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Optimizing ship voyages: a review on gas emissions, fuel consumption, and prediction – Journal of Ocean Engineering and Marine Energy

Last updated: January 26, 2026 4:45 pm
Published: 3 months ago
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No datasets were generated or analysed during the current study.

* Abebe M, Shin Y, Noh Y, Lee S, Lee I (2020) Machine learning approaches for ship speed prediction towards energy efficient shipping. Appl Sci 10(7):2325

Article Google Scholar

* Adland R, Cariou P, Wolff F-C (2020) Optimal ship speed and the cubic law revisited: empirical evidence from an oil tanker fleet. Transp Res Part E Logist Transp Rev 140:101972

Article Google Scholar

* Agarwala P, Chhabra S, Agarwala N (2021) Using digitalisation to achieve decarbonisation in the shipping industry. J Int Marit Saf Environ Aff Shipp 5(4):161-174

Google Scholar

* Aizenberg I, Aizenberg NN, Vandewalle JP (2000) Multi-valued and universal binary neurons: theory, learning and applications. Springer Science & Business Media

* Ammar NR, Seddiek IS (2017) Eco-environmental analysis of ship emission control methods: case study ro-ro cargo vessel. Ocean Eng 137:166-173. https://doi.org/10.1016/j.oceaneng.2017.03.052

Article Google Scholar

* Ammar NR, Seddiek IS (2020) An environmental and economic analysis of emission reduction strategies for container ships with emphasis on the improved energy efficiency indexes. Environ Sci Pollut Res Int 27(18):23342-23355. https://doi.org/10.1007/s11356-020-08861-7

Article Google Scholar

* Andersson H, Fagerholt K, Hobbesland K (2015) Integrated maritime fleet deployment and speed optimization: case study from roro shipping. Comput Oper Res 55:233-240. https://doi.org/10.1016/j.cor.2014.03.017

Article MathSciNet Google Scholar

* Bahrami N, Mostafa SS, Khosh Kholgh A (2025) Algorithmic optimisation of ship routes for improved fuel economy and reduced carbon footprint across varying sea states. Ships Offshore Struct: 1-13. https://doi.org/10.1080/17445302.2025.2502694

* Bahrami N, Siadatmousavi SM (2023) Ship voyage optimisation considering environmental forces using the iterative Dijkstra’s algorithm. Ships Offshore Struct. https://doi.org/10.1080/17445302.2023.2231200

Article Google Scholar

* Balcombe P, Heggo DA, Harrison M (2022) Total methane and CO2 emissions from liquefied natural gas carrier ships: the first primary measurements. Environ Sci Technol 56(13):9632-9640. https://doi.org/10.1021/acs.est.2c01383

Article Google Scholar

* Barreiro J, Zaragoza S, Diaz-Casas V (2022) Review of ship energy efficiency. Ocean Eng 257:111594

Article Google Scholar

* Bellman R (1954) The theory of dynamic programming. Bull Am Math Soc 60(6):503-515

Article MathSciNet Google Scholar

* Bouman EA, Lindstad E, Rialland AI, Strømman AH (2017) State-of-the-art technologies, measures, and potential for reducing GHG emissions from shipping-a review. Transp Res Part D Transp Environ 52:408-421

Article Google Scholar

* Bui-Duy L, Vu-Thi-Minh N (2021) Utilization of a deep learning-based fuel consumption model in choosing a liner shipping route for container ships in Asia. Asian J Shipp Logist 37(1):1-11

Article Google Scholar

* Caprace J-D, Marques CH, Assis LF, Lucchesi A, Pereda PC (2025) Sustainable shipping: modeling technological pathways toward net-zero emissions in maritime transport (Part I). Sustainability 17(8):3733

Article Google Scholar

* Cariou P (2011) Is slow steaming a sustainable means of reducing CO2 emissions from container shipping? Transp Res D Transp Environ 16(3):260-264

