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dc.contributor.advisorMorales-González, Juan Miguel 
dc.contributor.advisorPineda-Morente, Salvador 
dc.contributor.authorMuñoz Diaz, Miguel Angel
dc.contributor.otherMatemática Aplicadaes_ES
dc.date.accessioned2022-11-03T12:59:34Z
dc.date.available2022-11-03T12:59:34Z
dc.date.created2022-09-06
dc.date.issued2022-11-03
dc.identifier.urihttps://hdl.handle.net/10630/25335
dc.descriptionElectricity markets are a clear example of a sector in which decision making plays a crucial role in its daily activity. Moreover, uncertainty is intrinsic to electricity markets and affects most of the tasks that agents operating in them must carry out. Many of these tasks involve decisions characterized by low risk and being addressed periodically. In this thesis, we refer to these tasks as iterative decisions. This thesis applies the aforementioned innovative frameworks for decision making under uncertainty using contextual information in iterative decision making tasks faced daily by electricity market agents.es_ES
dc.description.abstractDecision making is critical for any business to survive in a market environment. Examples of decision making tasks are inventory management, resource allocation or portfolio selection. Optimization, understood as the scientific discipline that studies how to solve mathematical programming problems, can help make more efficient decisions in many of these situations. Particularly relevant, because of their frequency and difficulty, are those decisions affected by uncertainty, i.e., in which some of the parameters that precisely determine the optimization problem are unknown when the decision must be made. Fortunately, the development of information technologies has led to an explosion in the availability of data that can be used to assist decisions affected by uncertainty. However, most of the available historical data do not correspond to the unknown parameter of the problem but originate from other related sources. This subset of data, potentially valuable for obtaining better decisions, is called contextual information. This thesis is framed within a new scientific effort that seeks to exploit the potential of data and, in particular, of contextual information in decision making. To this end, in this thesis, we have developed mathematical frameworks and data-driven optimization models that exploit contextual information to make better decisions in problems characterized by the presence of uncertain parameters.es_ES
dc.language.isoenges_ES
dc.publisherUMA Editoriales_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectElectricidad -- mercadoes_ES
dc.subject.otherMercádos eléctircoses_ES
dc.subject.otherToma de decisioneses_ES
dc.subject.otherIncertidumbrees_ES
dc.subject.otherInvestigación Operativaes_ES
dc.titlePrescriptive Analytics in Electricity Marketses_ES
dc.typeinfo:eu-repo/semantics/doctoralThesises_ES
dc.centroEscuela de Ingenierías Industrialeses_ES
dc.rights.ccAttribution-NonCommercial-NoDerivatives 4.0 Internacional*


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