Utilize este identificador para referenciar este registo: http://hdl.handle.net/10071/14582
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dc.contributor.authorPaulo, H.-
dc.contributor.authorCardoso-Grilo, T.-
dc.contributor.authorRelvas, S.-
dc.contributor.authorBarbosa-Póvoa, A. P.-
dc.date.accessioned2017-11-03T15:57:13Z-
dc.date.available2017-11-03T15:57:13Z-
dc.date.issued2017-
dc.identifier.citationPaulo, H., Cardoso-Grilo, T., Relvas, S. & Barbosa-Póvoa, A. P. (2017). Designing integrated biorefineries supply chain: combining stochastic programming models with scenario reduction methods. In Antonio Espuña, Moisès Graells and Luis Puigjaner (Ed.), 27th European Symposium on Computer Aided Process Engineering – ESCAPE 27. (pp. 901-906). Barcelona: Elsevier.en_US
dc.identifier.urihttps://ciencia.iscte-iul.pt/id/ci-pub-39880-
dc.identifier.urihttp://hdl.handle.net/10071/14582-
dc.description.abstractThis paper addresses the design and planning of integrated biorefineries supply chain under uncertainty. A two-stage stochastic mixed integer linear programming (MILP) model is proposed considering the presence of uncertainty in the residual lignocellulosic biomass availability and technology conversion factors. Nevertheless, when the scenario tree approach is applied to a large real world case study, it generates a computationally complex problem to solve. To address this challenge the present paper proposes the improvement of the scenario tree approach through the use of two scenario reduction methods. The results illustrate the impact of the uncertain parameters over the network configuration of a real case when compared with the deterministic solution. Both scenario reduction methods appear promising and should be further explored when solving large scenario trees problems.por
dc.language.isoengpor
dc.publisherElsevierpor
dc.rightsopenAccesspor
dc.subjectSupply chain designpor
dc.subjectMILP modelpor
dc.subjectIntegrated biorefinerypor
dc.subjectStochastic programmingpor
dc.subjectScenario reduction methodspor
dc.titleDesigning integrated biorefineries supply chain: combining stochastic programming models with scenario reduction methodspor
dc.typeconferenceObjectpor
dc.pagination901-906en_US
dc.peerreviewedyespor
dc.journal27th European Symposium on Computer Aided Process Engineering – ESCAPE 27en_US
degois.publication.firstPage901por
degois.publication.lastPage906por
degois.publication.locationBarcelonapor
degois.publication.title27th European Symposium on Computer Aided Process Engineering – ESCAPE 27por
dc.date.updated2017-11-03T15:56:28Z-
dc.identifier.doi10.1016/B978-0-444-63965-3.50152-5-
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