Article Google Scholar

* Chatzinikolaou S, Ventikos N, Bilgili L, Celebi UB (2016) Ship Life cycle greenhouse gas emissions. In: Energy, transportation and global warming. Springer, pp 883-895

* Corbett JJ, Wang H, Winebrake JJ (2009) The effectiveness and costs of speed reductions on emissions from international shipping. Transp Res D Transp Environ 14(8):593-598

Article Google Scholar

* Deng S, Mi Z (2023) A review on carbon emissions of global shipping. Marine Development 1(1):4

Article Google Scholar

* Dijkstra EW (1959) A note on two problems in connexion with graphs:(Numerische Mathematik, 1 (1959), p 269-271)

* DNV (2022) Energy transition outlook 2022

* Dong S, Wang P, Abbas K (2021) A survey on deep learning and its applications. Comput Sci Rev 40:100379

Article MathSciNet Google Scholar

* Dong Z, Bian X (2020) Ship pipe route design using improved A* algorithm and genetic algorithm. IEEE Access 8:153273-153296

Article Google Scholar

* Du W, Li Y, Zhang G, Wang C, Zhu B, Qiao J (2022) Ship weather routing optimization based on improved fractional order particle swarm optimization. Ocean Eng 248:110680. https://doi.org/10.1016/j.oceaneng.2022.110680

Article Google Scholar

* Du Y, Meng Q, Wang S, Kuang H (2019) Two-phase optimal solutions for ship speed and trim optimization over a voyage using voyage report data. Transp Res Part B Methodol 122:88-114. https://doi.org/10.1016/j.trb.2019.02.004

Article Google Scholar

* Ekmekçioğlu A, Ünlügençoğlu K, Çelebi UB (2022) Estimation of shipping emissions based on real-time data with different methods: a case study of an oceangoing container ship. Environ Dev Sustain 24(3):4451-4470. https://doi.org/10.1007/s10668-021-01605-8

Article Google Scholar

* El Mekkaoui S, Benabbou L, Caron S, Berrado A (2023) Deep learning-based ship speed prediction for intelligent maritime traffic management. J Mar Sci Eng 11(1):191

Article Google Scholar

* Emblemsvåg J (2025) A study on the limitations of green alternative fuels in global shipping in the foreseeable future. J Mar Sci Eng 13(1):79

Article Google Scholar

* Emmert-Streib F, Yang Z, Feng H, Tripathi S, Dehmer M (2020) An introductory review of deep learning for prediction models with big data. Front Artif Intell 3:4

Article Google Scholar

* Esmailian E, Steen S (2022) A new method for optimal ship design in real sea states using the ship power profile. Ocean Eng 259:111893

Article Google Scholar

* Fagerholt K, Gausel NT, Rakke JG, Psaraftis HN (2015) Maritime routing and speed optimization with emission control areas. Transp Res Part C Emerg Technol 52:57-73. https://doi.org/10.1016/j.trc.2014.12.010

Article Google Scholar

* Fagerholt K, Laporte G, Norstad I (2010) Reducing fuel emissions by optimizing speed on shipping routes. J Oper Res Soc 61(3):523-529. https://doi.org/10.1057/jors.2009.77

Article Google Scholar

* Fan A, Yang J, Yang L, Wu D, Vladimir N (2022) A review of ship fuel consumption models. Ocean Eng 264:112405

Article Google Scholar

* Gerakoudi-Ventouri K (2022) Review of studies of blockchain technology effects on the shipping industry. J Shipp Trade 7(1):2

Article Google Scholar

* Gilbert P, Bows-Larkin A, Mander S, Walsh C (2014) Technologies for the high seas: meeting the climate challenge. Carbon Manag 5(4):447-461

Google Scholar

* von Graf Westarp A (2020) A new model for the calculation of the bunker fuel speed-consumption relation. Ocean Eng 204:107262. https://doi.org/10.1016/j.oceaneng.2020.107262

Article Google Scholar

* Grifoll M, Borén C, Castells-Sanabra M (2022) A comprehensive ship weather routing system using CMEMS products and A* algorithm. Ocean Eng 255:111427

