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• W2193628964 abstract "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." @default.
• W2193628964 created "2016-06-24" @default.
• W2193628964 creator A5057585686 @default.
• W2193628964 date "2015-10-05" @default.
• W2193628964 modified "2023-10-17" @default.
• W2193628964 title "Clustering of sample average approximation for stochastic program" @default.
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• W2193628964 doi "https://doi.org/10.18452/3076" @default.
• W2193628964 hasPublicationYear "2015" @default.
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