Page 353 - Biofuels for a More Sustainable Future
P. 353
Key issue, challenges, and status quo of models for biofuel supply chain design 309
Garcia-Flores, R., Wang, X.Z., Goltz, G.E., 2000. Agent-based information flow for process
industries’ supply chain Modelling. Comput. Chem. Eng. 24 (2–7), 1135–1141.
Gebreslassie, B.H., Yao, Y., You, F., 2012. Design under uncertainty of hydrocarbon bior-
efinery supply chains: multiobjective stochastic programming models, decomposition
algorithm, and a comparison between CVaR and downside risk. AICHE J. 58 (7),
2155–2179.
Giannakis, M., Louis, M., 2011. A multi-agent based framework for supply chain risk man-
agement. J. Purch. Supply Manag. 17 (1), 23–31.
Gjerdrum, J., Shah, N., Papageorgiou, L.G., 2001. A combined optimization and agent-
based approach to supply chain modelling and performance assessment. Prod. Plan. Con-
trol 12 (1), 81–88.
Gnansounou, E., Dauriat, A., Villegas, J., Panichelli, L., 2009. Life cycle assessment of bio-
fuels: energy and greenhouse gas balances. Bioresour. Technol. 100 (21), 4919–4930.
Gold, S., Seuring, S., 2011. Supply chain and logistics issues of bio-energy production.
J. Clean. Prod. 19 (1), 32–42.
Goldemberg, J., Coelho, S.T., Guardabassi, P., 2008. The sustainability of ethanol produc-
tion from sugarcane. Energy Policy 36 (6), 2086–2097.
Graham, R.G., Bergougnoum, M.A., Overend, R.P., 1984. Fast pyrolysis of biomass.
J. Anal. Appl. Pyrolysis 6, 95–135.
Gronalt, M., Rauch, P., 2007. Designing a regional forest fuel supply network. Biomass
Bioenergy 31 (6), 393–402.
Hajibabai, L., Ouyang, Y., 2013. Integrated planning of supply chain networks and multi-
modal transportation infrastructure expansion: model development and application to
the biofuel industry. Comput. Civ. Infrastruct. Eng. 28 (4), 247–259.
He-Lambert, L., English, B.C., Lambert, D.M., Shylo, O., Larson, J.A., Yu, T.E.,
Wilson, B., 2018. Determining a geographic high resolution supply chain network
for a large scale biofuel industry. Appl. Energy 218 (March), 266–281.
Hill, J., Nelson, E., Tilman, D., Polasky, S., Tiffany, D., 2006. Environmental, economic,
and energetic costs and benefits of biodiesel and ethanol biofuels. Proc. Natl. Acad. Sci.
103 (30), 11206–11210.
H€ohn, J., Lehtonen, E., Rasi, S., Rintala, J., 2014. A geographical information system (gis)
based methodology for determination of potential biomasses and sites for biogas plants in
southern Finland. Appl. Energy 2014 (113), 1–10.
Huang, Y., Chen, C.W., Fan, Y., 2010. Multistage optimization of the supply chains of bio-
fuels. Transport. Res. E-Log 46 (6), 820–830.
Huang, Y.E., Fan, Y., Chen, C.-W., 2014. An integrated biofuel supply chain to cope with
feedstock seasonality and uncertainty. Transp. Sci. 48 (4), 540–554.
Humbird, D., Davis, R., Tao, L., Kinchin, C., Hsu, D., Aden, A., Schoen, P., Lukas, J.,
Olthof, B., Worley, M., Sexton, D., 2017. Process Design and Economics for Biochem-
ical Conversion of Lignocellulosic Biomass to Ethanol: Dilute-Acid Pretreatment and
Enzymatic Hydrolysis of Corn Stover. National Renewable Energy Lab (NREL),
Golden, CO (United States).
Iakovou, E., Karagiannidis, A., Vlachos, D., Toka, A., Malamakis, A., 2010. Waste biomass-to-
energy supply chain management: a critical synthesis. Waste Manag. 30 (10), 1860–1870.
IEA, 2017. Energy Access Outlook. p. 2017.
Imhof, J.P., 1961. Computing the distribution of quadratic forms in normal variables.
Biometrika 48 (3/4), 419–426.
International Energy Agency (IEA), 2017. Roadmap Delivering Sustainable Bioenergy. IEA
Publications, Paris, France.
Inuiguchi, M., Ramik, J., 2000. Possibilistic linear programming: a brief review of fuzzy
mathematical programming and a comparison with stochastic programming in portfolio
selection problem. Fuzzy Sets Syst. 111, 3–28.