Optimization via simulation: A review

The paper reviews techniques for optimizing stochastic discrete-event systems via simulation. It discusses both the discrete parameter case and the continuous parameter case, but concentrates on the latter which has dominated most of the recent research in the area. For the discrete parameter case, the paper focuses on the techniques for optimization from a finite set: multiple-comparison procedures and ranking-and-selection procedures. For the continuous parameter case, it focuses on gradient-based methods, including perturbation analysis, the likelihood ratio method, and frequency domain experimentation. For illustrative purposes, the paper compares and contrasts 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, it speculates on future directions for the field, particularly in the context of the rapid advances being made in parallel computing.