Article Google Scholar

* Guo Z, Hong M, Zhang Y, Shi J, Qian L, Li H (2024) Research on safety evaluation and weather routing optimization of ship based on roll dynamics and improved A* algorithm. Int J Nav Archit Ocean Eng. https://doi.org/10.1016/j.ijnaoe.2024.100605

Article Google Scholar

* Hasselmann K, Barnett TP, Bouws E, Carlson H, Cartwright DE, Enke K, Ewing J, Gienapp A, Hasselmann D, Kruseman P (1973) Measurements of wind-wave growth and swell decay during the Joint North Sea Wave Project (JONSWAP). Ergaenzungsheft zur Deutschen Hydrographischen Zeitschrift, Reihe A

* Hu Z, Zhou T, Zhen R, Jin Y, Li X, Osman MT (2022) A two-step strategy for fuel consumption prediction and optimization of ocean-going ships. Ocean Eng 249:110904

Article Google Scholar

* Hüffmeier J, Johanson M (2021) State-of-the-art methods to improve energy efficiency of ships. J Mar Sci Eng. https://doi.org/10.3390/jmse9040447

Article Google Scholar

* IMO (2014) Third greenhouse gas study 2014. International Maritime Organization London, UK

* IMO, C. (2020) Fourth IMO GHG study 2020. International Maritime Organization London, UK

* ISO (2002) ISO 15016:2002. Guidelines for the assessment of speed and power performance by analysis of speed trial data

* ISO (2015) ISO 15016:2015 ships and marine technology — guidelines for the assessment of speed and power performance by analysis of speed trial data, p 85

* ITTC (2011) ITTC — recommended procedures and guidelines

* James RW (1956) Application of wave forecasts to marine navigation

* Javaid A (2013) Understanding Dijkstra’s algorithm. Available at SSRN 2340905

* Jimenez VJ, Kim H, Munim ZH (2022) A review of ship energy efficiency research and directions towards emission reduction in the maritime industry. J Clean Prod 366:132888

Article Google Scholar

* Jordan MI, Mitchell TM (2015) Machine learning: trends, perspectives, and prospects. Science 349(6245):255-260

Article MathSciNet Google Scholar

* Jørgensen U, Belingmo PR, Murray B, Berge SP, Pobitzer A (2022) Ship route optimization using hybrid physics-guided machine learning. J Phys Conf Ser. https://doi.org/10.1088/1742-6596/2311/1/012037

Article Google Scholar

* Joung T-H, Kang S-G, Lee J-K, Ahn J (2020) The IMO initial strategy for reducing Greenhouse Gas (GHG) emissions, and its follow-up actions towards 2050. J Int Marit Saf Environ Aff Shipp 4(1):1-7

Google Scholar

* Jugović A, Bukša J, Dragoslavić A, Sopta D (2019) The possibilities of applying blockchain technology in shipping. Sci J Mar Res. https://doi.org/10.31217/p.33.2.19

* Karagiannidis P, Themelis N, Zaraphonitis G, Spandonidis C, Giordamlis C (2019) Ship fuel consumption prediction using artificial neural networks. Proceedings of the annual meeting of marine technology conference proceedings, Athens, Greece

* Kim B, Kim T-W (2017) Weather routing for offshore transportation using genetic algorithm. Appl Ocean Res 63:262-275

Article Google Scholar

* Kim Y-R, Jung M, Park J-B (2021) Development of a fuel consumption prediction model based on machine learning using ship in-service data. J Mar Sci Eng 9(2):137

Article Google Scholar

* Kim Y, Lee K, Nam B, Han Y (2023) Application of reinforcement learning based on curriculum learning for the pipe auto-routing of ships. J Comput des Eng 10(1):318-328

Google Scholar

* Kobayashi E, Asajima T, Sueyoshi N (2011) Advanced navigation route optimization for an oceangoing vessel. In: Methods and algorithms in navigation: marine navigation and safety of sea transportation, 149

