Open Access
Numéro
Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles
Volume 76, 2021
Numéro d'article 47
Nombre de pages 23
DOI https://doi.org/10.2516/ogst/2021021
Publié en ligne 28 juin 2021
  • Ghelichi Z., Saidi-Mehrabad M., Pishvaee M.S. (2018) A stochastic programming approach toward optimal design and planning of an integrated green biodiesel supply chain network under uncertainty: A case study, Energy 156, 661–687. [Google Scholar]
  • Ward H., Radebach A., Vierhaus I., Fügenschuh A., Steckel J. (2017) Reducing global CO2 emissions with the technologies we have, Resour. Energy Econ. 49, 201–217. [Google Scholar]
  • Khan T.M.Y., Atabani A.E., Badruddin I.A., Badarudin A., Khayoon M.S., Triwahyono S. (2014) Recent scenario and technologies to utilize non-edible oils for biodiesel production, Renewable Sustainable Energy Rev. 37, 840–851. [Google Scholar]
  • Debnath D. (2019) Chapter 13 – Advanced biofuels: Supply chain management, in D. Debnath, S.C. Babu (eds), Biofuels, Bioenergy and Food Security, Academic Press, pp. 231–246. [Google Scholar]
  • Gnansounou E. (2019) Chapter 4 – Economic assessment of biofuels, in A. Pandey, et al. (eds), Biofuels: Alternative feedstocks and conversion processes for the production of liquid and gaseous biofuels (2nd edn.), Academic Press, pp. 95–121. [Google Scholar]
  • Sharma B., Lngalls R.G., Jones C.L., Khanchi A. (2013) Biomass supply chain design and analysis: Basis, overview, modeling, challenges, and future, Renewable Sustainable Energy Rev. 24, 608–627. [Google Scholar]
  • Peri M., Baldi L. (2013) The effect of biofuel policies on feedstock market: Empirical evidence for rapeseed oil prices in EU, Resour. Energy Econ. 35, 1, 18–37. [Google Scholar]
  • Achten W.M.J., Verchot L., Franken Y.J., Mathijs E., Singh V.P., Aerts R., Muys B. (2008) Jatropha bio-diesel production and use, Biomass Bioenergy 32, 1063–1084. [Google Scholar]
  • Downing M., Eaton L.M., Graham R.L., Langholtz M.H., Perlack R.D., Turhollow A.F. Jr, Stokes B., Brandt C.C. (2011) US billion-ton update: Biomass supply for a bioenergy and bioproducts industry. Technical Report, Oak Ridge National Laboratory (ORNL). [Google Scholar]
  • Huber G.W., Corma A. (2007) Synergies between bio- and oil refineries for the production of fuels from biomass, Angew. Chem. Int. Ed. 46, 38, 7184–7201. [Google Scholar]
  • Mahjoub N., Sahebi H., Mazdeh M., Teymouri A. (2020) Optimal design of the second and third generation biofuel supply network by a multi-objective model, J. Clean. Prod. 256, 120355. [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]
  • Fernandes L.O.J., Relvas S., Barbosa-Póvoa A.P. (2014) Collaborative design and tactical planning of downstream petroleum supply chains, Ind. Eng. Chem. Res. 53, 44, 17155–17181. [Google Scholar]
  • Guajardo M., Kylinger M., Rönnqvist M. (2013) Speciality oils supply chain optimization: From a decoupled to an integrated planning approach, Eur. J. Oper. Res. 229, 2, 540–551. [Google Scholar]
  • Fiorencio L., Oliveira F., Nunes P., Hamacher S. (2015) Investment planning in the petroleum downstream infrastructure, Int. Trans. Oper. Res. 22, 2, 339–362. [Google Scholar]
  • Kazemi Y., Szmerekovsky J. (2015) Modeling downstream petroleum supply chain: The importance of multi-mode transportation to strategic planning, Transp. Res E Logist. Transp. Rev. 83, 111–125. [Google Scholar]
  • Ghezavati V.R., Ghaffarpour M., Salimian M. (2015) A hierarchical approach for designing the downstream segment for a supply chain of petroleum production systems, J. Ind. Syst. Eng. 8, 4, 1–17. [Google Scholar]
  • Öztürkoğlu Ö., Lawal O. (2016) The integrated network model of pipeline, sea and road distribution of petroleum product, Int. J. Optimiz. Contr. Theor. Appl. 6, 2, 151–165. [Google Scholar]
  • Ghaithan A.M., Attia A., Duffuaa S.O. (2017) Multi-objective optimization model for a downstream oil and gas supply chain, Appl. Math. Model. 52, 689–708. [Google Scholar]
  • Lima C., Relvas S., Barbosa-Póvoa A. (2018) Stochastic programming approach for the optimal tactical planning of the downstream oil supply chain, Comput. Chem. Eng. 108, 314–336. [Google Scholar]
  • Wang B., Liang Y., Zheng T., Yuan M., Zhang H. (2019) Optimisation of a downstream oil supply chain with new pipeline route planning, Chem. Eng. Res. Des. 145, 300–313. [Google Scholar]
