A novel, data-driven heuristic framework for vessel weather routing

Research output: Contribution to journalArticle

Abstract

Fuel Oil Consumption (FOC) constitutes approximately two-thirds of a vessel’s voyage costs and profoundly correlates with the adversity of the weather conditions along its route. Furthermore, increased FOC also leads to increased emissions. As shipping is turning page towards a greener, more sustainable future, it is crucial to leverage key insights from past routes in order to identify approaches that minimise both the financial cost of operations and their Green House Gas (GHG) footprint. This study presents a novel framework for vessel weather routing based on historical ship performance and current weather conditions at a discretised grid of points in conjunction with a data-driven model that can predict main engine FOC. Subsequently, a modified version of Dijkstra’s algorithm that has been fitted with heuristics is applied recursively until an optimal route is obtained. The efficacy of the proposed framework is demonstrated through a case study concerning the optimal route selection for a 160,000 tonne DWT crude oil tanker sailing between the Gulf of Guinea and the Marseille anchorage. In this case study, an푅2of 89.4% was obtained while predicting the vessel’s FOC and five optimal routes were identified and ranked for two sailing speeds corresponding to different operating profiles, i.e. ballast and fully loaded.
Original languageEnglish
Article number106887
Number of pages10
JournalOcean Engineering
Volume197
Early online date14 Jan 2020
DOIs
Publication statusE-pub ahead of print - 14 Jan 2020

Fingerprint

Fuel oils
Oil tankers
Freight transportation
Greenhouse gases
Costs
Ships
Crude oil
Engines

Keywords

  • weather routing
  • fuel oil consumption prediction
  • ship energy effciency
  • machine learning

Cite this

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title = "A novel, data-driven heuristic framework for vessel weather routing",
abstract = "Fuel Oil Consumption (FOC) constitutes approximately two-thirds of a vessel’s voyage costs and profoundly correlates with the adversity of the weather conditions along its route. Furthermore, increased FOC also leads to increased emissions. As shipping is turning page towards a greener, more sustainable future, it is crucial to leverage key insights from past routes in order to identify approaches that minimise both the financial cost of operations and their Green House Gas (GHG) footprint. This study presents a novel framework for vessel weather routing based on historical ship performance and current weather conditions at a discretised grid of points in conjunction with a data-driven model that can predict main engine FOC. Subsequently, a modified version of Dijkstra’s algorithm that has been fitted with heuristics is applied recursively until an optimal route is obtained. The efficacy of the proposed framework is demonstrated through a case study concerning the optimal route selection for a 160,000 tonne DWT crude oil tanker sailing between the Gulf of Guinea and the Marseille anchorage. In this case study, an푅2of 89.4{\%} was obtained while predicting the vessel’s FOC and five optimal routes were identified and ranked for two sailing speeds corresponding to different operating profiles, i.e. ballast and fully loaded.",
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author = "Christos Gkerekos and Iraklis Lazakis",
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A novel, data-driven heuristic framework for vessel weather routing. / Gkerekos, Christos; Lazakis, Iraklis.

In: Ocean Engineering, Vol. 197, 106887, 01.02.2020.

Research output: Contribution to journalArticle

TY - JOUR

T1 - A novel, data-driven heuristic framework for vessel weather routing

AU - Gkerekos, Christos

AU - Lazakis, Iraklis

PY - 2020/1/14

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AB - Fuel Oil Consumption (FOC) constitutes approximately two-thirds of a vessel’s voyage costs and profoundly correlates with the adversity of the weather conditions along its route. Furthermore, increased FOC also leads to increased emissions. As shipping is turning page towards a greener, more sustainable future, it is crucial to leverage key insights from past routes in order to identify approaches that minimise both the financial cost of operations and their Green House Gas (GHG) footprint. This study presents a novel framework for vessel weather routing based on historical ship performance and current weather conditions at a discretised grid of points in conjunction with a data-driven model that can predict main engine FOC. Subsequently, a modified version of Dijkstra’s algorithm that has been fitted with heuristics is applied recursively until an optimal route is obtained. The efficacy of the proposed framework is demonstrated through a case study concerning the optimal route selection for a 160,000 tonne DWT crude oil tanker sailing between the Gulf of Guinea and the Marseille anchorage. In this case study, an푅2of 89.4% was obtained while predicting the vessel’s FOC and five optimal routes were identified and ranked for two sailing speeds corresponding to different operating profiles, i.e. ballast and fully loaded.

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