Mathematical Programming: Series A and B
A lift-and-project cutting plane algorithm for mixed 0-1 programs
Mathematical Programming: Series A and B
Mixed 0-1 programming by lift-and-project in a branch-and-cut framework
Management Science
Journal of Global Optimization
Mathematical Programming: Series A and B
The Million-Variable "March" for Stochastic Combinatorial Optimization
Journal of Global Optimization
Decomposition with branch-and-cut approaches for two-stage stochastic mixed-integer programming
Mathematical Programming: Series A and B
A comparative study of decomposition algorithms for stochastic combinatorial optimization
Computational Optimization and Applications
Fenchel decomposition for stochastic mixed-integer programming
Journal of Global Optimization
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Two-stage stochastic mixed-integer programming (SMIP) problems with recourse are generally difficult to solve. This paper presents a first computational study of a disjunctive cutting plane method for stochastic mixed 0-1 programs that uses lift-and-project cuts based on the extensive form of the two-stage SMIP problem. An extension of the method based on where the data uncertainty appears in the problem is made, and it is shown how a valid inequality derived for one scenario can be made valid for other scenarios, potentially reducing solution time. Computational results amply demonstrate the effectiveness of disjunctive cuts in solving several large-scale problem instances from the literature. The results are compared to the computational results of disjunctive cuts based on the subproblem space of the formulation and it is shown that the two methods are equivalently effective on the test instances.