Similar Search Results

Development of a two-stage ship fuel consumption prediction and reduction model for a dry bulk ship

Yan, Ran; Wang, Shuaian; Du, Yuquan - In: Transportation research / E : an international journal 138 (2020), pp. 1-22

Persistent link: https://www.econbiz.de/10012294932

Container freight rate level forecasting with machine learning methods

Feng, Yaling - In: International journal of business performance and … 13 (2022) 3, pp. 245-263

Persistent link: https://www.econbiz.de/10014287664

Service anomaly detection in dry bulk terminals : a machine learning approach

Ansorena, Iñigo L. - In: International journal of shipping and transport … 17 (2023) 3, pp. 281-302

Persistent link: https://www.econbiz.de/10014430017

Shipping market forecasting by forecast combination mechanism

Gao, Ruobin; Liu, Jiahui; Du, Liang; Yuen, Kum Fai - In: Maritime policy & management 49 (2022) 8, pp. 1059-1074

Persistent link: https://www.econbiz.de/10013532042

MPA-IBM project SAFER : sense-making analytics for Maritime event recognition

Yeo, Gavin; Lim, Shiau Hong; Wynter, Laura M.; Hassan, Hifaz - In: INFORMS journal on applied analytics 49 (2019) 4, pp. 269-280

Persistent link: https://www.econbiz.de/10012107647

Support vector machine algorithms : an application to ship price forecasting

Syriopoulos, Theodore; Tsatsaronis, Michael; Karamanos, … - In: Computational economics 57 (2021) 1, pp. 55-87

Persistent link: https://www.econbiz.de/10012486867

A machine learning approach to enable bulk orders of critical spare-parts in the shipping industry

Anglou, Fiorentia Zoi; Ponis, Stavros; Spanos, Athanasios - In: Journal of industrial engineering and management : JIEM 14 (2021) 3, pp. 604-621

Purpose: The main purpose of this paper is to propose a methodological approach and a decision support tool, based on prescriptive analytics, to enable bulk ordering of spare parts for shipping companies operating fleets of vessels. The developed tool utilises machine learning and operations...

Persistent link: https://www.econbiz.de/10012599063

Prediction of the severity of marine accidents using improved machine learning

Yang, Zaili; Wang, Jin; Li, Huanhuan - In: Transportation research : an international journal 188 (2024), pp. 1-31

Persistent link: https://www.econbiz.de/10015095272

A new exploration in Baltic Dry Index forecasting learning : application of a deep ensemble model

Su, Miao; Park, Keun Sik; Bae, Sung Hoon - In: Maritime economics & logistics 26 (2024) 1, pp. 21-43

Persistent link: https://www.econbiz.de/10014507710

Forecasting the potential of global marine shipping carbon emission under artificial intelligence based on a novel multivariate discrete grey model

Zeng, Zirui; Xu, Junwen; Shiwei, Zhou; Zhao, Yufeng; … - In: Marine economics and management : MAEM 7 (2024) 1, pp. 42-66

Purpose - To achieve sustainable development in shipping, accurately identifying the impact of artificial intelligence on shipping carbon emissions and predicting these emissions is of utmost importance. Design/methodology/approach - A multivariable discrete grey prediction model (WFTDGM) based...

Persistent link: https://www.econbiz.de/10015046355