* Kondratenko AA, Kujala P, Hirdaris SE (2023) Holistic and sustainable design optimization of Arctic ships. Ocean Eng 275:114095

Article Google Scholar

* Kotovirta V, Jalonen R, Axell L, Riska K, Berglund R (2009) A system for route optimization in ice-covered waters. Cold Reg Sci Technol 55(1):52-62

Article Google Scholar

* Krata P, Szlapczynska J (2018) Ship weather routing optimization with dynamic constraints based on reliable synchronous roll prediction. Ocean Eng 150:124-137

Article Google Scholar

* Kuhlemann S, Tierney K (2020) A genetic algorithm for finding realistic sea routes considering the weather. J Heuristics 26(6):801-825. https://doi.org/10.1007/s10732-020-09449-7

Article Google Scholar

* Kunkera Z, Opetuk T, Hadžić N, Tošanović N (2022) Using digital twin in a shipbuilding project. Appl Sci 12(24):12721

Article Google Scholar

* Kwon Y (2008) Speed loss due to added resistance in wind and waves. Nav Archit 3:14-16

Google Scholar

* Kytariolou A, Themelis N (2023) Ship routing optimisation based on forecasted weather data and considering safety criteria. J Navigation: 1-22

* Lashgari M, Akbari AA, Nasersarraf S (2021) A new model for simultaneously optimizing ship route, sailing speed, and fuel consumption in a shipping problem under different price scenarios. Appl Ocean Res 113:102725. https://doi.org/10.1016/j.apor.2021.102725

Article Google Scholar

* Le LT, Lee G, Park K-S, Kim H (2020) Neural network-based fuel consumption estimation for container ships in Korea. Marit Policy Manage 47(5):615-632. https://doi.org/10.1080/03088839.2020.1729437

Article Google Scholar

* LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436-444

Article Google Scholar

* Lee J-H, Nam Y-S, Kim Y, Liu Y, Lee J, Yang H (2022) Real-time digital twin for ship operation in waves. Ocean Eng 266:112867

Article Google Scholar

* Li M, Li B, Qi Z, Li J, Wu J (2023a) Optimized APF-ACO algorithm for ship collision avoidance and path planning. J Mar Sci Eng 11(6):1177

Article Google Scholar

* Li X (2021) An research on the design and optimization of shipping routes in the Arctic. J Phys: Conf Ser

* Li X, Gu Y, Fan X, Zou K, Hou X (2023b) An optimization model for ship speed based on maneuvering control. J Mar Sci Eng 11(1):49

Article Google Scholar

* Li X, Sun B, Guo C, Du W, Li Y (2020) Speed optimization of a container ship on a given route considering voluntary speed loss and emissions. Appl Ocean Res 94:101995. https://doi.org/10.1016/j.apor.2019.101995

Article Google Scholar

* Li X, Sun B, Jin J, Ding J (2022) Speed optimization of container ship considering route segmentation and weather data loading: turning point-time segmentation method. J Mar Sci Eng 10(12):1835

Article Google Scholar

* Li X, Wang H, Wu Q (2017) Multi-objective optimization in ship weather routing. In: 2017 constructive nonsmooth analysis and related topics (dedicated to the memory of VF Demyanov) (CNSA)

* Li Z, Ding L, Huang L, Ringsberg JW, Gong H, Fournier N, Chuang Z (2023c) Cost-benefit analysis of a trans-Arctic alternative route to the Suez Canal: a method based on high-fidelity ship performance, weather, and ice forecast models. J Mar Sci Eng 11(4):711

Article Google Scholar

* Liu M, Fang S, Dong H, Xu C (2021) Review of digital twin about concepts, technologies, and industrial applications. J Manuf Syst 58:346-361

Article Google Scholar

* Liu M, Zhou Q, Wang X, Yu C, Kang M (2020) Voyage performance evaluation based on a digital twin model. In: IOP conference series: materials science and engineering

