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Multiobjective economic and environmental optimization of global crude oil purchase and sale planning with noncooperative stakeholders

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  • Nicoletti, Jack
  • You, Fengqi

Abstract

The petrochemical supply chain is a worldwide undertaking, where final products will often travel thousands of miles from oil well to gas station pump. Within the crude oil supply chain, various entities compete and attempt to maximize their profits by exploiting the demands and needs of the other companies within the supply chain. Each company or player in the crude oil industry has its own objective, and it will compete against other players trying to pursue their respective objectives. Due to the non-cooperative structure of the crude oil supply chain, the decisions that maximize profit often do not coincide with decisions that minimize environmental impact, as a reduction in environmental impact usually correlates with a reduction in profit. In this work, the crude oil supply chain from oil well to refinery is modelled as a mixed-integer bilevel linear program that accounts for conflicting objectives and interactions between different stakeholders. The composition, pricing, transportation distances, and environmental impacts of the different crude oils are taken into consideration in the model. In the bilevel problem, the crude oil producers aim to maximize their own profits from the sale of their crude oil, while the crude oil refiner has the dual objectives of both maximizing the profit made from the sale of distilled products to the market and minimizing the life cycle environmental impact of the refinery products, which is determined by the type of crude oil purchased by the refinery. The resulting model is then applied to two case studies, both based on a U.S. refinery purchasing oil from various crude oil-producing countries. Both case studies produce a set of pareto-optimal decisions for the refiner that display the inherent trade-offs between minimizing “cradle-to-gate” environmental impact and maximizing profit. At the trade-off point in the first case, a 4.4% decrease in profit leads to a 3.0% decrease in the kilograms of CO2 per megajoule of energy produced. Meanwhile, the trade-off point selected in the second case displays a 7.5% reduction in the total environmental impact while decreasing total profit by only 5.9%. Furthermore, the refiner’s profit at the trade-off point in the second case is $2.148 M, which is situated between the worst-case deterministic profit of $1.558 M and the best-case deterministic profit of $2.652 M.

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  • Nicoletti, Jack & You, Fengqi, 2020. "Multiobjective economic and environmental optimization of global crude oil purchase and sale planning with noncooperative stakeholders," Applied Energy, Elsevier, vol. 259(C).
    • Handle: RePEc:eee:appene:v:259:y:2020:i:c:s0306261919319099
    DOI: 10.1016/j.apenergy.2019.114222
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    References listed on IDEAS

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    1. Nicoletti, Jack & Ning, Chao & You, Fengqi, 2019. "Incorporating agricultural waste-to-energy pathways into biomass product and process network through data-driven nonlinear adaptive robust optimization," Energy, Elsevier, vol. 180(C), pages 556-571.
    2. Gong, Hong-Fei & Chen, Zhong-Sheng & Zhu, Qun-Xiong & He, Yan-Lin, 2017. "A Monte Carlo and PSO based virtual sample generation method for enhancing the energy prediction and energy optimization on small data problem: An empirical study of petrochemical industries," Applied Energy, Elsevier, vol. 197(C), pages 405-415.
    3. Al-Othman, Wafa B.E. & Lababidi, Haitham M.S. & Alatiqi, Imad M. & Al-Shayji, Khawla, 2008. "Supply chain optimization of petroleum organization under uncertainty in market demands and prices," European Journal of Operational Research, Elsevier, vol. 189(3), pages 822-840, September.
    4. Benoît Colson & Patrice Marcotte & Gilles Savard, 2007. "An overview of bilevel optimization," Annals of Operations Research, Springer, vol. 153(1), pages 235-256, September.
    5. Fisk, C. S., 1984. "Game theory and transportation systems modelling," Transportation Research Part B: Methodological, Elsevier, vol. 18(4-5), pages 301-313.
    6. Ning, Chao & You, Fengqi, 2019. "Data-driven Wasserstein distributionally robust optimization for biomass with agricultural waste-to-energy network design under uncertainty," Applied Energy, Elsevier, vol. 255(C).
    7. Rahman, Md. Mustafizur & Canter, Christina & Kumar, Amit, 2015. "Well-to-wheel life cycle assessment of transportation fuels derived from different North American conventional crudes," Applied Energy, Elsevier, vol. 156(C), pages 159-173.
    8. Wang, Xiao & van ’t Veld, Klaas & Marcy, Peter & Huzurbazar, Snehalata & Alvarado, Vladimir, 2018. "Economic co-optimization of oil recovery and CO2 sequestration," Applied Energy, Elsevier, vol. 222(C), pages 132-147.
    9. Fred Glover, 1975. "Improved Linear Integer Programming Formulations of Nonlinear Integer Problems," Management Science, INFORMS, vol. 22(4), pages 455-460, December.
    10. Panda, Debashish & Ramteke, Manojkumar, 2019. "Preventive crude oil scheduling under demand uncertainty using structure adapted genetic algorithm," Applied Energy, Elsevier, vol. 235(C), pages 68-82.
    11. Wang, Minggang & Tian, Lixin & Du, Ruijin, 2016. "Research on the interaction patterns among the global crude oil import dependency countries: A complex network approach," Applied Energy, Elsevier, vol. 180(C), pages 779-791.
    12. Lee, Sangick & Choi, Inhwan & Chang, Daejun, 2013. "Multi-objective optimization of VOC recovery and reuse in crude oil loading," Applied Energy, Elsevier, vol. 108(C), pages 439-447.
    13. Dajun Yue & Jiyao Gao & Bo Zeng & Fengqi You, 2019. "A projection-based reformulation and decomposition algorithm for global optimization of a class of mixed integer bilevel linear programs," Journal of Global Optimization, Springer, vol. 73(1), pages 27-57, January.
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