2015-10-05Buch DOI: 10.18452/3076
Clustering of sample average approximation for stochastic program
We present an improvement to the Sample Average Approximation (SAA) method for two-stage stochastic program. Although the SAA has nice theoretical properties, such as convergence in probability and consistency, as long as the sample is large enough, the requirement on the sample size is always a concern for both academia and practitioners. Our clustering method employs the Maximum Volume Inscribed Ellipsoid (MVIE) to approximate the feasible set of each scenario and calculates a measure of similarity. The scenarios are clustered based on such a measure of similarity and our clustering method reduces the sample size considerably. Moreover, the clustering method will offer managerial implications by highlighting the mattering scenarios. The clustering method would be implemented in a distributed computational infrastructure with low-cost computers.
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