9/13/18 | 4:15pm | E51-335
Reception to follow.

Susan Hunter

Assistant Professor
Purdue University 

Abstract: We propose the Retrospective Partitioned Epsilon-constraint with Relaxed Local Enumeration (R-PERLE) algorithm to solve the bi-objective simulation optimization problem on integer lattices. In this nonlinear optimization problem, both objectives can only be observed with stochastic error, the decision variables are integer-valued, and a local solution is called a local efficient set. R-PERLE employs a version of sample average approximation called retrospective approximation (RA) to repeatedly call the PERLE sample-path solver at a sequence of increasing sample sizes, using the solution from the previous RA iteration as a warm start for the current RA iteration. As the number of RA iterations increases, R-PERLE provably converges to a local efficient set with probability one under appropriate regularity conditions. We discuss the design principles that make our algorithm efficient, and demonstrate that R-PERLE performs favorably relative to the current state of the art, MO-COMPASS, in our numerical experiments.

This work is joint with Kyle Cooper and Kalyani Nagaraj.
A preprint is available at: http://www.optimization-online.org/DB_HTML/2018/06/6649.html

Bio: Susan R. Hunter is an assistant professor in the School of Industrial Engineering at Purdue University. She received her Ph.D. in 2011 from the Grado Department of Industrial and Systems Engineering at Virginia Tech, and was a postdoctoral associate in the School of Operations Research and Information Engineering at Cornell University from 2011 to 2013. Her research interests include multi-objective simulation optimization, methodological issues that arise when implementing simulation optimization algorithms in parallel computing environments, and applications of simulation optimization in plant breeding.