* Lu R, Turan O, Boulougouris E, Banks C, Incecik A (2015) A semi-empirical ship operational performance prediction model for voyage optimization towards energy efficient shipping. Ocean Eng 110:18-28. https://doi.org/10.1016/j.oceaneng.2015.07.042

Article Google Scholar

* Luin B, Al-Mansour F, Perkovič M (2024) Optimization of shipping routes with AIS data. Therm Sci Eng Prog 56:103042

Article Google Scholar

* Lun YV, Lai K-H, Cheng TE, Yang D (2023) New technology development in the shipping industry. In: Shipping and logistics management. Springer, pp 257-279

* Mahesh B (2020) Machine learning algorithms — a review. Int J Sci Res (IJSR) 9:381-386

Article Google Scholar

* Mersin K (2020) A new method for calculating fuel consumption by using speed loss function. Int J Environ Geoinformatics 7(1):64-67

Article Google Scholar

* Mersin K, Alkan G, Mısırlıoğlu T (2017) A new method for calculating fuel consumption and displacement of a ship in maritime transport. Cogent Eng 4(1):1415107

Article Google Scholar

* Mersin K, Yıldırım M (2022) A new formula for calculation of optimum displacement and its effects. Int J Environ Geoinformatics 9(3):21-26

Article Google Scholar

* Molland AF, Turnock SR, Hudson DA (2017) Ship resistance and propulsion. Cambridge University Press

Book Google Scholar

* Moradi MH, Brutsche M, Wenig M, Wagner U, Koch T (2022) Marine route optimization using reinforcement learning approach to reduce fuel consumption and consequently minimize CO2 emissions. Ocean Eng 259:111882

Article Google Scholar

* Nguyen S, Chen PS-L, Du Y (2022) Risk assessment of maritime container shipping blockchain-integrated systems: an analysis of multi-event scenarios. Transp Res Part E Logist Transp Rev 163:102764

Article Google Scholar

* Nikolaidis E, Theodoropoulos K, Tonbol KMH, Maniati M (2024) Evaluating the effectiveness of the EU ETS in reducing greenhouse gas emissions in the shipping sector. Multidisc Adapt Clim Insights 1(2):75-84

Google Scholar

* Norstad I, Fagerholt K, Laporte G (2011) Tramp ship routing and scheduling with speed optimization. Transp Res Part C-Emerg Technol 19(5):853-865. https://doi.org/10.1016/j.trc.2010.05.001

Article Google Scholar

* Orlandi A, Calastrini F, Kalikatzarakis M, Guarnieri F, Busillo C, Coraddu A (2024) Air quality forecasting of along-route ship emissions in realistic meteo-marine scenarios. Ocean Eng 291:116464

Article Google Scholar

* Ormevik AB, Fagerholt K, Meisel F, Sandvik E (2023) A high-fidelity approach to modeling weather-dependent fuel consumption on ship routes with speed optimization. Marit Transp Res 5:100096

Article Google Scholar

* Panapakidis I, Sourtzi V-M, Dagoumas A (2020) Forecasting the fuel consumption of passenger ships with a combination of shallow and deep learning. Electronics 9(5):776

Article Google Scholar

* Panigrahi JK, Padhy CP, Sen D, Swain J, Larsen O (2012) Optimal ship tracking on a navigation route between two ports: a hydrodynamics approach. J Mar Sci Technol 17(1):59-67. https://doi.org/10.1007/s00773-011-0116-3

Article Google Scholar

* Pennino S, Gaglione S, Innac A, Piscopo V, Scamardella A (2020) Development of a new ship adaptive weather routing model based on seakeeping analysis and optimization. J Mar Sci Eng 8(4):270

Article Google Scholar

* Perera LP, Soares CG (2017) Weather routing and safe ship handling in the future of shipping. Ocean Eng 130:684-695

Article Google Scholar

* Petersen JP, Winther O, Jacobsen DJ (2012) A machine-learning approach to predict main energy consumption under realistic operational conditions. Ship Technol Res 59(1):64-72

