Comparison of Efficient Methods for Solving a Large-Scale Multistage Stochastic Program

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2009-01-01
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Wang, Yan
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Ryan, Sarah
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Industrial and Manufacturing Systems Engineering
The Department of Industrial and Manufacturing Systems Engineering teaches the design, analysis, and improvement of the systems and processes in manufacturing, consulting, and service industries by application of the principles of engineering. The Department of General Engineering was formed in 1929. In 1956 its name changed to Department of Industrial Engineering. In 1989 its name changed to the Department of Industrial and Manufacturing Systems Engineering.
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We use a rolling two-stage procedure for solving a multistage stochastic program to assess the effects of uncertain fuel costs on optimal energy flows in the U.S. The optimal solution to the largest deterministic equivalent is obtained via Benders decomposition. We apply methods including temporal aggregation and scenario reduction to find approximate solutions which require less computational effort. These methods exploit both the network structure of the model and the multistage nature of forecast revision and uncertainty resolution. We evaluate the approximations based on similarity of the effects of uncertainty on the optimal flows compared to the exact solution.

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This is a proceeding published as Yan Wang, Sarah M. Ryan, Comparison of Efficient Methods for Solving a Large-Scale Multistage Stochastic Program. Proceedings of the 2009 Industrial Engineering Research Conference. 2009. Posted with permission.

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Thu Jan 01 00:00:00 UTC 2009