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Fundamental properties and pseudo-polynomial-time algorithm for network containership sailing speed optimization

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  • Wang, Shuaian

Abstract

In container liner shipping, bunker cost is an important component of the total operating cost, and bunker consumption increases dramatically when the sailing speed of containerships increases. A higher speed implies higher bunker consumption (higher bunker cost), shorter transit time (lower inventory cost), and larger shipping capacity per ship per year (lower ship cost). Therefore, a container shipping company aims to determine the optimal sailing speed of containerships in a shipping network to minimize the total cost. We derive analytical solutions for sailing speed optimization on a single ship route with a continuous number of ships. The advantage of analytical solutions lies in that it unveils the underlying structure and properties of the problem, from which a number of valuable managerial insights can be obtained. Based on the analytical solution and the properties of the problem, the optimal integer number of ships to deploy on a ship route can be obtained by solving two equations, each in one unknown, using a simple bi-section search method. The properties further enable us to identify an optimality condition for network containership sailing speed optimization. Based on this optimality condition, we propose a pseudo-polynomial-time solution algorithm that can efficiently obtain an epsilon-optimal solution for sailing speed of containerships in a liner shipping network.

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  • Wang, Shuaian, 2016. "Fundamental properties and pseudo-polynomial-time algorithm for network containership sailing speed optimization," European Journal of Operational Research, Elsevier, vol. 250(1), pages 46-55.
  • Handle: RePEc:eee:ejores:v:250:y:2016:i:1:p:46-55
    DOI: 10.1016/j.ejor.2015.10.052
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    Cited by:

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    3. Zhen, Lu & Wang, Kai & Wang, Shuaian & Qu, Xiaobo, 2018. "Tug scheduling for hinterland barge transport: A branch-and-price approach," European Journal of Operational Research, Elsevier, vol. 265(1), pages 119-132.
    4. De, Arijit & Choudhary, Alok & Turkay, Metin & Tiwari, Manoj K., 2021. "Bunkering policies for a fuel bunker management problem for liner shipping networks," European Journal of Operational Research, Elsevier, vol. 289(3), pages 927-939.
    5. Shuaian Wang & Dan Zhuge & Lu Zhen & Chung-Yee Lee, 2021. "Liner Shipping Service Planning Under Sulfur Emission Regulations," Transportation Science, INFORMS, vol. 55(2), pages 491-509, March.
    6. Jia Peng & Zhang Weilun & Wenhao E & Sun Xueshan, 2017. "A Refueling Scheme Optimization Model for the Voyage Charter with Fuel Price Fluctuation and Ship Deployment Consideration," Journal of Systems Science and Information, De Gruyter, vol. 5(3), pages 267-278, June.
    7. Bin Yu & Zixuan Peng & Zhihui Tian & Baozhen Yao, 2019. "Sailing speed optimization for tramp ships with fuzzy time window," Flexible Services and Manufacturing Journal, Springer, vol. 31(2), pages 308-330, June.
    8. Wang, Shuaian & Wang, Xinchang, 2016. "A polynomial-time algorithm for sailing speed optimization with containership resource sharing," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 394-405.
    9. Nguyen, Son & Fu, Xiuju & Ogawa, Daichi & Zheng, Qin, 2023. "An application-oriented testing regime and multi-ship predictive modeling for vessel fuel consumption prediction," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 177(C).
    10. Fukasawa, Ricardo & He, Qie & Song, Yongjia, 2016. "A disjunctive convex programming approach to the pollution-routing problem," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 61-79.
    11. Dan Zhuge & Shuaian Wang & Lu Zhen & Gilbert Laporte, 2020. "Schedule design for liner services under vessel speed reduction incentive programs," Naval Research Logistics (NRL), John Wiley & Sons, vol. 67(1), pages 45-62, February.
    12. Yan, Ran & Wang, Shuaian & Du, Yuquan, 2020. "Development of a two-stage ship fuel consumption prediction and reduction model for a dry bulk ship," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 138(C).
    13. ManWo Ng & Wayne K. Talley, 2017. "Chassis inventory management at U.S. container ports:modelling and case study," International Journal of Production Research, Taylor & Francis Journals, vol. 55(18), pages 5394-5404, September.
    14. Wu, Wei-Ming, 2020. "The optimal speed in container shipping: Theory and empirical evidence," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 136(C).
    15. 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.

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