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Home All issues Volume 73 (2018) Oil & Gas Science and Technology - Rev. IFP Energies nouvelles, 73 (2018) 66 References
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Issue
Oil & Gas Science and Technology - Rev. IFP Energies nouvelles
Volume 73, 2018
Article Number 66
Number of page(s) 14
DOI https://doi.org/10.2516/ogst/2018056
Published online 05 December 2018
  • Bauquis P.R. (2001) A reappraisal of energy supply and demand in 2050, Oil Gas Sci. Technol. - Rev. IFP Energies nouvelles 56, 4, 389–402. [CrossRef] [Google Scholar]
  • Sahebi H., Nickel S., Ashayeri J. (2014) Strategic and tactical mathematical programming models within the crude oil supply chain context – A review, Comput. Chem. Eng. 68, 56–77. [Google Scholar]
  • La Londe B.J., Masters J.M. (1994) Emerging logistics strategies: Blueprints for the next century, Int. J. Phys. Distrib. Logist. Manag. 24, 7, 35–47. [CrossRef] [Google Scholar]
  • Mentzer J.T., DeWitt W., Keebler J.S., Min S.H., Nix N.W., Smith C.D., Zacharia Z.G. (2001) Defining supply chain management, JBL 22, 2, 1–25. [Google Scholar]
  • Neiro S.M., Pinto J.M. (2004) A general modeling framework for the operational planning of petroleum supply chains, Comput. Chem. Eng. 28, 6, 871–896. [Google Scholar]
  • MirHassani S. (2008) An operational planning model for petroleum products logistics under uncertainty, Appl. Math. Comput. 196, 2, 744–751. [Google Scholar]
  • Oliveira F., Gupta V., Hamacher S., Grossmann I.E. (2013) A Lagrangean decomposition approach for oil supply chain investment planning under uncertainty with risk considerations, Comput. Chem. Eng. 50, 184–195. [Google Scholar]
  • Oliveira F., Grossmann I.E., Hamacher S. (2014) Accelerating Benders stochastic decomposition for the optimization under uncertainty of the petroleum product supply chain, Comput. Oper. Res. 49, 47–58. [Google Scholar]
  • Fernandes L.J., Relvas S., Barbosa-Póvoa A.P. (2013) Strategic network design of downstream petroleum supply chains: single versus multi-entity participation, Chem. Eng. Res. Des. 91, 8, 1557–1587. [Google Scholar]
  • Chen J., Lu J., Qi S. (2010) Transportation network optimization of import crude oil in China based on minimum logistics cost, Paper presented at the Emergency Management and Management Sciences (ICEMMS), 2010 IEEE International Conference on Emergency Management. [Google Scholar]
  • Gupta V., Grossmann I.E. (2012) An efficient multi-period MINLP model for optimal planning of offshore oil and gas field infrastructure, Ind. Eng. Chem. Res. 51, 19, 6823–6840. [Google Scholar]
  • Sahebi H., Nickel S. (2014) Offshore oil network design with transportation alternatives, Eur. J. Ind. Eng. 8, 6, 739–761. [CrossRef] [Google Scholar]
  • Elsholkami M., Elkamel A. (2014) Design and operation of a sulfur supply chain for sour gas processing and bitumen upgrading operations, Energy Fuels 28, 11, 7252–7267. [Google Scholar]
  • Casault S., Groen A.J., Linton J.D. (2016) Linking the value assessment of oil and gas firms to ambidexterity theory using a mixture of normal distributions, Oil Gas Sci. Technol. - Rev. IFP Energies nouvelles 71, 3, 36. [CrossRef] [Google Scholar]
  • Chufu L., Xiaorong H., Bingzhen C., Qiang X., Chaowei L. (2008) A hybrid programming model for optimal production planning under demand uncertainty in refinery, Chin. J. Chem. Eng. 16, 2, 241–246. [CrossRef] [Google Scholar]
  • Al-Qahtani K., Elkamel A. (2010) Robust planning of multisite refinery networks: Optimization under uncertainty, Comput. Chem. Eng. 34, 6, 985–995. [Google Scholar]
  • Tong K., Feng Y., Rong G. (2011) Planning under demand and yield uncertainties in an oil supply chain, Ind. Eng. Chem. Res. 51, 2, 814–834. [Google Scholar]
  • Leiras A., Elkamel A., Hamacher S. (2010) Strategic planning of integrated multi-refinery networks: a robust optimization approach based on the degree of conservatism, Ind. Eng. Chem. Res. 49, 20, 9970–9977. [Google Scholar]
  • Oliveira F., Hamacher S. (2012) Optimization of the petroleum product supply chain under uncertainty: A case study in northern Brazil, Ind. Eng. Chem. Res. 51, 11, 4279–4287. [Google Scholar]
  • Ribas G.P., Hamacher S., Street A. (2010) Optimization under uncertainty of the integrated oil supply chain using stochastic and robust programming, Int. Trans. Oper. Res. 17, 6, 777–796. [Google Scholar]
  • Al-Othman W.B., Lababidi H.M., Alatiqi I.M., Al-Shayji K. (2008) Supply chain optimization of petroleum organization under uncertainty in market demands and prices, Eur. J. Oper. Res. 189, 3, 822–840. [Google Scholar]
  • Andersson H., Christiansen M., Fagerholt K. (2010) Transportation planning and inventory management in the LNG supply chain. Energy, natural resources and environmental economics, Springer, Berlin Heidelberg, pp. 427–439. [CrossRef] [Google Scholar]
  • Grønhaug R., Christiansen M. (2009) Supply chain optimization for the liquefied natural gas business, Innovations in distribution logistics, Springer, Berlin Heidelberg, pp. 195–218. [CrossRef] [Google Scholar]
  • Özelkan E.C., D’Ambrosio A., Teng S.G. (2008) Optimizing liquefied natural gas terminal design for effective supply-chain operations, Int. J. Prod. Econ. 111, 2, 529–542. [Google Scholar]
  • Pishvaee M.S., Razmi J., Torabi S.A. (2012) Robust possibilistic programming for socially responsible supply chain network design: A new approach, Fuzzy Sets Syst. 206, 1–20. [CrossRef] [Google Scholar]
  • Yu C.-S., Li H.-L. (2000) A robust optimization model for stochastic logistic problems, Int. J. Prod. Econ. 64, 1, 385–397. [Google Scholar]
  • Mulvey J.M., Vanderbei R.J., Zenios S.A. (1995) Robust optimization of large-scale systems, Oper. Res. 43, 2, 264–281. [Google Scholar]
  • Bertsimas D., Sim M. (2004) The price of robustness, Oper. Res. 52, 1, 35–53. [Google Scholar]
  • Ben-Tal A., Nemirovski A. (1998) Robust convex optimization, Math. Oper. Res. 23, 4, 769–805. [CrossRef] [MathSciNet] [Google Scholar]
  • Zekri A., Jerbi K. K. (2002) Economic evaluation of enhanced oil recovery, Oil Gas Sci. Technol. - Rev. IFP Energies nouvelles 57, 3, 259–267. [CrossRef] [Google Scholar]

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