IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v252y2016i2p635-645.html
   My bibliography  Save this article

A framework for crude oil scheduling in an integrated terminal-refinery system under supply uncertainty

Author

Listed:
  • Oliveira, F.
  • Nunes, P.M.
  • Blajberg, R.
  • Hamacher, S.

Abstract

Operational decisions for crude oil scheduling activities are determined on a daily basis and have a strong impact on the overall supply chain cost. The challenge is to develop a feasible schedule at a low cost that has a high level of confidence. This paper presents a framework to support decision making in terminal-refinery systems under supply uncertainty. The proposed framework comprises a stochastic optimization model based on mixed-integer linear programming for scheduling a crude oil pipeline connecting a marine terminal to an oil refinery and a method for representing oil supply uncertainty. The scenario generation method aims at generating a minimal number of scenarios while preserving as much as possible of the uncertainty characteristics. The proposed framework was evaluated considering real-world data. The numerical results suggest the efficiency of the framework in providing resilient solutions in terms of feasibility in the face of the inherent uncertainty.

Suggested Citation

  • Oliveira, F. & Nunes, P.M. & Blajberg, R. & Hamacher, S., 2016. "A framework for crude oil scheduling in an integrated terminal-refinery system under supply uncertainty," European Journal of Operational Research, Elsevier, vol. 252(2), pages 635-645.
    • Handle: RePEc:eee:ejores:v:252:y:2016:i:2:p:635-645
    DOI: 10.1016/j.ejor.2016.01.034
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221716000795
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2016.01.034?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. Escudero, Laureano F. & Quintana, Francisco J. & Salmeron, Javier, 1999. "CORO, a modeling and an algorithmic framework for oil supply, transformation and distribution optimization under uncertainty," European Journal of Operational Research, Elsevier, vol. 114(3), pages 638-656, May.
    2. Geyer, Alois & Hanke, Michael & Weissensteiner, Alex, 2010. "No-arbitrage conditions, scenario trees, and multi-asset financial optimization," European Journal of Operational Research, Elsevier, vol. 206(3), pages 609-613, November.
    3. Gabriel, Steven A. & Zhuang, Jifang & Egging, Ruud, 2009. "Solving stochastic complementarity problems in energy market modeling using scenario reduction," European Journal of Operational Research, Elsevier, vol. 197(3), pages 1028-1040, September.
    4. Ulstein, Nina Linn & Nygreen, Bjorn & Sagli, Jan Richard, 2007. "Tactical planning of offshore petroleum production," European Journal of Operational Research, Elsevier, vol. 176(1), pages 550-564, January.
    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. Klepikov, Vladimir Pavlovich & Klepikov, Vladimir Vladimirovich, 2020. "Quantitative approach to estimating crude oil supply in Southern Europe," Resources Policy, Elsevier, vol. 69(C).
    2. 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.
    3. Aragão, Amanda & Giampietro, Mario, 2016. "An integrated multi-scale approach to assess the performance of energy systems illustrated with data from the Brazilian oil and natural gas sector," Energy, Elsevier, vol. 115(P2), pages 1412-1423.

