Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/88423
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Title: Route and speed optimization for liner ships under emission control policies
Authors: Zhen, L
Hu, Z
Yan, R 
Zhuge, D 
Wang, S 
Issue Date: Jan-2020
Source: Transportation research. Part C, Emerging technologies, Jan. 2020, v. 110, p. 330-345
Abstract: Pollutants such as nitrogen oxides (NOx), sulfur dioxide (SO2), and particulate matters (PM) generated by shipping industry are increasing in recent years. In order to control the ship emission pollution, the International Maritime Organization (IMO) has established the Emission Control Areas (ECAs). In the fierce competition of the shipping market, liner shipping companies are looking for strategies to maintain their core competencies under the emission control policy. To achieve this goal, this paper first proposes a bi-objective mixed integer linear programming model, aiming to optimize sailing routes and speeds within and outside the ECA while minimizing the total fuel cost and SO2 emissions. Then, a new algorithm is developed to solve the proposed model by combining the two-stage iterative algorithm and fuzzy logic method based on ∊-constraint. Finally, this paper compares and analyzes the navigation plan of a real sailing route considering and not considering the effects of ECA. Some experiments are conducted to analyze the effects of fuel cost, decision makers, and ECA boundaries on the total fuel cost and SO2 emissions. The results indicate that the proposed model and algorithm can contribute to save fuel cost and reduce SO2 emissions under the ECA policy and provide different Pareto optimal solutions. Thus, the effectiveness of the model and the efficiency of the algorithm are validated. © 2019 Elsevier Ltd
Keywords: Bi-objective programming
Emission Control Area (ECA)
Route optimization
Sailing speed optimization
Two-stage iterative algorithm
Publisher: Pergamon Press
Journal: Transportation research. Part C, Emerging technologies 
ISSN: 0968-090X
DOI: 10.1016/j.trc.2019.11.004
Rights: © 2019 Elsevier Ltd. All rights reserved.
This is the preprint version of a work that was accepted for publication in Transportation Research Part C: Emerging Technologies. A definitive version was subsequently published in Transportation Research Part C: Emerging Technologies, Volume 110, January 2020, Pages 330-345, https://doi.org/10.1016/j.trc.2019.11.004
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