@book{
 title = {Design a sustainable supply chain under uncertainty using life cycle optimisation and stochastic programming},
 type = {book},
 year = {2017},
 source = {Chemical Engineering Transactions},
 identifiers = {[object Object]},
 volume = {61},
 id = {6c1379fe-2a51-3a2a-b942-152a90ff1c81},
 created = {2017-12-15T21:38:14.465Z},
 file_attached = {false},
 profile_id = {f27171f8-b200-3870-9028-dcb6a084733d},
 last_modified = {2017-12-15T21:38:14.465Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 private_publication = {true},
 abstract = {Copyright © 2017, AIDIC Servizi S.r.l. This work addresses the life cycle economic and environmental optimisation of a supply chain network considering both design and operational decisions under uncertainty. A general modelling framework is proposed that integrates the functional-unit-based life cycle optimisation methodology and the two-stage stochastic programming approach for sustainable supply chain optimisation under uncertainty. a stochastic mixed-integer linear fractional programming (SMILFP) model is developed to tackle multiple uncertainties regarding feedstock supply uncertainty and product demand uncertainty. To address the computational challenge of solving large-scale SMILFP problems, an efficient solution algorithm that takes advantage of the efficiency of parametric algorithm and the decomposition-based multi-cut L-shaped method is used. A case study based on a spatially explicit model for the county-level hydrocarbon biofuel supply chain is presented in Illinois to demonstrate the applicability of the proposed modelling and algorithmic framework.},
 bibtype = {book},
 author = {Gao, J. and You, F.}
}

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