Decomposition algorithms for two-stage chance-constrained programs

作     者：Liu, Xiao Kucukyavuz, Simge Luedtke, James 

作者机构：Ohio State Univ Dept Integrated Syst Engn Columbus OH 43210 USA Univ Wisconsin Dept Ind & Syst Engn Madison WI USA 

出 版 物：《MATHEMATICAL PROGRAMMING》 (数学规划)

年 卷 期：2016年第157卷第1期

页      面：219-243页

核心收录：

学科分类：1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 070104[理学-应用数学] 0835[工学-软件工程] 0701[理学-数学] 

基　　金：National Science Foundation (NSF) [CMMI-1055668] NSF [CMMI-0952907] Applied Mathematics activity, Advance Scientific Computing Research program within the DOE Office of Science under Argonne National Laboratory Div Of Civil, Mechanical, & Manufact Inn Directorate For Engineering [1055668, 0952907] Funding Source: National Science Foundation 

主　　题：Two-stage stochastic programming Chance constraints Benders decomposition Cutting planes 

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.