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Chance Constrained Mixed Integer Program: Bilinear and Linear Formulations, and Benders Decomposition

Bo Zeng (bzeng***at***usf.edu)
Yu An (yan2***at***mail.usf.edu)
Ludwig Kuznia (Ludwig.C.Kuznia***at***disney.com)

Abstract: In this paper, we study chance constrained mixed integer program with consideration of recourse decisions and their incurred cost, developed on a finite discrete scenario set. Through studying a non-traditional bilinear mixed integer formulation, we derive its linear counterparts and show that they could be stronger than existing linear formulations. We also develop a variant of Jensen's inequality that extends the one for stochastic program. To solve this challenging problem, we present a variant of Benders decomposition method in bilinear form, which actually provides an easy-to-use algorithm framework to integrate existing improvement techniques, along with a few enhancement strategies based on the structure of chance constrained program. Computational study shows that the presented Benders decomposition method outperforms a commercial solver by an order of magnitude on solving chance constrained program or detecting its infeasibility.

Keywords: chance constraint, stochastic program, bilinear formulation, linear formulation, Jensen's inequality, Benders decomposition

Category 1: Stochastic Programming

Category 2: Integer Programming

Citation:

Download: [PDF]

Entry Submitted: 03/30/2014
Entry Accepted: 03/31/2014
Entry Last Modified: 05/30/2014

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