Decomposition algorithms for two-stage chance-constrained programs

Xiao Liu, Simge Küçükyavuz*, James Luedtke

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

46 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)219-243
Number of pages25
JournalMathematical Programming
Issue number1
StatePublished - May 1 2016


  • Benders decomposition
  • Chance constraints
  • Cutting planes
  • Two-stage stochastic programming

ASJC Scopus subject areas

  • Software
  • Mathematics(all)


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