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Cited by 96Maritime Transport Emissions and EfficiencyArctic and Russian Policy StudiesMaritime Navigation and Safety

Data-driven ship energy efficiency analysis and optimization model for route planning in ice-covered Arctic waters

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Zhang, Chi, Zhang, Di, Zhang, Mingyang, Mao, Wengang·2019·Ocean Engineering
DOI 10.1016/j.oceaneng.2019.05.053
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  • AI and ML can be used to analyze weather patterns, currents, and other factors to optimize ship routes for speed, fuel efficiency, and safety.

    “AI and ML can be used to analyze weather patterns, currents, and other factors to optimize ship routes for speed, fuel efficiency, and safety [108] .”

    From Artificial intelligence, machine learning and deep learning in advanced robotics, a review (2023) · cites this paper as [108]

    The Abstract (page unavailable) describes a neural-network model that predicts ship energy efficiency under varying Arctic conditions and selects a route for optimum energy efficiency. The copy also shows wind, wave, ice-concentration, and ship-speed data, but the retrieved passages do not establish analysis of currents or route optimization for safety or speed as distinct objectives. (LLM, conf=0.87)

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Research works printed but not linked: 78

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  2. Ballou P J. Ship Energy Efficiency Management Requires a Total Solution Approach. Marine Technology Society Journal, 2013, 47(1):83-95. An application of fuzzy-AHP to ship operational energy efficiency measures. Ocean Engineering, 2016, 121:392-402.
  3. Cai W, Lin J. The navigation practice and normalized navigation prospect of Northeast Arctic channel. China Water Transport. 2016, 2, 21 24
  4. Choi M, Chung H, Yamaguchi H, Nagakawa K. Arctic sea route path planning based on an uncertain ice prediction model. Cold Regions Science & Technology, 2015, 109(109):61-69.
  5. Coraddu A, Figari M, Savio S. Numerical investigation on ship energy efficiency by Monte Carlo simulation. Proceedings of the Institution of Mechanical Engineers Part M Journal of Engineering for the Maritime Environment, 2014, 228(3):220-234.
  6. Danchuk V, Bakulich O, Svatko V. An Improvement in ant Algorithm Method for Optimizing a Transport Route with Regard to Traffic Flow. Procedia Engineering, 2017, 187: 425-434.
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  31. Tillig F, Ringsberg J W, Mao W, Ramne B. A generic energy systems model for efficient ship design and operation. Proceedings of the Institution of Mechanical Engineers Part M Journal of Engineering for the Maritime Environment, 2017, 231(2):649-666.
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  35. Wang Y, Yu Y, Li K, Zhao X, G G. A human-computer cooperation improved ant colony optimization for ship pipe route design. Ocean Engineering, 2018, 150:12-20.
  36. Wang Y, Zio E, Wei X, Zhang D, Wu B. A resilience perspective on water transport systems: The case of Eastern Star. International Journal of Disaster Risk Reduction, 2019, 33: 343-354.
  37. Zhang M, Zhang D, Fu S, Yan X, Goncharov V. Safety distance modeling for ship escort operations in Arctic ice-covered waters. Ocean Engineering, 2017, 146:202-216.
  38. Zhang M, Zhang D, Goerlandt F, Yan X, Kujala P. Use of HFACS and fault tree model for collision risk factors analysis of icebreaker assistance in ice-covered waters. Safety Science, 2019, 111:128-143.
  39. Zhao Q. The ice- China Maritime Safety. 2014, 9, 17 20.
  40. Abdulkader M M S, Gajpal Y, Elmekkawy T Y. Hybridized ant colony algorithm for the Multi Compartment Vehicle Routing Problem. Applied Soft Computing, 2015, 37:196-203.
  41. Ballou P J. Ship Energy Efficiency Management Requires a Total Solution Approach. Marine Technology Society Journal, 2013, 47(1):83-95. An application of fuzzy-AHP to ship operational energy efficiency measures. Ocean Engineering, 2016, 121:392-402.
  42. Cai W, Lin J. The navigation practice and normalized navigation prospect of Northeast Arctic channel. China Water Transport. 2016, 2, 21 24
  43. Choi M, Chung H, Yamaguchi H, Nagakawa K. Arctic sea route path planning based on an uncertain ice prediction model. Cold Regions Science & Technology, 2015, 109(109):61-69.
  44. Coraddu A, Figari M, Savio S. Numerical investigation on ship energy efficiency by Monte Carlo simulation. Proceedings of the Institution of Mechanical Engineers Part M Journal of Engineering for the Maritime Environment, 2014, 228(3):220-234.
  45. Danchuk V, Bakulich O, Svatko V. An Improvement in ant Algorithm Method for Optimizing a Transport Route with Regard to Traffic Flow. Procedia Engineering, 2017, 187: 425-434.
  46. Deo M C, Jagdale S S. Prediction of breaking waves with neural networks. Ocean Engineering, 2003, 30(9):1163-1178.
  47. Douglas B. Global sea level rise. Journal of Geophysical Research Atmospheres. 1991, 96.C4:6981-6992.
  48. Du Y, Meng Q, Wang S, Kuang H. Two-phase optimal solutions for ship speed and trim optimization over a voyage using voyage report data. Transportation Research Part B Methodological, 2019, 122:88-114.
