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Leveraging machine learning and optimization models for enhanced seaport efficiency

Mahdi Jahangard et al.

Maritime Economics & Logistics2025https://doi.org/10.1057/s41278-024-00309-warticle
AJG 1ABDC B
Weight
0.58

What the paper says

Abstract This study provides an overview of the application of predictive and prescriptive analytics in seaport operations and explore the potential of integrating predictive outputs into prescriptive analytics to advance research in this field. A systematic review of 124 papers was performed to identify and classify key topics based on application areas, types of applications, and employed techniques. Our findings show a growing interest in developing either predictive or prescriptive analytics models to improve seaport operational efficiency. However, there is limited research combining predictive outputs with prescriptive analytics for data-driven decision-making. Additionally, the hybridization of machine learning and operations research techniques remains underexplored. One promising area is applying machine learning models, such as reinforcement learning, to solve optimization problems. Predictive maintenance and data-enabled operational control measures for port equipment and facilities are also highlighted as interesting future research areas.

12 citations

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https://doi.org/https://doi.org/10.1057/s41278-024-00309-w

Or copy a formatted citation

@article{mahdi2025,
  title        = {{Leveraging machine learning and optimization models for enhanced seaport efficiency}},
  author       = {Mahdi Jahangard et al.},
  journal      = {Maritime Economics & Logistics},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1057/s41278-024-00309-w},
}

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Leveraging machine learning and optimization models for enhanced seaport efficiency

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Evidence weight

0.58

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.58 × 0.4 = 0.23
M · momentum0.80 × 0.15 = 0.12
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.