Chapter 5

Particle Methods for Data-Driven Simulation and Optimization

John R. Birge
The University of Chicago Booth School of Business, Chicago, Illinois 60637, John.Birge@ChicagoBooth.edu

Abstract

Particle methods provide a robust methodology for estimation and prediction. They can also apply to traditional operations research areas by, for example, providing an effective alternative for constructing Monte Carlo simulations of various systems and for dynamic optimization. This tutorial explains the fundamental ideas behind particle methods and how to use them in typical operations research contexts.

Key words: particle methods; simulation; optimization

The 2012 volume of the TutORials in Operations Research series will be available to people who have registered for the 2012 INFORMS Annual Meeting. All INFORMS members will be able to access TutORials after January 1, 2013. Printed TutORials books from this and previous years can also be ordered here, along with CDs from 2005 to 2009.

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Citation information:

Birge J. R. Particle Methods for Data-Driven Simulation and Optimization. P. Mirchandani, ed. INFORMS TutORials in Operations Research, Vol. 9. INFORMS, Hanover, MD, pp. 92--102.