Observational data-based quality assessment of scenario generation for stochastic programs

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dc.contributor.authorAy, Didem Sarı
dc.contributor.authorRyan, Sarah M.
dc.date.accessioned2021-02-19T21:16:34Z
dc.date.available2021-02-19T21:16:34Z
dc.date.issued2019
dc.departmentALKÜ
dc.descriptionRyan, Sarah/0000-0001-5903-1432; SARI AY, Didem/0000-0002-9403-9015
dc.description.abstractIn minimization problems with uncertain parameters, cost savings can be achieved by solving stochastic programming (SP) formulations instead of using expected parameter values in a deterministic formulation. To obtain such savings, it is crucial to employ scenarios of high quality. An appealing way to assess the quality of scenarios produced by a given method is to conduct a re-enactment of historical instances in which the scenarios produced are used when solving the SP problem and the costs are assessed under the observed values of the uncertain parameters. Such studies are computationally very demanding. We propose two approaches for assessment of scenario generation methods using past instances that do not require solving SP instances. Instead of comparing scenarios to observations directly, these approaches consider the impact of each scenario in the SP problem. The methods are tested in simulation studies of server location and unit commitment, and then demonstrated in a case study of unit commitment with uncertain variable renewable energy generation.
dc.identifier.doi10.1007/s10287-019-00349-1
dc.identifier.endpage540en_US
dc.identifier.issn1619-697X
dc.identifier.issn1619-6988
dc.identifier.issue3en_US
dc.identifier.scopusqualityQ3
dc.identifier.startpage521en_US
dc.identifier.urihttps://doi.org/10.1007/s10287-019-00349-1
dc.identifier.urihttps://hdl.handle.net/20.500.12868/480
dc.identifier.volume16en_US
dc.identifier.wosWOS:000476740000007
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthor0-belirlenecek
dc.language.isoen
dc.publisherSpringer Heidelberg
dc.relation.ispartofComputational Management Science
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectStochastic programming
dc.subjectScenario generation method assessment
dc.subjectScenario quality
dc.titleObservational data-based quality assessment of scenario generation for stochastic programs
dc.typeArticle

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