IDEAS home Printed from https://ideas.repec.org/a/ids/ijmore/v22y2022i4p496-527.html
   My bibliography  Save this article

A robust global optimisation framework for stochastic integrated refinery planning with demand and price uncertainties

Author

Listed:
  • Mahmud R. Siamizade
  • Theodore B. Trafalis

Abstract

This study presents a global optimisation framework for the strategic planning of an integrated oil refinery while accounting for the uncertainties in the crude oil and final product demand and market prices. To model the uncertainties, three modelling schemes have been utilised: 1) a robust optimisation framework; 2) a fuzzy possibilistic programming approach; 3) a risk aversion two-stage stochastic model with financial risk management. These methodologies are applied to an industrial case study, comprising an integrated oil refinery with tasks spanning from unloading crude oil at the refinery's front to distributing refined products to distribution centres by pipelines. The notable feature of this study is developing an optimisation approach with a multi-period mixed-integer nonlinear programming (MINLP) model and solving it through an aggregation/disaggregation linearisation algorithm and obtaining ε-global optimal solutions. The results indicate significant economical and operational advantage of the robust optimisation scheme depending on the decision maker's risk attitude.

Suggested Citation

  • Mahmud R. Siamizade & Theodore B. Trafalis, 2022. "A robust global optimisation framework for stochastic integrated refinery planning with demand and price uncertainties," International Journal of Mathematics in Operational Research, Inderscience Enterprises Ltd, vol. 22(4), pages 496-527.
    • Handle: RePEc:ids:ijmore:v:22:y:2022:i:4:p:496-527
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=126044
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

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

    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:ids:ijmore:v:22:y:2022:i:4:p:496-527. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=320 .

    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.