  49. Einhäuser Wolfgang , Koch C, Makeig S. The duration of the attentional blink in natural scenes depends on stimulus category. Vision Research 47.5(2007):597-607.
  50. Ferdous R, Khan F, Sadiq R, Amyotte P, Veitch B. Fault and event tree analyses for process systems risk analysis: uncertainty handling formulations. Risk Analysis, 2011, 31(1):86.
  51. Fu S, Zhang D, Montewka J, Zio E, Yan X. A quantitative approach for risk assessment of a ship stuck in ice in Arctic waters. Safety Science, 2017, 107:145-154.
  52. Fu S, Zhang D, Montewka J, Yan X, Zio E. Towards a probabilistic model for predicting ship besetting in ice in arctic waters. Reliability Engineering & System Safety, 2016, 155:124-136.
  53. Guinness R, Saarimaki J, Ruotsalainen L, Kuusniemi H, Goerlandt F, Montewka J, et al. A method for ice-aware maritime route optimization. Position, Location and Navigation Symposium (PLANS 2014). May 5-8, 2014, Monterey, 1 2 3 4 5 6 7
  54. Kotovirta V, Jalonen R, Axell L, Riska K, Berglund R. A system for route optimization in ice-covered waters. Cold Regions Science & Technology, 2009, 55(1):52-62.
  55. Li F, Goerlandt F, Kujala P, Lehtiranta J, Lensu M. Evaluation of selected state-of-the-art methods for ship transit simulation in various ice conditions based on full-scale measurement. Cold Regions Science and Technology 151(2018):94-108.
  56. Mao W, Rychlik I, Wallin J, Storhaug G. Statistical models for the speed prediction of a container ship. Ocean Engineering, 2016, 126:152-162.
  57. Meng Q, Du Y, Wang Y. Shipping log data based container ship fuel efficiency modeling. Transportation Research Part B Methodological, 2016, 83:207-229.
  58. Mileski J, Gharehgozli A, Ghoram L, Swaney R. Cooperation in developing a disaster prevention and response plan for Arctic shipping. Marine Policy, 2018, 92:131-137.
  59. Montewka J, Goerlandt F, Lensu M, Kuuliala L, Guinness R. Toward a hybrid model of ship performance in ice suitable for route planning purpose. Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk & Reliability, 2018:1748006X1876451.
  60. Nam J-H, Park I, Lee H, Kwon M, Choi K, Seo Y-K. Simulation of optimal arctic routes using a numerical sea ice model based on an ice-coupled ocean circulation method. International Journal of Naval Architecture & Ocean Engineering, 2013, 5(2):210-226.
  61. Qian Z, Xu L, Yan X, Cheng J, Zhang L, Yao Z. Navigation strategy and economic research of the northeast passage in the Arctic. Chinese Journal of Polar Research. 2015, 27(2):203-211.
  62. Reimer N. Ship Trial for Testing of an Ice Route Optimization System. Arctic technology conference. March 23-25, 2015, Copenhagen, Denmark.
  63. Rumelhart, David E.; Hinton, Geoffrey E.; Williams, Ronald J. Learning representations by back-propagating errors. 1986, Nature. 323 (6088): 533 536.
  64. Smith, James S., W. Bo, and B. M. Wilamowski. Neural Network training with Levenberg-Marquardt and adaptable weight compression. IEEE Transactions on Neural Networks and Learning Systems (2018):1-8.
  65. Song R, Liu Y, Bucknall R. A multi-layered fast marching method for unmanned surface vehicle path planning in a time-variant maritime environment. Ocean Engineering, 2017, 129:301-317.
  66. Spreen G, Kaleschke L, Heygster G. Sea ice remote sensing using AMSR-E 89 GHz channels. Journal of Geophysical Research Atmospheres. 2008, Vol. 113.
  67. Stathakis D. How many hidden layers and nodes? International Journal of Remote Sensing. 2009. 30 (8) :2133-2147
  68. Operational Limits Assessment Risk Indexing System (POLARIS). IOP Conference Series: Earth and Environmental Science 34.1(2016):012034.
  69. Sun X, Yan X, Wu B, Song X. Analysis of the operational energy efficiency for inland river ships. Transportation Research Part D Transport & Environment, 2013, 22(3):34-39.
  70. Tillig F, Ringsberg J W, Mao W, Ramne B. A generic energy systems model for efficient ship design and operation. Proceedings of the Institution of Mechanical Engineers Part M Journal of Engineering for the Maritime Environment, 2017, 231(2):649-666.
  71. Tsou M C, Cheng H C. An Ant Colony Algorithm for efficient ship routing. Polish Maritime Research, 2013, 20(3):28-38.
  72. V. Leh -covered waters. Journal of Navigation. (submitted), 2018.
  73. Wang Q, Li Y, Diao M, Gao W, Qi Z. Performance enhancement of INS/CNS integration navigation system based on particle swarm optimization back propagation neural network. Ocean Engineering, 2015, 108:33-45.
  74. Wang Y, Yu Y, Li K, Zhao X, G G. A human-computer cooperation improved ant colony optimization for ship pipe route design. Ocean Engineering, 2018, 150:12-20.
  75. Wang Y, Zio E, Wei X, Zhang D, Wu B. A resilience perspective on water transport systems: The case of Eastern Star. International Journal of Disaster Risk Reduction, 2019, 33: 343-354.
  76. Zhang M, Zhang D, Fu S, Yan X, Goncharov V. Safety distance modeling for ship escort operations in Arctic ice-covered waters. Ocean Engineering, 2017, 146:202-216.
  77. Zhang M, Zhang D, Goerlandt F, Yan X, Kujala P. Use of HFACS and fault tree model for collision risk factors analysis of icebreaker assistance in ice-covered waters. Safety Science, 2019, 111:128-143.
  78. Zhao Q. The ice- China Maritime Safety. 2014, 9, 17 20.

