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Modeling Shipping Route Optimization Based On Seasonal Ocean Current Variability Using The Ant Colony Algorithm

*Brachmantiyo Rachman Pratama orcid  -  Master Program of Marine and Coastal Engineering, Universitas Hang Tuah Surabaya, Indonesia
Erik Sugianto orcid scopus publons  -  Department of Marine Engineering, Universitas Hang Tuah Surabaya, Indonesia
Supartono Supartono  -  Master Program of Marine and Coastal Engineering, Universitas Hang Tuah Surabaya, Indonesia
Stevanus Fransiscus Sahusilawahe  -  Suaka Bahari Maritime Academy Cirebon, Indonesia
Received: 9 Feb 2026; Revised: 29 Jun 2026; Accepted: 30 Jun 2026; Available online: 30 Jun 2026; Published: 1 Jul 2026.
Open Access Copyright (c) 2026 Kapal: Jurnal Ilmu Pengetahuan dan Teknologi Kelautan
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract

Indonesia possesses dense and strategic shipping lanes, where seasonal ocean currents in the Java Sea frequently impact vessel transit times. On palm-oil shipping routes from Kumai to various ports in Java, route efficiency is paramount as fuel constitutes the largest operational expense. Due to the variability of ocean currents, conventional routes are not always the most efficient choice. This study utilizes 2024 ocean-current data from the Copernicus Marine Service alongside the technical specifications of the MT Nusaniwe to calculate effective speed, travel time, and fuel consumption. The Ant Colony Optimization (ACO) algorithm is applied to evaluate alternative route combinations under varying seasonal current conditions. The methodology encompasses current analysis, effective-speed formulation, fuel-consumption modeling, and route-optimization simulations for both the northwest and southeast monsoon seasons. Previous literature has not integrated ACO with seasonal oceanographic data for route optimization, which defines the research gap addressed herein. The results demonstrate that ACO-based routes integrated with oceanographic data outperform conventional routes, achieving significant reductions in transit time and fuel consumption. Consequently, this model proves that leveraging seasonal currents can enhance vessel operational efficiency and support data-driven voyage planning.

Keywords: Route Optimization; Ocean; Currents, Monsoon; Ant Colony Optimization; Maritime Shipping