  • Leiras A., Ribas G., Hamacher S., Elkamel A. (2013) Tactical and operational planning of multirefinery networks under uncertainty: An iterative integration approach, Ind. Eng. Chem. Res. 52, 25, 8507–8517. [Google Scholar]
  • Gamari A., Sahebi H. (2017) The stochastic lot-sizing problem with lost sales: A chemical-petrochemical case study, J. Manuf. Syst. 44, 53–64. [Google Scholar]
  • Nasab N.M., Amin-Naseri M. (2016) Designing an integrated model for a multi-period, multi-echelon and multi-product petroleum supply chain, Energy 114, 708–733. [Google Scholar]
  • Jabbarzadeh A., Pishvaee M., Papi A. (2016) A multi-period fuzzy mathematical programming model for crude oil supply chain network design considering budget and equipment limitations, J. Ind. Syst. Eng. 9, special issue on supply chain, 88–107. [Google Scholar]
  • Farahani M., Rahmani D. (2017) Production and distribution planning in petroleum supply chains regarding the impacts of gas injection and swap, Energy 141, 991–1003. [Google Scholar]
  • Azadeh A., Shafiee F., Yazdanparast R., Heydari J., Mohammadi Fathabad A. (2017) Evolutionary multi-objective optimization of environmental indicators of integrated crude oil supply chain under uncertainty, J. Clean. Prod. 152, 295–311. [Google Scholar]
  • Azadeh A., Shafiee F., Yazdanparast R., Heydari J., Keshvarparast A. (2017) Optimum integrated design of crude oil supply chain by a unique mixed integer nonlinear programming model, Ind. Eng. Chem. Res. 56, 19, 5734–5746. [Google Scholar]
  • Zhou X., Zhang H., Xin S., Yan Y., Long Y., Yuan M., Liang Y. (2020) Future scenario of China’s downstream oil supply chain: Low carbon-oriented optimization for the design of planned multi-product pipelines, J. Clean. Prod. 244, 118866. [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]
  • Gupta V., Grossmann I.E. (2014) Multistage stochastic programming approach for offshore oilfield infrastructure planning under production sharing agreements and endogenous uncertainties, J. Petrol. Sci. Eng. 124, 180–197. [Google Scholar]
  • Beiranvand H., Ghazanfari M., Sahebi H., Pishvaee M.S. (2018) A robust crude oil supply chain design under uncertain demand and market price: A case study, Oil Gas Sci. Technol. - Rev. IFP Energies nouvelles 73, 66. [Google Scholar]
  • Babazadeh R. (2017) Optimal design and planning of biodiesel supply chain considering non-edible feedstock, Renewable Sustainable Energy Rev. 75, 1089–1100. [Google Scholar]
  • Babazadeh R., Razmi J., Pishvaee M.S., Rabbani M. (2017) A sustainable second-generation biodiesel supply chain network design problem under risk, Omega 66, 258–277. [Google Scholar]
  • Ezzati F., Babazadeh R., Donyavi A. (2018) Optimization of multimodal, multi-period and complex biodiesel supply chain systems: Case study, Renew. Energy Focus 26, 81–92. [Google Scholar]
  • Kheybari S., Kazemi M., Rezaei J. (2019) Bioethanol facility location selection using best-worst method, Appl. Energy 242, 612–623. [Google Scholar]
  • Kang S., Heo S., Realff M., Lee J. (2020) Three-stage design of high-resolution microalgae-based biofuel supply chain using geographic information system, Appl. Energy 265, 114773. [Google Scholar]
  • Lin T., Rodriguez L., Shastri Y., Hansen A., Ting K.C. (2014) Integrated strategic and tactical biomass–biofuel supply chain optimization, Bioresour. Technol. 156, 256–266. [Google Scholar]
  • Xie F., Huang Y., Eksioglu S. (2014) Integrating multimodal transport into cellulosic biofuel supply chain design under feedstock seasonality with a case study based on California, Bioresour. Technol. 152, 15–23. [Google Scholar]
  • Santibañez-Aguilar J.E., Revera-Toledo M., Flores-Tlacuahuac A., Ponce-Ortega J.M. (2015) A mixed-integer dynamic optimization approach for the optimal planning of distributed biorefineries, Compute. Chem. Eng. 80, 37–62. [Google Scholar]
  • Mousavi Ahranjani P., Ghaderi S.F., Azadeh A., Babazadeh R. (2018) Hybrid multiobjective robust possibilistic programming approach to a sustainable bioethanol supply chain network design, Ind. Eng. Chem. Res. 57, 44, 15066–15083. [Google Scholar]
