IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v259y2020ics0306261919319099.html
   My bibliography  Save this article

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

Author

Listed:
  • 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.

Suggested Citation

  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261919319099
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2019.114222?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    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. 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.
    5. 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.
    6. Fred Glover, 1975. "Improved Linear Integer Programming Formulations of Nonlinear Integer Problems," Management Science, INFORMS, vol. 22(4), pages 455-460, December.
    7. Benoît Colson & Patrice Marcotte & Gilles Savard, 2007. "An overview of bilevel optimization," Annals of Operations Research, Springer, vol. 153(1), pages 235-256, September.
    8. 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.
    9. 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.
    10. Fisk, C. S., 1984. "Game theory and transportation systems modelling," Transportation Research Part B: Methodological, Elsevier, vol. 18(4-5), pages 301-313.
    11. 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.
    12. 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).
    13. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Popkova, Elena G. & Sergi, Bruno S., 2024. "Energy infrastructure: Investment, sustainability and AI," Resources Policy, Elsevier, vol. 91(C).
    2. Elaheh Jafarnejad & Ahmad Makui & Ashkan Hafezalkotob & Amir Aghsami, 2024. "Governance intervention policies in the production competition of biofuels and fossil fuels: a pathway to sustainable development," Operations Management Research, Springer, vol. 17(2), pages 660-682, June.
    3. Saad Balhasan & Mohammed Alnahhal & Shahrul Shawan & Bashir Salah & Waqas Saleem & Mosab I. Tabash, 2022. "Optimization of Exploration and Production Sharing Agreements Using the Maxi-Min and Nash Solutions," Energies, MDPI, vol. 15(23), pages 1-19, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhao, Ning & You, Fengqi, 2019. "Dairy waste-to-energy incentive policy design using Stackelberg-game-based modeling and optimization," Applied Energy, Elsevier, vol. 254(C).
    2. Li, Tao & Wan, Yan, 2019. "Estimating the geographic distribution of originating air travel demand using a bi-level optimization model," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 131(C), pages 267-291.
    3. Miltiadis D. Lytras & Kwok Tai Chui, 2019. "The Recent Development of Artificial Intelligence for Smart and Sustainable Energy Systems and Applications," Energies, MDPI, vol. 12(16), pages 1-7, August.
    4. Behdad Beheshti & Oleg A. Prokopyev & Eduardo L. Pasiliao, 2016. "Exact solution approaches for bilevel assignment problems," Computational Optimization and Applications, Springer, vol. 64(1), pages 215-242, May.
    5. Xu, Xiao & Hu, Weihao & Du, Yuefang & Liu, Wen & Liu, Zhou & Huang, Qi & Chen, Zhe, 2020. "Robust chance-constrained gas management for a standalone gas supply system based on wind energy," Energy, Elsevier, vol. 212(C).
    6. Joaquim Dias Garcia & Guilherme Bodin & Alexandre Street, 2024. "BilevelJuMP.jl: Modeling and Solving Bilevel Optimization Problems in Julia," INFORMS Journal on Computing, INFORMS, vol. 36(2), pages 327-335, March.
    7. David Palma-Heredia & Manel Poch & Miquel À. Cugueró-Escofet, 2020. "Implementation of a Decision Support System for Sewage Sludge Management," Sustainability, MDPI, vol. 12(21), pages 1-18, October.
    8. M. Hosein Zare & Juan S. Borrero & Bo Zeng & Oleg A. Prokopyev, 2019. "A note on linearized reformulations for a class of bilevel linear integer problems," Annals of Operations Research, Springer, vol. 272(1), pages 99-117, January.
    9. Rahman Khorramfar & Osman Y. Özaltın & Karl G. Kempf & Reha Uzsoy, 2022. "Managing Product Transitions: A Bilevel Programming Approach," INFORMS Journal on Computing, INFORMS, vol. 34(5), pages 2828-2844, September.
    10. Junlong Zhang & Osman Y. Özaltın, 2021. "Bilevel Integer Programs with Stochastic Right-Hand Sides," INFORMS Journal on Computing, INFORMS, vol. 33(4), pages 1644-1660, October.
    11. Andreas Lanz & Gregor Reich & Ole Wilms, 2022. "Adaptive grids for the estimation of dynamic models," Quantitative Marketing and Economics (QME), Springer, vol. 20(2), pages 179-238, June.
    12. Shi, Yi & Deng, Yawen & Wang, Guoan & Xu, Jiuping, 2020. "Stackelberg equilibrium-based eco-economic approach for sustainable development of kitchen waste disposal with subsidy policy: A case study from China," Energy, Elsevier, vol. 196(C).
    13. Lucio Bianco & Massimiliano Caramia & Stefano Giordani & Veronica Piccialli, 2016. "A Game-Theoretic Approach for Regulating Hazmat Transportation," Transportation Science, INFORMS, vol. 50(2), pages 424-438, May.
    14. Xu, Jiuping & Shu, Kejing & Wang, Fengjuan & Yang, Guocan, 2024. "Bi-level multi-objective distribution strategy integrating the permit trading scheme towards coal production capacity layout optimization: Case study from China," Resources Policy, Elsevier, vol. 91(C).
    15. Yokoyama, Ryohei & Kitano, Hiroyuki & Wakui, Tetsuya, 2017. "Optimal operation of heat supply systems with piping network," Energy, Elsevier, vol. 137(C), pages 888-897.
    16. Cerulli, Martina & Serra, Domenico & Sorgente, Carmine & Archetti, Claudia & Ljubić, Ivana, 2023. "Mathematical programming formulations for the Collapsed k-Core Problem," European Journal of Operational Research, Elsevier, vol. 311(1), pages 56-72.
    17. Chan Y. Han & Brian J. Lunday & Matthew J. Robbins, 2016. "A Game Theoretic Model for the Optimal Location of Integrated Air Defense System Missile Batteries," INFORMS Journal on Computing, INFORMS, vol. 28(3), pages 405-416, August.
    18. Lorenzo Lampariello & Simone Sagratella, 2015. "It is a matter of hierarchy: a Nash equilibrium problem perspective on bilevel programming," DIAG Technical Reports 2015-07, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
    19. Grimm, Veronika & Schewe, Lars & Schmidt, Martin & Zöttl, Gregor, 2017. "Uniqueness of market equilibrium on a network: A peak-load pricing approach," European Journal of Operational Research, Elsevier, vol. 261(3), pages 971-983.
    20. Wei Jiang & Huiqiang Wang & Bingyang Li & Haibin Lv & Qingchuan Meng, 2020. "A multi-user multi-operator computing pricing method for Internet of things based on bi-level optimization," International Journal of Distributed Sensor Networks, , vol. 16(1), pages 15501477199, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:259:y:2020:i:c:s0306261919319099. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.