An Expected Utility-Based Optimization of Slow Steaming in Sulphur Emission Control Areas by Applying Big Data Analytics
This paper analyses the operator's risk-based decision (RBD) company for slow steaming, and creates a sailing speed optimization model for slow steaming (SSOM-SS), aiming to balance the expected utility-based objectives (EUO) of fuel consumption, SOx emissions and delivery delay. Considering th...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8943336/ |
| _version_ | 1818665634786967552 |
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| author | Yuzhe Zhao Jingmiao Zhou Yujun Fan Haibo Kuang |
| author_facet | Yuzhe Zhao Jingmiao Zhou Yujun Fan Haibo Kuang |
| author_sort | Yuzhe Zhao |
| collection | DOAJ |
| description | This paper analyses the operator's risk-based decision (RBD) company for slow steaming, and creates a sailing speed optimization model for slow steaming (SSOM-SS), aiming to balance the expected utility-based objectives (EUO) of fuel consumption, SOx emissions and delivery delay. Considering the limitations of existing theoretical fuel consumption functions under uncertainties in voyages, the authors applies big data analytics (BDA) techniques like data fusion and feature selection to provide the SSOM-SS with accurate and suitable data on fuel consumption. In addition, a solver is built based on the genetic algorithm (GA) to solve the SSOM-SS. The effectiveness of the SSOM-SS is verified through a case study on the RBD for slow steaming of an Orient Overseas Container Line (OOCL) containership sailing across the sulphur emission control areas (SECAs) in Chinese coastal regions. The results show that the SSOM-SS can facilitate the RBD for slow steaming, and provide a novel tool for sailing speed optimization. |
| first_indexed | 2024-12-17T05:51:46Z |
| format | Article |
| id | doaj.art-bbb2d963b04f4469a205ea13ecdefafb |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T05:51:46Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-bbb2d963b04f4469a205ea13ecdefafb2022-12-21T22:01:08ZengIEEEIEEE Access2169-35362020-01-0183646365510.1109/ACCESS.2019.29622108943336An Expected Utility-Based Optimization of Slow Steaming in Sulphur Emission Control Areas by Applying Big Data AnalyticsYuzhe Zhao0https://orcid.org/0000-0001-6881-923XJingmiao Zhou1https://orcid.org/0000-0003-2190-1783Yujun Fan2https://orcid.org/0000-0001-8800-6383Haibo Kuang3https://orcid.org/0000-0002-9357-0576Collaborative Innovation Center for Transport Studies, Dalian Maritime University, Dalian, ChinaCollaborative Innovation Center for Transport Studies, Dalian Maritime University, Dalian, ChinaCollaborative Innovation Center for Transport Studies, Dalian Maritime University, Dalian, ChinaCollaborative Innovation Center for Transport Studies, Dalian Maritime University, Dalian, ChinaThis paper analyses the operator's risk-based decision (RBD) company for slow steaming, and creates a sailing speed optimization model for slow steaming (SSOM-SS), aiming to balance the expected utility-based objectives (EUO) of fuel consumption, SOx emissions and delivery delay. Considering the limitations of existing theoretical fuel consumption functions under uncertainties in voyages, the authors applies big data analytics (BDA) techniques like data fusion and feature selection to provide the SSOM-SS with accurate and suitable data on fuel consumption. In addition, a solver is built based on the genetic algorithm (GA) to solve the SSOM-SS. The effectiveness of the SSOM-SS is verified through a case study on the RBD for slow steaming of an Orient Overseas Container Line (OOCL) containership sailing across the sulphur emission control areas (SECAs) in Chinese coastal regions. The results show that the SSOM-SS can facilitate the RBD for slow steaming, and provide a novel tool for sailing speed optimization.https://ieeexplore.ieee.org/document/8943336/Big data analytics (BDA)slow steamingsailing speed optimizationfuel consumptiongenetic algorithm (GA)risk aversion |
| spellingShingle | Yuzhe Zhao Jingmiao Zhou Yujun Fan Haibo Kuang An Expected Utility-Based Optimization of Slow Steaming in Sulphur Emission Control Areas by Applying Big Data Analytics IEEE Access Big data analytics (BDA) slow steaming sailing speed optimization fuel consumption genetic algorithm (GA) risk aversion |
| title | An Expected Utility-Based Optimization of Slow Steaming in Sulphur Emission Control Areas by Applying Big Data Analytics |
| title_full | An Expected Utility-Based Optimization of Slow Steaming in Sulphur Emission Control Areas by Applying Big Data Analytics |
| title_fullStr | An Expected Utility-Based Optimization of Slow Steaming in Sulphur Emission Control Areas by Applying Big Data Analytics |
| title_full_unstemmed | An Expected Utility-Based Optimization of Slow Steaming in Sulphur Emission Control Areas by Applying Big Data Analytics |
| title_short | An Expected Utility-Based Optimization of Slow Steaming in Sulphur Emission Control Areas by Applying Big Data Analytics |
| title_sort | expected utility based optimization of slow steaming in sulphur emission control areas by applying big data analytics |
| topic | Big data analytics (BDA) slow steaming sailing speed optimization fuel consumption genetic algorithm (GA) risk aversion |
| url | https://ieeexplore.ieee.org/document/8943336/ |
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