Application of Artificial Intelligence (AI) in Ship Route Management to Reduce Fuel Consumption

Ratna Kurnia Dewi

Abstract


The application of artificial intelligence (AI) in ship route management is increasingly gaining attention in the shipping industry as a solution to improve operational efficiency and reduce fuel consumption. The maritime industry faces major challenges related to high operational costs and significant carbon emissions. Therefore, this study aims to analyze the effectiveness of AI in optimizing ship navigation to reduce fuel consumption. This study uses a quantitative method with an experimental approach and case studies, where data is collected through direct observation, interviews with ship operational managers, and analysis of documents related to fuel consumption before and after the application of AI. The results show that ships using AI experience a reduction in fuel consumption of 15-25% compared to conventional methods. Multiple linear regression analysis reveals that ship speed, ocean currents, and the selected route contribute significantly to fuel efficiency. Although the application of AI provides economic and environmental benefits, there are several challenges, such as high initial investment costs, limited digital infrastructure in some waters, and resistance of ship crews to new technologies. Therefore, this study recommends improving digital infrastructure, collaboration between shipping companies and governments, and training for ship crews to increase the adoption of AI in the maritime sector. With the right strategy, AI has the potential to be a key solution in creating a more efficient and sustainable shipping industry.


Keywords


AI; ship navigation; fuel efficiency; carbon emissions; route management

References


Creswell, J. W. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Sage Publications.

Ghozali, I. (2020). Application of multivariate analysis with IBM SPSS 25 program . Diponegoro University Publishing Agency.

Ghozali, I. (2021). Multivariate regression analysis for economic and business research . Diponegoro University.

International Maritime Organization (IMO). (2023). IMO strategy on reduction of GHG emissions from ships . Retrieved from https://www.imo.org

International Transport Forum. (2023). The impact of AI on maritime logistics: Efficiency and sustainability . OECD Publishing.

Jones, M., Smith, R., & Taylor, D. (2023). AI in maritime operations: Case studies and industry adoption trends . Maritime Technology Review, 12(4), 102-120.

Kim, H., & Lee, J. (2021). Artificial intelligence and energy efficiency in electric-powered ships . Journal of Marine Engineering, 17(3), 89-105.

Kim, J., & Lee, S. (2022). Artificial intelligence in maritime navigation: Fuel efficiency and operational optimization . Journal of Marine Science and Technology, 27(3), 125-140.

Lee, S., Park, Y., & Kim, H. (2022). Real-time AI-based route optimization for fuel-efficient maritime navigation . Journal of Ocean Engineering, 45(2), 75-92.

Liu, H., Zhang, Y., & Wang, X. (2023). Integration of AI-based route optimization in shipping industry: Challenges and opportunities . Maritime Economics & Logistics, 25(2), 210-230.

Miles, M. B., & Huberman, A. M. (2019). Qualitative data analysis: An expanded sourcebook (3rd ed.). Sage Publications.

Rolls-Royce. (2023). Autonomous ships: The future of AI in maritime navigation . Rolls-Royce Marine Technology Report.

Smith, R., Jones, M., & Taylor, D. (2022). GHG emissions reduction strategies in the shipping sector: The role of AI and digital transformation . International Journal of Sustainable Shipping, 10(1), 45-60.

Sugiyono. (2019). Quantitative, qualitative, and R&D research methods . Alfabeta.

Tanaka, K., Yamamoto, T., & Saito, H. (2022). AI-driven fuel optimization in cargo ships: A comparative study with traditional engine efficiency methods . Oceanic Engineering Journal, 39(1), 55-72.

United Nations Conference on Trade and Development (UNCTAD). (2023). Review of maritime transport 2023 . UNCTAD Publications.

Wang, L., Chen, K., & Zhao, Y. (2021). AI-driven fuel consumption reduction in cargo ships: A comparative study . Transportation Research Part D: Transport and Environment, 102, 1-15.

Wang, X., Liu, P., & Zhang, H. (2020). Traditional vs. AI-based ship navigation: A comparative analysis of fuel efficiency and environmental impact . Ocean Engineering, 198, 106-120.

Wang, X., Liu, P., & Zhang, H. (2022). AI-enhanced navigation systems: Impacts on fuel efficiency and carbon reduction in maritime industry . Transportation Research Part D: Transport and Environment, 109, 1-18.

Zhang, Y., Kim, H., & Park, J. (2020). Digitalization and smart shipping: The role of AI in modern maritime logistics . Journal of Maritime Science & Technology, 14(3), 80-98.

Zhang, Y., Kim, H., & Park, J. (2021). Smart shipping and AI-powered route optimization: A review of recent advances . Journal of Maritime Innovation, 15(4), 90-105


Full Text: PDF

DOI: 10.33751/jhss.v9i2.12488

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.