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Smart Routing for Sustainable Shipping: A Review of Trajectory Optimization Approaches in Waterborne Transport

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  • Yevgeniy Kalinichenko

    (Department of Navigation and Control of the Ship, Odesa National Maritime University, Mechnykova St. 34, 65029 Odesa, Ukraine)

  • Sergey Rudenko

    (Department of Navigation and Maritime Safety, Odesa National Maritime University, Mechnykova St. 34, 65029 Odesa, Ukraine)

  • Andrii Holovan

    (Department of Navigation and Control of the Ship, Odesa National Maritime University, Mechnykova St. 34, 65029 Odesa, Ukraine)

  • Nadiia Vasalatii

    (Department of Navigation and Control of the Ship, Odesa National Maritime University, Mechnykova St. 34, 65029 Odesa, Ukraine)

  • Anastasiia Zaiets

    (Department of Shipbuilding and Ship Repair, Odesa National Maritime University, Mechnykova St. 34, 65029 Odesa, Ukraine)

  • Oleksandr Koliesnik

    (Department of Navigation and Control of the Ship, Odesa National Maritime University, Mechnykova St. 34, 65029 Odesa, Ukraine)

  • Leonid Oberto Santana

    (Department of Navigation and Control of the Ship, Odesa National Maritime University, Mechnykova St. 34, 65029 Odesa, Ukraine)

  • Nataliia Dolynska

    (Department of Navigation and Control of the Ship, Odesa National Maritime University, Mechnykova St. 34, 65029 Odesa, Ukraine)

Abstract

Smart routing has emerged as a critical enabler of sustainable shipping, addressing the growing demand for energy-efficient, safe, and adaptive vessel navigation in both maritime and inland waterborne transport. This review examines the current landscape of trajectory optimization approaches by analyzing selected peer-reviewed studies and categorizing them into six thematic areas: AI/ML-based prediction, optimization and path planning algorithms, data-driven methods using AIS and GIS, weather routing and environmental modeling, digital platforms and decision support systems, and hybrid or rule-based frameworks for autonomous navigation. The analysis highlights recent advances in deep learning for trajectory forecasting, multi-objective and heuristic optimization techniques, and the use of real-time environmental data in routing decisions. Supplemental review using Scopus-based topic mapping confirms the centrality of integrated digital strategies, high-performance computing, and physics-informed modeling in emerging research. Despite notable progress, the field remains fragmented, with limited real-time integration, underexplored regulatory alignment, and a lack of explainable AI applications. The review concludes by outlining future directions, including the development of hybrid and interpretable optimization frameworks, and expanding research tailored to inland navigation with its distinct operational challenges. These insights aim to support the design of next-generation navigation systems that are robust, intelligent, and environmentally compliant.

Suggested Citation

  • Yevgeniy Kalinichenko & Sergey Rudenko & Andrii Holovan & Nadiia Vasalatii & Anastasiia Zaiets & Oleksandr Koliesnik & Leonid Oberto Santana & Nataliia Dolynska, 2025. "Smart Routing for Sustainable Shipping: A Review of Trajectory Optimization Approaches in Waterborne Transport," Sustainability, MDPI, vol. 17(18), pages 1-40, September.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:18:p:8466-:d:1754419
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    References listed on IDEAS

    as
    1. Zvyagina, Tatiana & Zvyagin, Petr, 2022. "A model of multi-objective route optimization for a vessel in drifting ice," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    2. Cheng-Hong Yang & Guan-Cheng Lin & Chih-Hsien Wu & Yen-Hsien Liu & Yi-Chuan Wang & Kuo-Chang Chen, 2022. "Deep Learning for Vessel Trajectory Prediction Using Clustered AIS Data," Mathematics, MDPI, vol. 10(16), pages 1-19, August.
    3. Epameinondas K. Koumaniotis & Fotios D. Kanellos, 2024. "Optimal Routing and Sustainable Operation Scheduling of Large Ships with Integrated Full-Electric Propulsion," Sustainability, MDPI, vol. 16(23), pages 1-18, December.
    4. Wang, Kai & Li, Zhongwei & Zhang, Rui & Ma, Ranqi & Huang, Lianzhong & Wang, Zhuang & Jiang, Xiaoli, 2025. "Computational fluid dynamics-based ship energy-saving technologies: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 207(C).
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