Masterclass: Ranking & Selection for Simulation Optimization

© Barry L. Nelson
Department of Industrial Engineering & Management Sciences
Northwestern University
STOR-i & Department of Management Science
Lancaster University

All material on this site is the copyrighted property of Barry L. Nelson. It is free to use for educational purposes, including classroom instruction, but may not be marketed or sold, and the software may not be embedded in any other software product without permission of the author.


Simulation optimization means optimizing the performance of a stochastic system that can only be analyzed via simulation. If the number of feasible solutions is finite and small enough, then simulating all of them is possible; ranking & selection (R&S) procedures are algorithms for doing just that in a statistically sound and efficient way. The availability of cheap, high-performance parallel computing has redefined "small enough" to be hundreds of thousands to millions of feasible solution. This is a hands-on class in which students will learn the mathematical-statistics foundations of R&S, apply R&S procedures to simulation optimization problems, and create their own R&S procedure. Knowledge of R and RStudio is useful, but not strictly required, and is only used to allow students to run examples.

R Code

Download these RScript files to participate in the hands-on examples that apply various R&S procedures to five different simulation optimization problems.



All slides

References only

Supporting Text

Foundations of Ranking & Selection for Simulation Optimization

last update 1/27/2022