Distributed discrete optimization under uncertainty
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abstract
Combinatorial optimization problems have applications in a variety of disciplines. In the presence of data uncertainty, these problems lead to stochastic optimization problems which result in very large scale combinatorial optimization problems with discrete decision variables. This class of problems falls in the area of Stochastic Mixed-Integer Programming or SMIP. Solving these problems is still a challenge and therefore, distributed implementations of the solution methods for these problems should be explored. In this paper we present a distributed computing design for a novel general decomposition- coordination method for SMIP.
