Computational solution of capacity planning models under uncertainty
Parallel Computing - Special issue on parallel computing in economics, finance and decision-making
Computational Optimization and Applications
A Multi-Stage Stochastic Integer Programming Approach for Capacity Expansion under Uncertainty
Journal of Global Optimization
Journal of Global Optimization
Decomposition algorithms for stochastic combinatorial optimization: computational experiments and extensions
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
Computations with disjunctive cuts for two-stage stochastic mixed 0-1 integer programs
Journal of Global Optimization
Fenchel decomposition for stochastic mixed-integer programming
Journal of Global Optimization
Computers and Industrial Engineering
Hi-index | 0.00 |
This paper presents comparative computational results using three decomposition algorithms on a battery of instances drawn from two different applications. In order to preserve the commonalities among the algorithms in our experiments, we have designed a testbed which is used to study instances arising in server location under uncertainty and strategic supply chain planning under uncertainty. Insights related to alternative implementation issues leading to more efficient implementations, benchmarks for serial processing, and scalability of the methods are also presented. The computational experience demonstrates the promising potential of the disjunctive decomposition (D 2) approach towards solving several large-scale problem instances from the two application areas. Furthermore, the study shows that convergence of the D 2 methods for stochastic combinatorial optimization (SCO) is in fact attainable since the methods scale well with the number of scenarios.