Article Google Scholar

* Psaraftis HN, Kontovas CA (2014) Ship speed optimization: concepts, models and combined speed-routing scenarios. Transp Res Part C Emerg Technol 44:52-69

Article Google Scholar

* Qi Y, Yang J, Qin KS (2024) Spatial-temporal analysis of carbon emissions from ships in ports based on AIS data. Ocean Eng 308:118394

Article Google Scholar

* Repka S, Erkkilä-Välimäki A, Jonson JE, Posch M, Törrönen J, Jalkanen JP (2021) Assessing the costs and environmental benefits of IMO regulations of ship-originated SOx and NOx emissions in the Baltic Sea. Ambio 50(9):1718-1730

Article Google Scholar

* Roh M-I (2013) Determination of an economical shipping route considering the effects of sea state for lower fuel consumption. Int J Nav Archit Ocean Eng 5(2):246-262. https://doi.org/10.2478/IJNAOE-2013-0130

Article Google Scholar

* Ronen D (1982) The effect of oil price on the optimal speed of ships. J Oper Res Soc 33(11):1035-1040

Article Google Scholar

* Rudzki K, Gomulka P, Hoang AT (2022) Optimization model to manage ship fuel consumption and navigation time. Pol Marit Res 29(3):141-153

Article Google Scholar

* Ryder S, Chappell D (1980) Optimal speed and ship size for the liner trades. Marit Policy Manag 7(1):55-57

Article Google Scholar

* Schröder-Hinrichs J-U, Hollnagel E, Baldauf M, Hofmann S, Kataria A (2013) Maritime human factors and IMO policy. Marit Policy & Management 40(3):243-260

Article Google Scholar

* Seddiek IS, Elgohary MM (2014) Eco-friendly selection of ship emissions reduction strategies with emphasis on SOx and NOx emissions. Int J Nav Archit Ocean Eng 6(3):737-748

Article Google Scholar

* Serra P, Fancello G (2020) Towards the IMO’s GHG goals: a critical overview of the perspectives and challenges of the main options for decarbonizing international shipping. Sustainability 12(8):3220

Article Google Scholar

* Shi H, Wang X (2018) Research on the development path of blockchain in shipping industry. In: Proceedings of the Asia-Pacific conference on intelligent medical 2018 & international conference on transportation and traffic engineering 2018

* Sirimanne SN, Hoffman J, Juan W, Asariotis R, Assaf M, Ayala G, Benamara H, Chantrel D, Hoffmann J, Premti A (2019) Review of maritime transport 2019. United Nations conference on trade and development, Geneva, Switzerland

* Sun Y, Deng J, Zhang F, Hu W, Zhang Y, Chen Y, Xiao Y, Cui L, Liu Y, Zhao J (2025) Global distribution and warming effect of brown carbon from shipping emissions. Carbon Res 4(1):44

Article Google Scholar

* Szlapczynska J, Szlapczynski R (2019) Preference-based evolutionary multi-objective optimization in ship weather routing. Appl Soft Comput 84:105742

Article Google Scholar

* Tzortzis G, Sakalis G (2021) A dynamic ship speed optimization method with time horizon segmentation. Ocean Eng 226:108840. https://doi.org/10.1016/j.oceaneng.2021.108840

Article Google Scholar

* Ushakov S, Stenersen D, Einang PM (2019) Methane slip from gas fuelled ships: a comprehensive summary based on measurement data. J Mar Sci Technol 24(4):1308-1325

Article Google Scholar

* Uyanik T, Arslanoglu Y, Kalenderli O (2019) Ship fuel consumption prediction with machine learning. In: Proceedings of the 4th international mediterranean science and engineering congress, Antalya, Turkey

* Uyanık T, Karatuğ Ç, Arslanoğlu Y (2020) Machine learning approach to ship fuel consumption: a case of container vessel. Transp Res D Transp Environ 84:102389. https://doi.org/10.1016/j.trd.2020.102389

