Chapter 9

Optimization via Simulation Over Discrete Decision Variables

Barry L. Nelson
Department of Industrial Engineering and Management Sciences,Northwestern University, Evanston, Illinois 60208, nelsonb@northwestern.edu

Abstract
Both the simulation research and software communities have been interested in optimization via simulation (OvS), by which we mean maximizing or minimizing the expected value of some output of a stochastic simulation. Continuous-decision-variable OvS, and gradient estimation to support it, has been an active research area with significant advances. However, the decision variables in many operations research and management science simulations are more naturally discrete, even categorical. In this tutorial we describe some of the research directions and results available for discrete-decision-variable OvS, and provide some guidance for using the OvS heuristics that are built into simulation modeling software.

Key words: simulation; stochastic optimization; random search; ranking and selection

Download Chapter 9 - Password-protected content.

The 2010 volume of the TutORials in Operations Research series will be available to people who have registered for the 2010 INFORMS Annual Meeting. All INFORMS members will be able to access TutORials after January 1, 2011. Printed TutORials books from this and previous years can also be ordered here, along with CDs from 2005 to 2009.

For login instructions click here.

________________________________________________

Citation Information:

Nelson, B. L. 2010. Optimization via simulation over discrete decision variables. J. J. Hasenbein, ed. TutORials in Operations Research, Vol. 7. INFORMS, Hanover, MD, pp. 193–207.