Dr. Yongjia Song. Recent Advances in Chance-constrained Stochastic Programs, (May 05, 2016, 14:00-15:00,Thursday) Room N510, Shunde Building 2016.04.11

TitleRecent Advances in Chance-constrained Stochastic Programs


PresenterDr. Yongjia Song


Host Dr. Xiaolei Xie


Date May 05, 2016, 14:00-15:00 (Thursday)


VenueRoom 510, Shunde Building




Abstract In this talk, we first briefly review the background of chance-constrained stochastic programming (CCSP) as well as the state-of-the-art solution methodology for CCSPs. We will then focus on a recently proposed solution method based on the Lagrangian duals for CCSPs, where the nonanticipativity constraints are relaxed. We compare the strength of the proposed dual bounds and demonstrate that they are superior to the bound obtained from the continuous relaxation of a standard mixed-integer programming (MIP) formulation. We also derive two new MIP formulations for CCSPs, and demonstrate that for chance-constrained linear programs, the continuous relaxations of these formulations yield bounds equal to the proposed dual bounds. Promising computational results indicate the superiority of the proposed methods.


BioDr. Yongjia Song is an assistant professor in the Department of Statistical Sciences and Operations Research at Virginia Commonwealth University (VCU). He received the B.S. degree in computational mathematics from Peking University, China in 2009, the M.S. degrees in industrial engineering and computer sciences from University of Wisconsin-Madison in 2012, and the Ph.D. degree in industrial engineering from University of Wisconsin-Madison in 2013. His research interests include optimization under uncertainty (stochastic and robust optimization), integer programming (linear and nonlinear), and applications of optimization in transportation, power system, and data analytics. His research has been supported by NSF. 

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