A Decision Support System for Vessel Speed Decision in Maritime Logistics using Weather Archive Big Data

Habin Lee, Nursen Ayden, Youngseok Choi, Zahir Irani, Saowanit Lekhavat

Abstract

Speed optimization of liner vessels has significant economic and environmental impact for reducing fuel cost and Green House Gas (GHG) emission as the shipping over maritime logistics takes more than 70% of world transportation. While slow steaming is widely used as best practices for liner shipping companies, they are also under the pressure to maintain service level agreement (SLA) with their cargo clients. Thus, deciding optimal speed that minimizes fuel consumption while maintaining SLA is managerial decision problem. Studies in the literature use theoretical fuel consumption functions in their speed optimization models but these functions have limitations due to weather conditions in voyages. This paper uses weather archive data to estimate the real fuel consumption function for speed optimization problems. In particular, Copernicus data set is used as the source of big data and data mining technique is applied to identify the impact of weather conditions based on a given voyage route. Particle swarm optimization, a metaheuristic optimization method, is applied to find Pareto optimal solutions that minimize fuel consumption and maximize SLA. The usefulness of the proposed approach is verified through the real data obtained from a liner company and real world implications are discussed.

Publisher Statement: This is an open access article under the CC BY license. (http://creativecommons.org/licenses/by/4.0/)
Original languageEnglish
Pages (from-to)(in press)
Number of pages13
JournalComputers & Operations Research
Volume(in press)
DOIs
StatePublished - 13 Jun 2017

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Fuel consumption
Freight transportation
Logistics
Industry
Big data
Decision support systems
Gas emissions
Greenhouse gases
Particle swarm optimization (PSO)
Data mining
Environmental impact
Economics
Costs

Keywords

  • Speed optimization
  • Sustainable maritime logistics
  • Weather archive data
  • Liner shipping
  • Particle swarm optimization

Cite this

A Decision Support System for Vessel Speed Decision in Maritime Logistics using Weather Archive Big Data. / Lee, Habin; Ayden, Nursen; Choi, Youngseok; Irani, Zahir; Lekhavat, Saowanit.

In: Computers & Operations Research, Vol. (in press), 13.06.2017, p. (in press).

Research output: Contribution to journalArticle

Lee, Habin; Ayden, Nursen; Choi, Youngseok; Irani, Zahir; Lekhavat, Saowanit / A Decision Support System for Vessel Speed Decision in Maritime Logistics using Weather Archive Big Data.

In: Computers & Operations Research, Vol. (in press), 13.06.2017, p. (in press).

Research output: Contribution to journalArticle

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