A Clustering Approach to Solving Large Stochastic Matching Problems

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A Clustering Approach to Solving Large Stochastic Matching Problems
In this work we focus on efficient heuristics for solving a class of stochastic planning problems that arise in a variety of business, investment, and industrial applications. The problem is best described in terms of future buy and sell contracts. By buying less reliable, but less expensive, buy (supply) contracts, a company or a trader can cover a positionof more reliable and more expensive sell contracts. The goal is to maximize the expected net gain (profit) by constructing a close to optimum portfolioout of the available buy and sell contracts. This stochastic planning problem can be formulated as a two-stage stochastic linear programming problem with recourse. However, this formalization leads to solutions that are exponential in the number of possible failure combinations. Thus, this approach is not feasible for large scale problems. In this work we investigate heuristic approximation techniques alleviating the efficiency problem. We primarily focus on the clustering approach a...
Milos Hauskrecht, Eli Upfal
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2001
Where UAI
Authors Milos Hauskrecht, Eli Upfal
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