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Speed Optimization for Container Ship Fleet Deployment Considering Fuel Consumption

Author

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  • Chao-Feng Gao

    (Logistics Research Center, Shanghai Maritime University, Shanghai 201306, China
    School of Management, Shanghai University of Engineering Science, Shanghai 201620, China)

  • Zhi-Hua Hu

    (Logistics Research Center, Shanghai Maritime University, Shanghai 201306, China)

Abstract

In recent years, low energy consumption has become the common choice of economic development in the world. In order to control energy consumption, shipping line speed optimization has become strategically important. to reduce fuel consumption, this study optimizes the container ship fleet deployment problem by adopting the strategy of adjusting each leg of each route’s sailing speed. To calculate fuel consumption more accurately, both sailing speed and the ship’s payload are considered. A multi-objective mixed integer nonlinear programming model is established to optimize the allocation of liner routes with multiple ship types on multiple routes. A linear outer-approximation algorithm and an improved piecewise linear approximation algorithm are used for linearization. If segments of an interval increase, the results will be more accurate but will take more time to compute. As fuel prices increase, to make trade-offs among economic and environmental considerations, the shipping company is adopting the “adding ship and slow down its speed” strategy, which verifies the validity and applicability of the established model.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:9:p:5242-:d:550304
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    References listed on IDEAS

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    Cited by:

    1. Xinyu Li & Yi Zuo & Junhao Jiang, 2022. "Application of Regression Analysis Using Broad Learning System for Time-Series Forecast of Ship Fuel Consumption," Sustainability, MDPI, vol. 15(1), pages 1-21, December.
    2. Xiangang Lan & Qin Tao & Xincheng Wu, 2023. "Liner-Shipping Network Design with Emission Control Areas: A Real Case Study," Sustainability, MDPI, vol. 15(4), pages 1-23, February.
    3. Kai Li & Yongqiang Zhuo & Xiaoqing Luo, 2022. "Optimization method of fuel saving and cost reduction of tugboat main engine based on genetic algorithm," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(1), pages 605-614, March.

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