We provide a descriptive review of the main approaches for carrying out simulation
optimization, and sample some recent algorithmic and theoretical developments in …

We describe the basic principles of ranking and selection, a collection of experiment-design
techniques for comparing “populations” with the goal of finding the best among them. We …

What is reinforcement learning? How is reinforcement learning different from stochastic
optimization? And finally, can it be used for applications to quantitative finance for my current …

Probably one of the most successful interfaces between operations research and computer
science has been the development of discrete-event simulation software. The recent …

Ordinal Optimization has emerged as an efficient technique for simulation and optimization.
Exponential convergence rates can be achieved in many cases. In this paper, we present a …

In a sequential Bayesian ranking and selection problem with independent normal
populations and common known variance, we study a previously introduced measurement …

For several decades, simulation has been used as a descriptive tool by the operations
research community in the modeling and analysis of a wide variety of complex real systems …

Managers of large industrial projects often measure performance by multiple attributes. For
example, our paper is motivated by the simulation of a large industrial project called a land …

This article considers a multi-objective Ranking and Selection (R+ S) problem, where the
system designs are evaluated in terms of more than one performance measure. The concept …

We consider the problem of finding the system with the best primary performance measure
among a finite number of simulated systems in the presence of a stochastic constraint on a …