Robust solutions of bi-blend recipe optimization with quadratic constraints

dc.centroE.T.S.I. Informáticaes_ES
dc.contributor.advisorHendrix, Eligius María Theodorus
dc.contributor.advisorCasado, Leocadio G.
dc.contributor.authorNiewerth, Freek
dc.date.accessioned2016-07-20T10:13:29Z
dc.date.available2016-07-20T10:13:29Z
dc.date.created2009-02
dc.date.issued2016-07-20
dc.departamentoArquitectura de Computadores
dc.description.abstractProduction companies use raw materials to compose end-products. They often make different products with the same raw materials. In this research, the focus lies on the production of two end-products consisting of (partly) the same raw materials as cheap as possible. Each of the products has its own demand and quality requirements consisting of quadratic constraints. The minimization of the costs, given the quadratic constraints is a global optimization problem, which can be difficult because of possible local optima. Therefore, the multi modal character of the (bi-) blend problem is investigated. Standard optimization packages (solvers) in Matlab and GAMS were tested on their ability to solve the problem. In total 20 test cases were generated and taken from literature to test solvers on their effectiveness and efficiency to solve the problem. The research also gives insight in adjusting the quadratic constraints of the problem in order to make a robust problem formulation of the bi-blend problem.es_ES
dc.identifier.urihttp://hdl.handle.net/10630/11859
dc.language.isoenges_ES
dc.rightsby-nc-nd*
dc.rights.accessRightsopen accesses_ES
dc.subjectOptimización matemáticaes_ES
dc.subject.otherGlobal optimizationes_ES
dc.subject.otherQuadratic inequalitieses_ES
dc.subject.otherDesignes_ES
dc.subject.otherMixture designes_ES
dc.subject.otherBlendinges_ES
dc.titleRobust solutions of bi-blend recipe optimization with quadratic constraintses_ES
dc.typemaster thesises_ES
dspace.entity.typePublication
relation.isAdvisorOfPublication0c3992b1-f2f1-4f53-a186-1dbf6d6cef5a
relation.isAdvisorOfPublication.latestForDiscovery0c3992b1-f2f1-4f53-a186-1dbf6d6cef5a

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