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dc.contributor.authorMorton, David P.
dc.date1994-01
dc.date.accessioned2013-02-27T23:36:48Z
dc.date.available2013-02-27T23:36:48Z
dc.date.issued1994-01
dc.identifier.urihttp://hdl.handle.net/10945/29272
dc.description.abstractHandling 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. Stochastic programming, Hydroelectric scheduling, Large-scale Systemsen_US
dc.description.sponsorshipNational Research Councilen_US
dc.description.urihttp://archive.org/details/enhanceddecompos00mort
dc.format.extentNAen_US
dc.language.isoen_US
dc.publisherMonterey, California. Naval Postgraduate Schoolen_US
dc.subject.lcshHYDROELECTRICITY.en_US
dc.titleAn Enhanced Decomposition Algorithm for Multistage Stochastic Hydroelectric Schedulingen_US
dc.title.alternativeNAen_US
dc.typeTechnical Reporten_US
dc.contributor.corporateNaval Postgraduate School (U.S.)
dc.contributor.departmentOperations Research
dc.subject.authorStochastic Programming, Hydroelectric Scheduling, Large-Scale Systemsen_US
dc.description.funderNaval Postgraduate School, Monterey, California.en_US
dc.description.recognitionNAen_US
dc.identifier.oclcNA
dc.identifier.npsreportNPS-OR-94-001
dc.description.distributionstatementApproved for public release; distribution is unlimited.


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