LANS Informal Seminar: Susan Hunter

Seminar Title: Bi-objective simulation optimization on integer lattices using the epsilon-constraint method in a retrospective approximation framework
Speaker: Susan Hunter, Assistant Professor, School of Industrial Engineering, Purdue University

Date/Time: 2018-07-18 10:30
Location: Bldg. 240. Room 4301

Description:
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