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Routing through dynamic ocean currents: General heuristics and empirical results in the gulf stream region

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  • Lo, Hong K.
  • McCord, Mark R.

Abstract

Anticipating the availability of good quality ocean current data in the near future, we formulate the problem of routing an ocean vessel through currents to minimize fuel consumption, propose methods to increase the efficiency of the solution techniques, and simulate voyages to investigate the performance of our approach. We formulate the problem as a dynamic program (DP) with two variables: Heading and Power (H&P). We then develop two heuristics, Headingthen-Power (H/P) and Heading-Alone (HA), that reduce the complexity of the formulation by decomposing the heading optimization from the power-setting optimization. To improve computational efficiency, we propose three approaches based on ship and ocean current dynamics to limit the spatial and temporal ranges that must be investigated to solve our DP formulations. In our simulation study, these approaches reduced the spatial ranges by over one third and the temporal ranges by over 70%. The study simulated minimum fuel current routing of 96 voyages in the Gulf Stream region, leading to average fuel savings of 7.4% and 4.5% for eastbound and westbound voyages, respectively. Moreover, the simplest HA heuristic, which emphasizes heading over power optimization, provided solutions as good as those provided by the most complete H&P formulation while reducing the computational time by a factor of 40. This indicates that the shipping industry's practice of emphasizing heading considerations seems appropriate in the current routing case and that current routing implementations and algorithmic developments might be able to reduce problem complexity by concentrating on spatial variables at the expense of temporal variables.

Suggested Citation

  • Lo, Hong K. & McCord, Mark R., 1995. "Routing through dynamic ocean currents: General heuristics and empirical results in the gulf stream region," Transportation Research Part B: Methodological, Elsevier, vol. 29(2), pages 109-124, April.
  • Handle: RePEc:eee:transb:v:29:y:1995:i:2:p:109-124
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    References listed on IDEAS

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    1. Hong Kam Lo & McCord, Mark R. & Wall, Cori K., 1991. "Value of ocean current information for strategic routing," European Journal of Operational Research, Elsevier, vol. 55(2), pages 124-135, November.
    2. Anastassios N. Perakis & Nikiforos A. Papadakis, 1989. "Minimal Time Vessel Routing in a Time-Dependent Environment," Transportation Science, INFORMS, vol. 23(4), pages 266-276, November.
    3. