Feature Article: Optimization for simulation: Theory vs. Practice

TLDR

Discrete‑event simulation software has become a highly successful interface between operations research and computer science, and most packages now routinely incorporate optimization routines. The article argues that a gap exists between theory‑driven simulation‑optimization research—focused on stochastic convergence and elegant algorithms—and the general metaheuristic methods adopted by commercial simulation software. It provides a tutorial overview of research‑based approaches and contrasts them with the algorithms implemented in commercial packages. The author speculates on promising research directions and practical future developments to bridge this gap.

Abstract

Probably one of the most successful interfaces between operations research and computer science has been the development of discrete-event simulation software. The recent integration of optimization techniques into simulation practice, specically into commercial software, has become nearly ubiquitous, as most discrete-event simulation packages now include some form of “optimization” routine. The main thesis of this article, how-ever,is that there is a disconnect between research in simulation optimization—which has addressed the stochastic nature of discrete-event simulation by concentratingon theoretical results of convergence and specialized algorithms that are mathematically elegant—and the recent software developments, which implement very general algorithms adopted from techniques in the deterministic optimization metaheuristic literature (e.g., genetic algorithms, tabu search, artificial neural networks). A tutorial exposition that summarizes the approaches found in the research literature is included, as well as a discussion contrasting these approaches with the algorithms implemented in commercial software. The article concludes with the author's speculations on promising research areas and possible future directions in practice.