Decomposition algorithms for two-stage chance-constrained programs

• 成果类型: Article
• 署名作者: Liu, Xiao; Kucukyavuz, Simge; Luedtke, James
• 署名单位: University System of Ohio; Ohio State University; University of Wisconsin System; University of Wisconsin Madison
• 刊物名称: MATHEMATICAL PROGRAMMING
• ISSN/ISSBN: 0025-5610
• DOI: 10.1007/s10107-014-0832-7
• 发表日期: 2016
• 页码: 219-243
• 关键词: discrete-distributions; optimization; equivalents

摘要

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.