Ph.D. Career Development Workshop by INFORMS Chapter

ISE DEPARTMENT PH.D. CAREER DEVELOPMENT WORKSHOP ANNOUNCEMENT

Title:

Recent Progress in SAS Optimization Tools and Solutions

Speaker: 

Dr. Yan Xu,   SAS Institute Inc.

Time and location:

2:30-3:30pm, Feb. 23, 375 Mohler Lab

Abstract: 

SAS has significantly increased its investment in the area of Optimization over the past several years.  It is an area of growing importance with the current economic climate     being a major driving force behind it.  SAS provides a suite of optimization tools which includes a powerful algebraic modeling language that transparently represents model formulations, and a set of optimization solvers for linear, mixed-integer, quadratic and nonlinear programs. Based on these tools and leveraging the power of SAS in other areas, a number of solutions have been successfully developed for tackling industrial problems like optimizing retail pricing, increasing marketing effectiveness, reducing inventory cost, etc. In this talk, we first present the latest techniques that we used to improve SAS solvers. Then, we show how to model and solve real world optimization problem by using SAS optimization tools. Finally, we discuss several challenges that we are facing in further improving optimization products.

Biography:

Yan Xu is an Analytical Solutions manager in the Operations Research department at SAS Institute Inc., Cary, North Carolina, where he leads the numerical optimization team to develop linear, mixed-integer, nonlinear, and local search optimization solvers. Dr. Xu received his undergraduate and master degree from Fudan University in China and his Ph.D. from Lehigh University in the area of parallel tree searching algorithms. Dr. Xu’s current work focuses on the design and implementation of the algorithms for solving general linear and mixed-integer optimization problems.  Dr. Xu is a full member of COIN-OR. He has published papers in journals such as INFORMS Journal of Computing, and won several awards in the area of computational optimization.