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  1. A. Rifai, B. Rochaddi, U. Fadika, J. Marwoto, and H. Setiyono, "Kajian Pengaruh Angin Musim Terhadap Sebaran Suhu Permukaan Laut (Studi Kasus : Perairan Pangandaran Jawa Barat) : "Study of Monsoon Influence on Sea Surface Temperature Distribution (Case Study: Pangandaran Waters, West Java)," Indonesian Journal of Oceanography, vol. 2, no. 1, pp. 98-104, Mar. 2020, doi: https://doi.org/10.14710/ijoce.v2i1.7499
  2. Y. N. Sari, A. Wirasatriya, K. Kunarso, B. Rochaddi, and G. Handoyo, "Variabilitas Arus Permukaan Di Perairan Samudra Hindia Selatan Jawa : Surface Current Variability in the Waters of the Southern Indian Ocean of Java," Indonesian Journal of Oceanography, vol. 2, no. 1, pp. 1-7, Mar. 2020, doi: https://doi.org/10.14710/ijoce.v2i1.6785
  3. A. Yusron, W. S. Pranowo, Y. Yulianto, and Candrasa, “Karakteristik Gelombang Laut di Teluk Banten dan Sekitarnya pada Monsun Peralihan Berdasarkan Data Model Global : The Characteristics of Sea Waves in Banten Bay and Its Surroundings During The Transitional Monsoon Based on Global Model Data ”, chartdatum, vol. 10, no. 2, pp. 141–156. 2025, doi: https://doi.org/10.37875/chartdatum.v10i2.364
  4. R. P. Pasaribu, H. Sagala, A. Rahman, and A. Cahyani, “Karakteristik arus Laut Jawa pada musim barat di beberapa kedalaman : Characteristics of Java Sea Currents in the West Monsoon at Several Depths,” Jurnal Teknik Kelautan, vol. 22, no. 1, 2024, doi: https://doi.org/10.32693/jgk.22.1.2024.823
  5. Y. Suo, L. N. Zhu, Q. G. Zang, and Q. Wang, “An ant colony optimization algorithm for selection problem,” Applied Mechanics and Materials, vols. 411–414, pp. 1939–1942, 2013. doi: https://doi.org/10.4028/www.scientific.net/AMM.411-414.1939
  6. E. Asmara and B. P. Ichtiarto, “Penerapan p-Median terhadap optimasi alokasi dan lokasi distribution center pada Sistem Logistik Pedesaan di Indonesia : Application of p-median to optimization of allocation and location distribution center in rural logistics system Indonesia,” Operations Excellence, vol. 13, no. 2, pp. 215–222, 2021, doi: https://doi.org/10.22441/oe.2021.v13.i2.020
  7. A. Ihsan, T. A. Adlie, and S. Harliansyah, “Optimalisasi pencarian jalur terpendek mobile robot menggunakan metode Ant Colony Optimization : Shortest Path Search Optimization for Mobile Robots Using the Ant Colony Optimization (ACO) Method,” Techné, vol. 23, no. 1, pp. 39–54, 2024, doi: https://doi.org/10.31358/techne.v23i1.389
  8. B. Vernimmen, W. Dullaert, and S. Engelen, "Schedule unreliability in liner shipping: origins and consequences for the hinterland supply chain," Maritime Economics & Logistics, vol. 9, no. 3, pp. 193–213, 2007, doi: https://doi.org/10.1057/palgrave.mel.9100182
  9. H. Wang, O. L. Osen, G. Li, W. Li, H.-N. Dai, and W. Zeng, "Big data and industrial Internet of Things for the maritime industry in Northwestern Norway," in TENCON 2015 - 2015 IEEE Region 10 Conference, 2015, pp. 1–5, doi: https://doi.org/10.1109/TENCON.2015.7372918
  10. Z. Tian, F. Liu, Z. Li, R. Malekian, and Y. Xie, “Development of key technologies in vessels connected to the Internet,” Symmetry, vol. 9, no. 10, Art. no. 211, 2017, doi: https://doi.org/10.3390/sym9100211
  11. A. Hlali and S. Hammami, "Seaport concept and services characteristics: Theoretical test," The Open Transportation Journal, vol. 11, no. 1, 2017, doi: https://doi.org/10.2174/1874447801711010120
  12. W. Abigail, M. Zainuri, and W. S. Pranowo, "Studi Tentang Produktivitas Primer Berdasarkan Distribusi Nutrien dan Intensitas Cahaya di Perairan Selat Badung, Bali : Study on Primary Productivity Based on Nutrient Distribution and Light Intensity in Badung Strait Waters, Bali," Journal of Oceanography, vol. 4, no. 1, pp. 150 - 158, Jan. 2015
  13. J. Zaucha and M. Matczak, “Role of maritime ports,” SHS Web of Conferences, vol. 58, Art. no. 01033, 2018. doi: https://doi.org/10.1051/shsconf/20185801033
  14. T. Wang, J. Liu, and F. Zeng, “Application of QFD and FMEA in ship power plant design,” in Proc. ISCID, 2018. doi: https://doi.org/10.1109/ISCID.2017.219
  15. C. Jiang, S. Fu, and Y. Yu, “Risk identification analysis of fire accidents for passenger ships by FMEA,” in Proc. ICTIS, 2023. doi: https://doi.org/10.1109/ICTIS60134.2023.10243781
  16. B. O. Ceylan, D. A. Akyar, and M. S. Celik, “A novel FMEA approach for risk assessment of air pollution from ships,” Marine Policy, vol. 150, Art. no. 105536, 2023. doi: https://doi.org/10.1016/j.marpol.2023.105536
  17. C. Fan, J. Montewka, and D. Zhang, “Risk prioritization in autonomous ship operational modes using FMEA,” in Proc. ICTIS, 2021. doi: https://doi.org/10.1109/ICTIS54573.2021.9798656
  18. Y. E. Priharanto et al., “Risk assessment of the fishing vessel main engine by fuzzy-FMEA approach,” Journal of Failure Analysis and Prevention, vol. 23, no. 2, pp. 697–706, 2023. doi: https://doi.org/10.1007/s11668-023-01607-w
  19. L. Huang, “A mathematical modeling and optimization algorithm for marine ship route planning,” Journal of Mathematics, vol. 2023, Art. no. 5671089, 2023. doi: http://doi.org/10.1155/2023/5671089
  20. X. Zhang, Y. Wang, and D. Zhang, “Location-routing optimization using hybrid ant colony algorithm,” Mathematics, vol. 12, no. 12, Art. no. 1851, 2024. doi: https://doi.org/10.3390/math12121851

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