Article Google Scholar

* Vakili S, Ballini F, Schönborn A, Christodoulou A, Dalaklis D, Ölçer AI (2023) Assessing the macroeconomic and social impacts of slow steaming in shipping: a literature review on small island developing states and least developed countries. J Shipp Trade 8(1):2

Article Google Scholar

* Veneti A, Makrygiorgos A, Konstantopoulos C, Pantziou G, Vetsikas IA (2017) Minimizing the fuel consumption and the risk in maritime transportation: a bi-objective weather routing approach. Comput Oper Res 88:220-236

Article MathSciNet Google Scholar

* Vettor R, Bergamini G, Guedes Soares C (2021) A comprehensive approach to account for weather uncertainties in ship route optimization. J Mar Sci Eng 9(12):1434

Article Google Scholar

* Vettor R, Soares CG (2016) Development of a ship weather routing system. Ocean Eng 123:1-14

Article Google Scholar

* Vettor R, Soares CG (2022) Reflecting the uncertainties of ensemble weather forecasts on the predictions of ship fuel consumption. Ocean Eng 250:111009

Article Google Scholar

* Vettor R, Szlapczynska J, Szlapczynski R, Tycholiz W, Soares CG (2020) Towards improving optimised ship weather routing. Pol Marit Res 27(1):60-69

Article Google Scholar

* Walther L, Rizvanolli A, Wendebourg M, Jahn C (2016) Modeling and optimization algorithms in ship weather routing. Int J e-Navig Maritime Econ 4:31-45

Google Scholar

* Wang H, Lang X, Mao W (2021a) Voyage optimization combining genetic algorithm and dynamic programming for fuel/emissions reduction. Transp Res D Transp Environ 90:102670. https://doi.org/10.1016/j.trd.2020.102670

Article Google Scholar

* Wang H, Lang X, Mao W, Zhang D, Storhaug G (2020a) Effectiveness of 2d optimization algorithms considering voluntary speed reduction under uncertain metocean conditions. Ocean Eng 200:107063

Article Google Scholar

* Wang H, Mao W, Eriksson L (2019) A three-dimensional Dijkstra’s algorithm for multi-objective ship voyage optimization. Ocean Eng 186:106131. https://doi.org/10.1016/j.oceaneng.2019.106131

Article Google Scholar

* Wang H, Zhou P, Wang Z (2017) Reviews on current carbon emission reduction technologies and projects and their feasibilities on ships. J Mar Sci Appl 16(2):129-136

Article Google Scholar

* Wang K, Li J, Huang L, Ma R, Jiang X, Yuan Y, Mwero NA, Negenborn RR, Sun P, Yan X (2020b) A novel method for joint optimization of the sailing route and speed considering multiple environmental factors for more energy efficient shipping. Ocean Eng 216:107591

Article Google Scholar

* Wang K, Li J, Yan X, Huang L, Jiang X, Yuan Y, Ma R, Negenborn RR (2020c) A novel bi-level distributed dynamic optimization method of ship fleets energy consumption. Ocean Eng 197:106802. https://doi.org/10.1016/j.oceaneng.2019.106802

Article Google Scholar

* Wang X, Teo C-C (2013) Integrated hedging and network planning for container shipping’s bunker fuel management. Marit Econ Logist 15(2):172-196. https://doi.org/10.1057/mel.2013.5

Article Google Scholar

* Wang X, Zhao X, Wang G, Wang Q, Feng K (2022) Weather route optimization method of unmanned ship based on continuous dynamic optimal control. Sustainability 14(4):2165

Article Google Scholar

* Wang Y, Wei H, Zhang X, Li K, Guan G, Jin C, Yan L (2021b) Optimal design of ship branch pipe route by a cooperative co-evolutionary improved particle swarm genetic algorithm. Mar Technol Soc J 55(5):116-128

Article Google Scholar

* Xiao G, Pan L, Lai F (2025) Application, opportunities, and challenges of digital technologies in the decarbonizing shipping industry: a bibliometric analysis. Front Mar Sci 12:1523267

