Combinatorial optimization
Decomposition Algorithms for Stochastic Programming on a Computational Grid
Computational Optimization and Applications
Journal of Global Optimization
A finite branch-and-bound algorithm for two-stage stochastic integer programs
Mathematical Programming: Series A and B
International Journal of Computational Science and Engineering
A comparative study of decomposition algorithms for stochastic combinatorial optimization
Computational Optimization and Applications
Computations with disjunctive cuts for two-stage stochastic mixed 0-1 integer programs
Journal of Global Optimization
A general algorithm for solving two-stage stochastic mixed 0-1 first-stage problems
Computers and Operations Research
Computers and Operations Research
A branch-and-cluster coordination scheme for selecting prison facility sites under uncertainty
Computers and Operations Research
Fenchel decomposition for stochastic mixed-integer programming
Journal of Global Optimization
Computers and Industrial Engineering
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Combinatorial optimization problems have applications in a variety of sciences and engineering. In the presence of data uncertainty, these problems lead to stochastic combinatorial optimization problems which result in very large scale combinatorial optimization problems. In this paper, we report on the solution of some of the largest stochastic combinatorial optimization problems consisting of over a million binary variables. While the methodology is quite general, the specific application with which we conduct our experiments arises in stochastic server location problems. The main observation is that stochastic combinatorial optimization problems are comprised of loosely coupled subsystems. By taking advantage of the loosely coupled structure, we show that decomposition-coordination methods provide highly effective algorithms, and surpass the scalability of even the most efficiently implemented backtracking search algorithms.