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Export Reference (APA) 
Paulo, 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.
Export Reference (IEEE) 
H. Paulo et al.,  "Designing integrated biorefineries supply chain: combining stochastic programming models with scenario reduction methods", in 27th European Symp. on Computer Aided Process Engineering – ESCAPE 27, Antonio Espuña, Moisès Graells and Luis Puigjaner, Ed., Barcelona, Elsevier, 2017, pp. 901-906
Export BibTeX 
@inproceedings{paulo2017_1766417153621,
	author = "Paulo, H. and Cardoso-Grilo, T. and Relvas, S. and Barbosa-Póvoa, A. P.",
	title = "Designing integrated biorefineries supply chain: combining stochastic programming models with scenario reduction methods",
	booktitle = "27th European Symposium on Computer Aided Process Engineering – ESCAPE 27",
	year = "2017",
	editor = "Antonio Espuña, Moisès Graells and Luis Puigjaner",
	volume = "",
	number = "",
	series = "",
	doi = "10.1016/B978-0-444-63965-3.50152-5",
	pages = "901-906",
	publisher = "Elsevier",
	address = "Barcelona",
	organization = "",
	url = "http://www.wcce10.org/index.php/jointevents/escape27"
}
Export RIS 
TY  - CPAPER
TI  - Designing integrated biorefineries supply chain: combining stochastic programming models with scenario reduction methods
T2  - 27th European Symposium on Computer Aided Process Engineering – ESCAPE 27
AU  - Paulo, H.
AU  - Cardoso-Grilo, T.
AU  - Relvas, S.
AU  - Barbosa-Póvoa, A. P.
PY  - 2017
SP  - 901-906
DO  - 10.1016/B978-0-444-63965-3.50152-5
CY  - Barcelona
UR  - http://www.wcce10.org/index.php/jointevents/escape27
AB  - This 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. 
ER  -