Article Google Scholar

* Xing H, Spence S, Chen H (2020) A comprehensive review on countermeasures for CO2 emissions from ships. Renew Sustain Energy Rev 134:110222

Article Google Scholar

* Yan R, Wang S, Du Y (2020) Development of a two-stage ship fuel consumption prediction and reduction model for a dry bulk ship. Transp Res Part E Logist Transp Rev 138:101930

Article Google Scholar

* Yan R, Wang S, Psaraftis HN (2021) Data analytics for fuel consumption management in maritime transportation: status and perspectives. Transp Res E Logist Transp Rev 155:102489. https://doi.org/10.1016/j.tre.2021.102489

Article Google Scholar

* Yang J, Wu L, Zheng J (2022) Multi-objective weather routing algorithm for ships: the perspective of shipping company’s navigation strategy. J Mar Sci Eng 10(9):1212

Article Google Scholar

* Yang L, Chen G, Zhao J, Rytter NGM (2020) Ship speed optimization considering ocean currents to enhance environmental sustainability in maritime shipping. Sustainability 12(9):3649

Article Google Scholar

* Yuan Y, Wang X, Tong L, Yang R, Shen B (2023) Research on multi-objective energy efficiency optimization method of ships considering carbon tax. J Mar Sci Eng 11(1):82

Article Google Scholar

* Yuan Z, Liu J, Zhang Q, Liu Y, Yuan Y, Li Z (2021) Prediction and optimisation of fuel consumption for inland ships considering real-time status and environmental factors. Ocean Eng 221:108530. https://doi.org/10.1016/j.oceaneng.2020.108530

Article Google Scholar

* Zaccone R, Figari M, Martelli M (2018) An optimization tool for ship route planning in real weather scenarios the 28th international ocean and polar engineering conference

* Zaccone R, Ottaviani E, Figari M, Altosole M (2018b) Ship voyage optimization for safe and energy-efficient navigation: a dynamic programming approach. Ocean Eng 153:215-224. https://doi.org/10.1016/j.oceaneng.2018.01.100

Article Google Scholar

* Zakerdoost H, Ghassemi H (2019) A multi-level optimization technique based on fuel consumption and energy index in early-stage ship design. Struct Multidiscip Optim 59:1417-1438

Article Google Scholar

* Zaman I, Pazouki K, Norman R, Younessi S, Coleman S (2017) Challenges and opportunities of big data analytics for upcoming regulations and future transformation of the shipping industry. Procedia Eng 194:537-544

Article Google Scholar

* Zhang G, Wang H, Zhao W, Guan Z, Li P (2021) Application of improved multi-objective ant colony optimization algorithm in ship weather routing. J Ocean Univ China 20:45-55

Article Google Scholar

* Zhang J, Zhang Z, Liu D (2024) Comparative study of different alternative fuel options for shipowners based on carbon intensity index model under the background of green shipping development. J Mar Sci Eng 12(11):2044

Article Google Scholar

* Zheng Z, Xie S, Dai H-N, Chen X, Wang H (2018) Blockchain challenges and opportunities: a survey. Int J Web Grid Serv 14(4):352-375

Article Google Scholar

* Zhou T, Hu Q, Hu Z, Zhen R (2022) An adaptive hyper parameter tuning model for ship fuel consumption prediction under complex maritime environments. J Ocean Eng Sci 7(3):255-263

Article Google Scholar

* Zhu Y, Zuo Y, Li T (2021) Modeling of ship fuel consumption based on multisource and heterogeneous data: case study of passenger ship. J Mar Sci Eng 9(3):273

Article Google Scholar

* Zincir B (2022) Environmental and economic evaluation of ammonia as a fuel for short-sea shipping: a case study. Int J Hydrogen Energy 47(41):18148-18168. https://doi.org/10.1016/j.ijhydene.2022.03.281

Article Google Scholar

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