Modeling framework and computational algorithm for hedging against uncertainty in sustainable supply chain design using functional-unit-based life cycle optimization

•Integrated modeling framework addressing the life cycle optimization of supply chain under uncertainty.•An efficient tailored global optimization algorithm for solving stochastic mixed-integer linear fractional programming problem.•Reduction of scenario number by adopting a sample average approxima...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Computers & chemical engineering Ročník 107; s. 221 - 236
Hlavní autoři: Gao, Jiyao, You, Fengqi
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 05.12.2017
Témata:
Biofuels
Life cycle optimization
Mixed-integer fractional programming
Sustainable supply chain
Uncertainty
Mixed-integer fractional programming
Life cycle optimization
Uncertainty
Biofuels
Sustainable supply chain
ISSN:0098-1354, 1873-4375
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:•Integrated modeling framework addressing the life cycle optimization of supply chain under uncertainty.•An efficient tailored global optimization algorithm for solving stochastic mixed-integer linear fractional programming problem.•Reduction of scenario number by adopting a sample average approximation approach.•A case study based on spatially explicit model for the county-level hydrocarbon biofuel supply chain in Illinois, USA. In this work, we address the life cycle economic and environmental optimization of a supply chain network considering both design and operational decisions under uncertainty. A modeling framework is proposed that integrates the functional-unit-based life cycle optimization methodology and the two-stage stochastic programming approach for sustainable supply chain optimization under uncertainty. We develop a stochastic mixed-integer linear fractional programming (SMILFP) model to tackle multiple uncertainties regarding feedstock supply and product demand. To address the computational challenge of solving the resulting large-scale SMILFP problems, an efficient solution algorithm is developed that takes advantage of the efficiency of parametric algorithm and the decomposition-based multi-cut L-shaped method. We present a case study based on a spatially explicit model for the optimal design and operations of a county-level hydrocarbon biofuel supply chain in Illinois to demonstrate the applicability of the proposed modeling framework and the efficiency of the solution algorithm.
ISSN:0098-1354
1873-4375
DOI:10.1016/j.compchemeng.2017.05.021