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An enhanced decomposition algorithm for multistage stochastic hydroelectric scheduling

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Abstract

Handling uncertainty in natural inflow is an important part of a hydroelectric scheduling model. In a stochastic programming formulation, natural inflow may be modeled as a random vector with known distribution, but the size of the resulting mathematical program can be formidable. Decomposition-based algorithms take advantage of special structure and provide an attractive approach to such problems. We develop an enhanced Benders decomposition algorithm for solving multistage stochastic linear programs. The enhancements include warm start basis selection, preliminary cut generation, the multicut procedure, and decision tree traversing strategies. Computational results are presented for a collection of stochastic hydroelectric scheduling problems.

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Authors and Affiliations

  1. Graduate Program in Operations Research and Industrial Engineering, Department of Mechanical Engineering, The University of Texas at Austin, 78712, Austin, TX, USA

    David P. Morton

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  1. David P. Morton
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Morton, D.P. An enhanced decomposition algorithm for multistage stochastic hydroelectric scheduling. Ann Oper Res 64, 211–235 (1996). https://doi.org/10.1007/BF02187647

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