Design under uncertainty of hydrocarbon biorefinery supply chains: Multiobjective stochastic programming models, decomposition algorithm, and a Comparison between CVaR and downside risk

A bicriterion, multiperiod, stochastic mixed‐integer linear programming model to address the optimal design of hydrocarbon biorefinery supply chains under supply and demand uncertainties is presented. The model accounts for multiple conversion technologies, feedstock seasonality and fluctuation, geo...

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Veröffentlicht in:AIChE journal Jg. 58; H. 7; S. 2155 - 2179
Hauptverfasser: Gebreslassie, Berhane H., Yao, Yuan, You, Fengqi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Hoboken Wiley Subscription Services, Inc., A Wiley Company 01.07.2012
Wiley
Schlagworte:
Applications of mathematics to chemical engineering. Modeling. Simulation. Optimization
Applied sciences
biomass to liquid
Chemical engineering
Economics. Management. Design assessment
Exact sciences and technology
multiobjective optimization
risk management
Safety
stochastic programming
supply chain
uncertainty
Uncertainty
biomass to liquid
supply chain
Decomposition method
Financial management
Linear programming
Mixed integer programming
Biomass
Algorithm
Modeling
Optimization
Optimal design
Stochastic programming
multiobjective optimization
Production planning
Seasonal variation
Risk management
Mathematical programming
Logistics
ISSN:0001-1541, 1547-5905
Online-Zugang:Volltext
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Zusammenfassung:A bicriterion, multiperiod, stochastic mixed‐integer linear programming model to address the optimal design of hydrocarbon biorefinery supply chains under supply and demand uncertainties is presented. The model accounts for multiple conversion technologies, feedstock seasonality and fluctuation, geographical diversity, biomass degradation, demand variation, government incentives, and risk management. The objective is simultaneous minimization of the expected annualized cost and the financial risk. The latter criterion is measured by conditional value‐at‐risk and downside risk. The model simultaneously determines the optimal network design, technology selection, capital investment, production planning, and logistics management decisions. Multicut L‐shaped method is implemented to circumvent the computational burden of solving large scale problems. The proposed modeling framework and algorithm are illustrated through four case studies of hydrocarbon biorefinery supply chain for the State of Illinois. Comparisons between the deterministic and stochastic solutions, the different risk metrics, and two decomposition methods are discussed. The computational results show the effectiveness of the proposed strategy for optimal design of hydrocarbon biorefinery supply chain under the presence of uncertainties. © 2012 American Institute of Chemical Engineers AIChE J, 2012
Bibliographie:ArticleID:AIC13844
U.S. Department of Energy - No. DE-AC02-06CH11357
istex:52CF27514264F3CC3B920C983B40ED67153C69FE
ark:/67375/WNG-60KPS8T3-6
Initiative for Sustainability and Energy (ISEN) at Northwestern University
ISSN:0001-1541
1547-5905
DOI:10.1002/aic.13844