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Ship Speed Optimization Considering Ocean Currents to Enhance Environmental Sustainability in Maritime Shipping

Author

Listed:
  • Liqian Yang

    (School of Economics and Management, Harbin Engineering University, Harbin 150001, China)

  • Gang Chen

    (China Institute of FTZ Supply Chain, Shanghai Maritime University, Haigang Ave 1550, Shanghai 201306, China
    Department of Technology and Innovation, University of Southern Denmark, 5230 Odense, Denmark)

  • Jinlou Zhao

    (School of Economics and Management, Harbin Engineering University, Harbin 150001, China)

  • Niels Gorm Malý Rytter

    (Department of Technology and Innovation, University of Southern Denmark, 5230 Odense, Denmark)

Abstract

Enhancing environmental sustainability in maritime shipping has emerged as an important topic for both firms in shipping-related industries and policy makers. Speed optimization has been proven to be one of the most effective operational measures to achieve this goal, as fuel consumption and greenhouse gas (GHG) emissions of a ship are very sensitive to its sailing speed. Existing research on ship speed optimization does not differentiate speed through water (STW) from speed over ground (SOG) when formulating the fuel consumption function and the sailing time function. Aiming to fill this research gap, we propose a speed optimization model for a fixed ship route to minimize the total fuel consumption over the whole voyage, in which the influence of ocean currents is taken into account. As the difference between STW and SOG is mainly due to ocean currents, the proposed model is capable of distinguishing STW from SOG. Thus, in the proposed model, the ship’s fuel consumption and sailing time can be determined with the correct speed. A case study on a real voyage for an oil products tanker shows that: (a) the average relative error between the estimated SOG and the measured SOG can be reduced from 4.75% to 1.36% across sailing segments, if the influence of ocean currents is taken into account, and (b) the proposed model can enable the selected oil products tanker to save 2.20% of bunker fuel and reduce 26.12 MT of CO2 emissions for a 280-h voyage. The proposed model can be used as a practical and robust decision support tool for voyage planners/managers to reduce the fuel consumption and GHG emissions of a ship.

Suggested Citation

  • Liqian Yang & Gang Chen & Jinlou Zhao & Niels Gorm Malý Rytter, 2020. "Ship Speed Optimization Considering Ocean Currents to Enhance Environmental Sustainability in Maritime Shipping," Sustainability, MDPI, vol. 12(9), pages 1-24, May.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:9:p:3649-:d:352922
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    References listed on IDEAS

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    1. Houming Fan & Jiaqi Yu & Xinzhe Liu, 2019. "Tramp Ship Routing and Scheduling with Speed Optimization Considering Carbon Emissions," Sustainability, MDPI, vol. 11(22), pages 1-19, November.
    2. Meng, Qiang & Du, Yuquan & Wang, Yadong, 2016. "Shipping log data based container ship fuel efficiency modeling," Transportation Research Part B: Methodological, Elsevier, vol. 83(C), pages 207-229.
    3. Reinhardt, Line Blander & Pisinger, David & Sigurd, Mikkel M. & Ahmt, Jonas, 2020. "Speed optimizations for liner networks with business constraints," European Journal of Operational Research, Elsevier, vol. 285(3), pages 1127-1140.
    4. K Fagerholt & G Laporte & I Norstad, 2010. "Reducing fuel emissions by optimizing speed on shipping routes," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(3), pages 523-529, March.
    5. Zhibin Zhang & Chee-Chong Teo & Xiaoyu Wang, 2015. "Optimality properties of speed optimization for a vessel operating with time window constraint," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(4), pages 637-646, April.
    6. Du, Yuquan & Meng, Qiang & Wang, Shuaian & Kuang, Haibo, 2019. "Two-phase optimal solutions for ship speed and trim optimization over a voyage using voyage report data," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 88-114.
    7. Harilaos N. Psaraftis, 2019. "Speed Optimization vs Speed Reduction: the Choice between Speed Limits and a Bunker Levy," Sustainability, MDPI, vol. 11(8), pages 1-18, April.
    8. Du, Yuquan & Chen, Qiushuang & Quan, Xiongwen & Long, Lei & Fung, Richard Y.K., 2011. "Berth allocation considering fuel consumption and vessel emissions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 47(6), pages 1021-1037.
    9. Jun Xia & Kevin X. Li & Hong Ma & Zhou Xu, 2015. "Joint Planning of Fleet Deployment, Speed Optimization, and Cargo Allocation for Liner Shipping," Transportation Science, INFORMS, vol. 49(4), pages 922-938, November.
    10. Zhen, Lu & Wang, Shuaian & Zhuge, Dan, 2017. "Dynamic programming for optimal ship refueling decision," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 100(C), pages 63-74.
    11. Aydin, N. & Lee, H. & Mansouri, S.A., 2017. "Speed optimization and bunkering in liner shipping in the presence of uncertain service times and time windows at ports," European Journal of Operational Research, Elsevier, vol. 259(1), pages 143-154.
    12. Qi, Xiangtong & Song, Dong-Ping, 2012. "Minimizing fuel emissions by optimizing vessel schedules in liner shipping with uncertain port times," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(4), pages 863-880.
    13. He, Qie & Zhang, Xiaochen & Nip, Kameng, 2017. "Speed optimization over a path with heterogeneous arc costs," Transportation Research Part B: Methodological, Elsevier, vol. 104(C), pages 198-214.
    14. Mansouri, S. Afshin & Lee, Habin & Aluko, Oluwakayode, 2015. "Multi-objective decision support to enhance environmental sustainability in maritime shipping: A review and future directions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 78(C), pages 3-18.
    15. Wang, Shuaian & Meng, Qiang, 2012. "Sailing speed optimization for container ships in a liner shipping network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(3), pages 701-714.
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    Cited by:

    1. Chao-Feng Gao & Zhi-Hua Hu, 2021. "Speed Optimization for Container Ship Fleet Deployment Considering Fuel Consumption," Sustainability, MDPI, vol. 13(9), pages 1-18, May.
    2. Xi Jiang & Haijun Mao & Yadong Wang & Hao Zhang, 2020. "Liner Shipping Schedule Design for Near-Sea Routes Considering Big Customers’ Preferences on Ship Arrival Time," Sustainability, MDPI, vol. 12(18), pages 1-20, September.
    3. Yan, Ran & Wang, Shuaian & Psaraftis, Harilaos N., 2021. "Data analytics for fuel consumption management in maritime transportation: Status and perspectives," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 155(C).

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