 1997 winter simulation conference

Investigates the finite-time behavior of two specific simulation optimization algorithms: a Robbins-Monro procedure applied in a conventional way and a more recently proposed single-run optimization algorithm. By applying these algorithms to simple systems it is shown that, in practice, convergence of the former algorithm can be slow while that of the latter is very fast. The authors also provide evidence that the choice of projection operator (to deal with constraints in the optimization problem) has a significant effect on the finite-time performance of the latter algorithm. These results provide some basic insight into the behavior of such algorithms. >

Ying Tat Leung

Papers

Finite-time behavior of two simulation optimization algorithms