Fernández-Reyes Doallo, R.; Camarero Orive, A.; González Cancelas, N. Predictive Model for LNG Ship Routing applying Machine Learning. Preprints2022, 2022120252. https://doi.org/10.20944/preprints202212.0252.v1
APA Style
Fernández-Reyes Doallo, R., Camarero Orive, A., & González Cancelas, N. (2022). Predictive Model for LNG Ship Routing applying Machine Learning. Preprints. https://doi.org/10.20944/preprints202212.0252.v1
Chicago/Turabian Style
Fernández-Reyes Doallo, R., Alberto Camarero Orive and Nicoleta González Cancelas. 2022 "Predictive Model for LNG Ship Routing applying Machine Learning" Preprints. https://doi.org/10.20944/preprints202212.0252.v1
Abstract
The purpose of this paper is to develop a theoretical predictive model for LNG shipping routes selection process. Strategic decisions about shipping costs could be improved if a deeper knowledge about products economic value is provided. Developments made on the extraction and industrial processes related to this fossil fuel are driving the natural gas sector towards a unique globalised market. Moreover, data analytics applications as well as machine learning are topics presented as perfect catalysers for achieving an unprecedented natural gas globalised market. Additionally, this paper aims at showing the state of the art of new techniques used in transportation engineering that might have synergies with other industries (eg. commodities cost reduction, energy supply…). Finally, this paper aims to provide foundation for further research and development using more sophisticated data and algorithms that will help to close the gap between theoretical and practical scope of this techniques.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