  • Fattahi M., Govindan K. (2018) A multi-stage stochastic program for the sustainable design of biofuel supply chain networks under biomass supply uncertainty and disruption risk: A real-life case study, Transp. Res E Logist. Transp. Rev. 118, 534–567. [Google Scholar]
  • Ghani N.M.A.M.A., Vogiatzis C., Szmerekovsky J. (2018) Biomass feedstock supply chain network design with biomass conversion incentives, Energy Policy 116, 39–49. [Google Scholar]
  • Azadeh A., Arani H.V., Dashti H. (2014) A stochastic programming approach towards optimization of biofuel supply chain, Energy 76, 513–525. [Google Scholar]
  • Zhang Y., Jiang Y. (2017) Robust optimization on sustainable biodiesel supply chain produced from waste cooking oil under price uncertainty, Waste Manag. 60, 329–339. [Google Scholar]
  • Mohseni S., Pishvaee M.S., Sahebi H. (2016) Robust design and planning of microalgae biomass-to-biodiesel supply chain: A case study in Iran, Energy 111, 736–755. [Google Scholar]
  • Gilani H., Sahebi H., Oliveira F. (2020) Sustainable sugarcane-to-bioethanol supply chain network design: A robust possibilistic programming model, Appl. Energy 278, 115653. [Google Scholar]
  • Bairamzadeh S., Saidi-Mehrabad M., Pishvaee M.S. (2018) Modelling different types of uncertainty in biofuel supply network design and planning: A robust optimization approach, Renew. Energy 116, 500–517. [Google Scholar]
  • Shavazipour B., Stray J., Stewart T.J. (2020) Sustainable planning in sugar-bioethanol supply chain under deep uncertainty: A case study of South African sugarcane industry, Comput. Chem. Eng. 143, 107091. [Google Scholar]
  • Ghaderi H., Moini A., Pishvaee M.S. (2018) A multi-objective robust possibilistic programming approach to sustainable switchgrass-based bioethanol supply chain network design, J. Clean. Prod. 179, 368–406. [Google Scholar]
  • Babazadeh R., Ghaderi H., Pishvaee M.S. (2019) A benders-local branching algorithm for second-generation biodiesel supply chain network design under epistemic uncertainty, Comput. Chem. Eng. 124, 364–380. [Google Scholar]
  • Razm S., Nickel S., Saidi-mehrabad M., Sahebi H. (2019) A global bioenergy supply network redesign through integrating transfer pricing under uncertain condition, J. Clean. Prod. 208, 1081–1095. [Google Scholar]
  • Tong K., Gong J., Yue D., You F. (2013) Stochastic programming approach to optimal design and operations of integrated hydrocarbon biofuel and petroleum supply chains, ACS Sustain. Chem. Eng. 2, 1, 49–61. [Google Scholar]
  • Tong K., Gleeson M.J., Rong G., You F. (2014) Optimal design of advanced drop-in hydrocarbon biofuel supply chain integrating with existing petroleum refineries under uncertainty, Biomass Bioenergy 60, 108–120. [Google Scholar]
  • Tong K., You F., Rong G. (2014) Robust design and operations of hydrocarbon biofuel supply chain integrating with existing petroleum refineries considering unit cost objective, Comput. Chem. Eng. 68, 128–139. [Google Scholar]
  • Wang B., Gebreslassie B.H., You F. (2013) Sustainable design and synthesis of hydrocarbon biorefinery via gasification pathway: Integrated life cycle assessment and technoeconomic analysis with multiobjective superstructure optimization, Comput. Chem. Eng. 52, 55–76. [Google Scholar]
  • Gebreslassie B.H., Yao Y., You F. (2012) Design under uncertainty of hydrocarbon biorefinery supply chains: Multiobjective stochastic programming models, decomposition algorithm, and a Comparison between CVaR and downside risk, AIChE J. 58, 7, 2155–2179. [Google Scholar]
  • Adhitya A., Srinivasan R., Karimi I.A. (2007) Heuristic rescheduling of crude oil operations to manage abnormal supply chain events, AIChE J. 53, 2, 397–422. [Google Scholar]
  • Babazadeh R., Razmi J., Rabbani M., Pishvaee M.S. (2017) An integrated data envelopment analysis – mathematical programming approach to strategic biodiesel supply chain network design problem, J. Clean. Prod. 147, 694–707. [Google Scholar]

Les statistiques affichées correspondent au cumul d'une part des vues des résumés de l'article et d'autre part des vues et téléchargements de l'article plein-texte (PDF, Full-HTML, ePub... selon les formats disponibles) sur la platefome Vision4Press.

Les statistiques sont disponibles avec un délai de 48 à 96 heures et sont mises à jour quotidiennement en semaine.

Le chargement des statistiques peut être long.