Cited by

Sorted by citations

Authors
Zhang, C., Zhang, D., Zhang, M., & Mao, W.
Affiliations
  • Wuhan University of Technology (CN)
  • Aalto University (FI)
  • Chalmers University of Technology (SE)
Corresponding author
Zhang, Di
ORCID
Venue
Ocean Engineering
Publisher
Elsevier BV
Published
2019-08
Volume
186
Pages
106071
ISSN
0029-8018 (ISSN-L)
DOI
10.1016/j.oceaneng.2019.05.053
OpenAlex
W2952062072
Cited by
96
References
0
Citations by year
2026: 10 · 2025: 12 · 2024: 9 · 2023: 17 · 2022: 21 · 2021: 17 · 2020: 10
FWCI
4.92
Type
journal article
Language
en
Open access
Licence
Locations
  • Ocean Engineering (publishedVersion)
  • Aaltodoc (Aalto University) (submittedVersion, cc-by-nc-nd)
  • Chalmers Research (Chalmers University of Technology) (submittedVersion)
Funders
  • H2020 Marie Skłodowska-Curie Actions
  • National Natural Science Foundation of China
Topics
  • Physical Sciences › Environmental Science › Environmental Engineering › Maritime Transport Emissions and Efficiency
  • Social Sciences › Sociology and Political Science › Arctic and Russian Policy Studies
  • Physical Sciences › Engineering › Ocean Engineering › Maritime Navigation and Safety
Keywords
ship energy efficiency, ice-going ships, Arctic navigation, route optimization, energy efficiency prediction, neural network model, merchant shipping, environmental impact of shipping

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