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Sustainable sugarcane-to-bioethanol supply chain network design: A robust possibilistic programming model

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  • Gilani, H.
  • Sahebi, H.
  • Oliveira, Fabricio

Abstract

Issues such as high fossil-fuel price fluctuations, considerable air pollution, and the realization that fossil sources are not unlimited threaten the global economy. Hence, researchers have been attracted by biomass, especially sugarcane, as a source of renewable energy to produce bioethanol and other biofuels. This research proposes a three-phase robust supply chain network design optimization model to produce bioethanol from sugarcane. Fuzzy integrated data envelopment analysis method is employed to select suitable cultivation lands as supply potential points. The model considers sustainability in its implementation so that the objectives are to maximize the profit, minimize environmental effects, and maximize social performance. Since some parameters are naturally uncertain, a robust possibilistic programming model is proposed, considering the possibility of transportation disruptions. The model performance has been illustrated through a case study in Iran. Finally, the results of the mathematical model have shown that the recommended supply chain design is justifiable. Practical strategic insights include recommendations for locating refineries in seven provinces (Gilan, Kermanshah, Hamedan, Semnan, Khorasan Razavi, Khouzestan, Kihkooliye). These results have been validated using a simulation-based approach, which demonstrates that the proposed model recommends decisions that more suitable than the deterministic model in terms of average and standard deviation of objective values.

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  • Gilani, H. & Sahebi, H. & Oliveira, Fabricio, 2020. "Sustainable sugarcane-to-bioethanol supply chain network design: A robust possibilistic programming model," Applied Energy, Elsevier, vol. 278(C).
    • Handle: RePEc:eee:appene:v:278:y:2020:i:c:s030626192031151x
    DOI: 10.1016/j.apenergy.2020.115653
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    2. Kwon, Oseok & Han, Jeehoon, 2021. "Waste-to-bioethanol supply chain network: A deterministic model," Applied Energy, Elsevier, vol. 300(C).
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    4. Hossein Savoji & Seyed Meysam Mousavi & Jurgita Antucheviciene & Miroslavas Pavlovskis, 2022. "A Robust Possibilistic Bi-Objective Mixed Integer Model for Green Biofuel Supply Chain Design under Uncertain Conditions," Sustainability, MDPI, vol. 14(20), pages 1-16, October.
    5. Islam Hassanin & Matjaz Knez, 2022. "Managing Supply Chain Activities in the Field of Energy Production Focusing on Renewables," Sustainability, MDPI, vol. 14(12), pages 1-33, June.
    6. Gilani, Hani & Sahebi, Hadi, 2022. "A data-driven robust optimization model by cutting hyperplanes on vaccine access uncertainty in COVID-19 vaccine supply chain," Omega, Elsevier, vol. 110(C).
    7. Alireza Goli & Ali Ala & Seyedali Mirjalili, 2023. "A robust possibilistic programming framework for designing an organ transplant supply chain under uncertainty," Annals of Operations Research, Springer, vol. 328(1), pages 493-530, September.
    8. Elsagan, Zahwa A. & Ali, Rehab M. & El-Naggar, Mohamed A. & El-Ashtoukhy, E.-S.Z. & AbdElhafez, Sara E., 2023. "New perspectives for maximizing sustainable bioethanol production from corn stover," Renewable Energy, Elsevier, vol. 209(C), pages 608-618.
    9. Mondal, Arijit & Giri, Binoy Krishna & Roy, Sankar Kumar, 2023. "An integrated sustainable bio-fuel and bio-energy supply chain: A novel approach based on DEMATEL and fuzzy-random robust flexible programming with Me measure," Applied Energy, Elsevier, vol. 343(C).
    10. Thamine G. Rodrigues & Ricardo L. Machado, 2023. "Life Cycle Assessment of the Sugarcane Supply Chain in the Brazilian Midwest Region," Sustainability, MDPI, vol. 16(1), pages 1-18, December.
    11. Cabrera-Jiménez, Richard & Mateo-Sanz, Josep M. & Gavaldà, Jordi & Jiménez, Laureano & Pozo, Carlos, 2022. "Comparing biofuels through the lens of sustainability: A data envelopment analysis approach," Applied Energy, Elsevier, vol. 307(C).

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