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Decomposition Algorithms for Two-Stage Chance-Constrained Programs

Xiao Liu (liu.2738***at***osu.edu)
Simge Kucukyavuz (kucukyavuz.2***at***osu.edu)
James Luedtke (jrluedt1***at***wisc.edu)

Abstract: We study a class of chance-constrained two-stage stochastic optimization problems where second-stage feasible recourse decisions incur additional cost. In addition, we propose a new model, where ``recovery" decisions are made for the infeasible scenarios to obtain feasible solutions to a relaxed second-stage problem. We develop decomposition algorithms with specialized optimality and feasibility cuts to solve this class of problems. Computational results on a chance-constrained resource planing problem indicate that our algorithms are highly effective in solving these problems compared to a mixed-integer programming reformulation and a naive decomposition method.

Keywords: two-stage stochastic programming , chance constraints , Benders decomposition , cutting planes

Category 1: Integer Programming

Category 2: Stochastic Programming

Category 3: Combinatorial Optimization (Branch and Cut Algorithms )

Citation: March/2014

Download: [PDF]

Entry Submitted: 03/02/2014
Entry Accepted: 03/02/2014
Entry Last Modified: 08/11/2014

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