Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/6249
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dc.contributor.advisorMitra, G-
dc.contributor.advisorLucas, CA-
dc.contributor.authorValente, Christian-
dc.date.accessioned2012-02-14T16:34:40Z-
dc.date.available2012-02-14T16:34:40Z-
dc.date.issued2011-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/6249-
dc.descriptionThis thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.en_US
dc.description.abstractDecision making under uncertainty is an important yet challenging task; a number of alternative paradigms which address this problem have been proposed. Stochastic Programming (SP) and Robust Optimization (RO) are two such modelling ap-proaches, which we consider; these are natural extensions of Mathematical Pro-gramming modelling. The process that goes from the conceptualization of an SP model to its solution and the use of the optimization results is complex in respect to its deterministic counterpart. Many factors contribute to this complexity: (i) the representation of the random behaviour of the model parameters, (ii) the interfac-ing of the decision model with the model of randomness, (iii) the difficulty in solving (very) large model instances, (iv) the requirements for result analysis and perfor-mance evaluation through simulation techniques. An overview of the software tools which support stochastic programming modelling is given, and a conceptual struc-ture and the architecture of such tools are presented. This conceptualization is pre-sented as various interacting modules, namely (i) scenario generators, (ii) model generators, (iii) solvers and (iv) performance evaluation. Reflecting this research, we have redesigned and extended an established modelling system to support modelling under uncertainty. The collective system which integrates these other-wise disparate set of model formulations within a common framework is innovative and makes the resulting system a powerful modelling tool. The introduction of sce-nario generation in the ex-ante decision model and the integration with simulation and evaluation for the purpose of ex-post analysis by the use of workflows is novel and makes a contribution to knowledge.en_US
dc.language.isoenen_US
dc.publisherBrunel University, School of Information Systems, Computing and Mathematics-
dc.relation.ispartofSchool of Information Systems, Computing and Mathematics-
dc.relation.urihttp://bura.brunel.ac.uk/bitstream/2438/6249/1/FulltextThesis.pdf-
dc.subjectScenario generationen_US
dc.subjectSimulationen_US
dc.subjectOptimisationen_US
dc.subjectUncertaintyen_US
dc.subjectDecision evaluationen_US
dc.titleDesign and architecture of a stochastic programming modelling systemen_US
dc.typeThesisen_US
Appears in Collections:Dept of Mathematics Theses
Mathematical Sciences

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