A binary particle swarm optimization algorithm for ship routing and scheduling of liquefied natural gas transportation


With the increasing global demands for energy, fuel supply management is a challenging task of today’s industries in order to decrease the cost of energy and diminish its adverse environmental impacts. To have a more environmentally friendly fuel supply network, Liquefied Natural Gas (LNG) is suggested as one of the best choices for manufacturers. As the consumption rate of LNG is increasing dramatically in the world, many companies try to carry this product all around the world by themselves or outsource it to third-party companies. However, the challenge is that the transportation of LNG requires specific vessels and there are many clauses in related LNG transportation contracts which may reduce the revenue of these companies, it seems essential to find the best option for them. The aim of this paper is to propose a meta-heuristic Binary Particle Swarm Optimization (BPSO) algorithm to come with an optimized solution for ship routing and scheduling of LNG transportation. The application demonstrates what sellers need to do to reduce their costs and increase their profits by considering or removing some obligations.

Publication DOI: https://doi.org/10.1080/19427867.2019.1581485
Divisions: College of Business and Social Sciences > Aston Business School > Operations & Information Management
College of Business and Social Sciences > Aston Business School
Additional Information: This is an Accepted Manuscript of an article published by Taylor & Francis Group in Transportation Letters on 26 Feb 2019, available online at: http://www.tandfonline.com/10.1080/19427867.2019.1581485
Uncontrolled Keywords: Binary particle swarm optimization,liquefied natural gas,optimization,scheduling,ship routing,transportation,Transportation
Publication ISSN: 1942-7875
Last Modified: 22 Apr 2024 07:22
Date Deposited: 19 Mar 2019 10:56
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://www.tan ... 67.2019.1581485 (Publisher URL)
PURE Output Type: Article
Published Date: 2019-02-26
Accepted Date: 2019-02-01
Authors: Karbassi Yazdi, Amir
Kaviani, Mohamad Amin
Emrouznejad, Ali (ORCID Profile 0000-0001-8094-4244)
Sahebi, Hadi



Version: Accepted Version

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