Optimization in simulation: Current issues and the future outlook

Abstract

Simulation is commonly used to find the best values of decision variables for problems which defy analytical solutions. This objective is similar to that of optimization problems and thus, mathematical programming techniques may be applied to simulation. However, the application of mathematical programming techniques, e.g., the gradient methods, to simulation is compounded by the random nature of simulation responses and by the complexity of the statistical issues involved. The literature relevant to optimization in simulation is scattered, and no comprehensive and up‐to‐date treatment of the subject is presently available. To that end, this article brings together numerous concepts related to t he problem of optimization in simulation. Specifically, it discusses the application of mathematical programming techniques to optimization in simulation, response surface methodology and designs, perturbation analysis, and frequency domain simulation experiments. The article provides a user with an overview of the available optimization techniues and identifies future research possibilities.

A study of response surface in simulation of emergency room systems

“…According to Safizadeh (1990), RSM assumes that the user is able to identify, at least approximately, the region of interest as is characterized by the constraints that were defined for the problem.…”

Simulation optimization: a review of algorithms and applications

“…[135,99,174,15,59,29,7,16,191,61,60,194,62,9,88,5,152]), books and research monographs (cf. [186,171,115,35]), and theses (cf.…”

An On-line Simulation Approach to Search Efficient Values of Decision Variables in Stochastic Systems

“…Simulation optimization problems have been discussed continuously by Glynn (1986), Meketon (1987), Jacobson and Schruben (1989), Safizadeh (1990), Ho and Cao (1991), Rubinstein and Shapiro (1992), etc. Methods using Finite Differences, which is widely used in optimization, have the disadvantage that at least n + 1 simulation runs are necessary to estimate the gradient of a given problem when the number of parameters is n (Heidergott 1995).…”