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Enhanced Cut Generation Methods for Decomposition-Based Branch and Cut for Two-Stage Stochastic Mixed-Integer Programs

Department of Industrial, Welding and Systems Engineering, Data Driven Decisions Lab, The Ohio State University, Columbus, Ohio 43210
Department of Industrial, Welding and Systems Engineering, Data Driven Decisions Lab, The Ohio State University, Columbus, Ohio 43210
yuan.65{at}osu.edu
sen.22{at}osu.edu

This paper is devoted to a study of computational speed-ups that may be possible in cut generation associated with decomposition-based branch-and-cut methods (e.g., D²-BAC) for stochastic mixed-integer programs (SMIPs). We discuss some bottlenecks in the cut generation process and suggest several enhancements to speed up this process. Our computational results show that significant improvements (approximately 50% reduction in computation times) may be possible by streamlining the computations associated with the cut generation process. This paper establishes new benchmarks for serial processing of two-stage SMIPs.

Key words: stochastic mixed-integer programming; disjunctive decomposition; cut generation; stochastic server location
History: received January 2008; revised June 2008; accepted August 2008.