    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. Wong, Man Hong, 2013. "Investment models based on clustered scenario trees," European Journal of Operational Research, Elsevier, vol. 227(2), pages 314-324.
    2. Owadally, Iqbal & Jang, Chul & Clare, Andrew, 2021. "Optimal investment for a retirement plan with deferred annuities," Insurance: Mathematics and Economics, Elsevier, vol. 98(C), pages 51-62.
    3. Jun Maekawa & Koji Shimada, 2019. "A Speculative Trading Model for the Electricity Market: Based on Japan Electric Power Exchange," Energies, MDPI, vol. 12(15), pages 1-15, July.
    4. Pineau, Pierre-Olivier & Rasata, Hasina & Zaccour, Georges, 2011. "Impact of some parameters on investments in oligopolistic electricity markets," European Journal of Operational Research, Elsevier, vol. 213(1), pages 180-195, August.
    5. Mustapha Ouhimmou & Sophie D'Amours & Robert Beauregard & Daoud Ait-Kadi & Satyaveer Singh Chauhan, 2009. "Optimization Helps Shermag Gain Competitive Edge," Interfaces, INFORMS, vol. 39(4), pages 329-345, August.
    6. Patrizia Beraldi & Maria Bruni, 2014. "A clustering approach for scenario tree reduction: an application to a stochastic programming portfolio optimization problem," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(3), pages 934-949, October.
    7. Hanke, Michael & Penev, Spiridon & Schief, Wolfgang & Weissensteiner, Alex, 2017. "Random orthogonal matrix simulation with exact means, covariances, and multivariate skewness," European Journal of Operational Research, Elsevier, vol. 263(2), pages 510-523.
    8. Devine, Mel T. & Siddiqui, Sauleh, 2023. "Strategic investment decisions in an oligopoly with a competitive fringe: An equilibrium problem with equilibrium constraints approach," European Journal of Operational Research, Elsevier, vol. 306(3), pages 1473-1494.
    9. Pongsakdi, Arkadej & Rangsunvigit, Pramoch & Siemanond, Kitipat & Bagajewicz, Miguel J., 2006. "Financial risk management in the planning of refinery operations," International Journal of Production Economics, Elsevier, vol. 103(1), pages 64-86, September.
    10. Moiseeva, Ekaterina & Wogrin, Sonja & Hesamzadeh, Mohammad Reza, 2017. "Generation flexibility in ramp rates: Strategic behavior and lessons for electricity market design," European Journal of Operational Research, Elsevier, vol. 261(2), pages 755-771.
    11. Devine, Mel T. & Bertsch, Valentin, 2018. "Examining the benefits of load shedding strategies using a rolling-horizon stochastic mixed complementarity equilibrium model," European Journal of Operational Research, Elsevier, vol. 267(2), pages 643-658.
    12. Rafael Bernardo Carmona Benitez & Hector Cruz, 2023. "A multiproduct gasoline supply chain with product standardization and postponement strategy," Papers 23004, Working Papers of Business and Economics School. Anahuac University (Mexico)..
    13. Raimund M. Kovacevic, 2019. "Arbitrage conditions for electricity markets with production and storage," Computational Management Science, Springer, vol. 16(4), pages 671-696, October.
    14. Baltensperger, Tobias & Füchslin, Rudolf M. & Krütli, Pius & Lygeros, John, 2016. "Multiplicity of equilibria in conjectural variations models of natural gas markets," European Journal of Operational Research, Elsevier, vol. 252(2), pages 646-656.
    15. Egging-Bratseth, Ruud & Baltensperger, Tobias & Tomasgard, Asgeir, 2020. "Solving oligopolistic equilibrium problems with convex optimization," European Journal of Operational Research, Elsevier, vol. 284(1), pages 44-52.
    16. Consiglio, Andrea & Tumminello, Michele & Zenios, Stavros A., 2015. "Designing and pricing guarantee options in defined contribution pension plans," Insurance: Mathematics and Economics, Elsevier, vol. 65(C), pages 267-279.
    17. Huppmann, Daniel, 2013. "Endogenous production capacity investment in natural gas market equilibrium models," European Journal of Operational Research, Elsevier, vol. 231(2), pages 503-506.
    18. Tiago P. Filomena & Miguel A. Lejeune, 2014. "Warm-Start Heuristic for Stochastic Portfolio Optimization with Fixed and Proportional Transaction Costs," Journal of Optimization Theory and Applications, Springer, vol. 161(1), pages 308-329, April.
    19. Ibrahim Abada & Pierre-André Jouvet, 2013. "A stochastic generalized Nash-Cournot model for the northwestern European natural gas markets: The S-GaMMES model," Working Papers 1308, Chaire Economie du climat.
    20. Geyer, Alois & Hanke, Michael & Weissensteiner, Alex, 2014. "No-Arbitrage ROM simulation," Journal of Economic Dynamics and Control, Elsevier, vol. 45(C), pages 66-79.

    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:ejores:v:252:y:2016:i:2:p:635-645. 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/locate/eor .

    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.