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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
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    References listed on IDEAS

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