Seminar on July 18, 2018



Improved Decomposition Algorithms for Two-Stage

Stochastic Integer Programs


Merve Bodur

(University of Toronto)



Many practical planning, design and operational problems involve making decisions under uncertainty. Also, most of them include some integer decisions. Stochastic programming is a useful tool for dealing with uncertainty and integrality requirements in optimization problems. We consider two-stage stochastic integer programs, where the decision maker must take some (integer) decisions before the uncertainty is revealed, then can observe the realizations and take recourse actions.

These problems yield to large-scale mixed integer programs, which are computationally very challenging, thus decomposition methods are used. The most common solution approach is Benders decomposition. However, standard Benders decomposition algorithm usually fails due to the weakness of the linear programming relaxations. We propose two methods to strengthen Benders decomposition algorithm with integrality-based cuts. We present numerical results on integrated service system staffing and scheduling, capacitated facility location and network interdiction problems that demonstrate the computational efficiency of the proposed approaches. 


Short Bio: 

Merve Bodur is an Assistant Professor in the Department of Mechanical and Industrial Engineering at the University of Toronto. She obtained her Ph.D. from University of Wisconsin-Madison and did a postdoc at Georgia Tech. She received her B.S. in Industrial Engineering and B.A. in Mathematics from Bogazici University, Turkey. Her research interests include theory and applications of stochastic programming, integer programming, multiobjective integer programming and combinatorial optimization.





All interested are cordially invited.  


DATE           : July 18, 2018

TIME           : 14:00

ROOM         : VYKM-2