IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v349y2025i2d10.1007_s10479-019-03183-5.html
   My bibliography  Save this article

A genetic algorithm-based grey-box model for ship fuel consumption prediction towards sustainable shipping

Author

Listed:
  • Liqian Yang

    (Harbin Engineering University
    Aalborg University)

  • Gang Chen

    (Aalborg University)

  • Niels Gorm Malý Rytter

    (Aalborg University)

  • Jinlou Zhao

    (Harbin Engineering University)

  • Dong Yang

    (The Hong Kong Polytechnic University)

Abstract

In order to enhance sustainability in maritime shipping, shipping companies spend good efforts in improving the operational energy efficiency of existing ships. Accurate fuel consumption prediction model is a prerequisite of such operational improvements. Existing grey-box models (GBMs) are found with significant performance potential for ship fuel consumption prediction, although having a limitation of separating weather directions. Aiming to overcome this limitation, we propose a novel genetic algorithm-based GBM (GA-based GBM), where ship fuel consumption is modelled in a procedure based on basic principles of ship propulsion and the unknown parameters in this model are estimated with a GA-based procedure. Real ship operation data from a crude oil tanker over a 7-year sailing period are used to demonstrate the accuracy and reliability of the proposed model. To highlight the contribution of this work, we compare the proposed model against the latest GBM. The results show that the fitting performance of the proposed model is remarkably better, especially for oblique weather directions. The proposed model can be employed as a basis of ship energy efficiency management programs to reduce fuel consumption and greenhouse gas (GHG) emissions of a ship. This is beneficial to achieve the goal of sustainable shipping.

Suggested Citation

  • Liqian Yang & Gang Chen & Niels Gorm Malý Rytter & Jinlou Zhao & Dong Yang, 2025. "A genetic algorithm-based grey-box model for ship fuel consumption prediction towards sustainable shipping," Annals of Operations Research, Springer, vol. 349(2), pages 525-551, June.
    • Handle: RePEc:spr:annopr:v:349:y:2025:i:2:d:10.1007_s10479-019-03183-5
    DOI: 10.1007/s10479-019-03183-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-019-03183-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10479-019-03183-5?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.

    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:spr:annopr:v:349:y:2025:i:2:d:10.1007_s10479-019-03183-5. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.