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Ship fleet scheduling and deployment optimization considering carbon and sulfur emissions

Author

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  • Zhen, Lu
  • Jiang, Mingzhu
  • Wang, Shuaian
  • Wu, Jingwen

Abstract

With increasing demands to reduce the environmental impact of maritime shipping, how to schedule and deploy ship fleets for green maritime shipping has become an increasingly prominent issue in both research and practice. This study introduces a mixed-integer nonlinear programming model for complex shipping networks with multiple routes. The model integrates the adoption of green technologies and biofuels, adjusts sailing speeds for each segment, sets timetables, and allocates cargo between origin–destination pairs on each route, while considering carbon emissions and sulfur limits in Emission Control Areas. The aim is to assist shipping companies in maximizing their cost-effectiveness while adhering to strict environmental regulations. To tackle this issue, we formulate an adaptive large neighborhood search heuristic algorithm grounded in alternating optimization. The algorithm proposes customized operators to optimize both solution speed and solution quality. Extensive numerical experiments demonstrate that, for large-scale instances, the proposed ALNS heuristic achieves an average optimality gap of 4.52 % relative to the lower bound, and significantly outperforms benchmark methods in terms of both solution quality and computational efficiency. Additionally, sensitivity analyses reveal the significant impact of fuel price fluctuations, technological upgrade costs, transshipment, and environmental policy regulations on fleet deployment and total costs. We propose strategies such as initially increasing the deployment of more economical green technologies when the prices of expensive green technologies decrease and gradually transitioning to more advanced technologies. These managerial insights may be useful for optimizing the tactical operation management of fleet deployment for green maritime shipping. Finally, extensions are explored to enable our proposed methodology to be applied to a more generic and realistic environment of maritime shipping operations.

Suggested Citation

  • Zhen, Lu & Jiang, Mingzhu & Wang, Shuaian & Wu, Jingwen, 2026. "Ship fleet scheduling and deployment optimization considering carbon and sulfur emissions," European Journal of Operational Research, Elsevier, vol. 329(2), pages 653-668.
    • Handle: RePEc:eee:ejores:v:329:y:2026:i:2:p:653-668
    DOI: 10.1016/j.ejor.2025.07.021
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    References listed on IDEAS

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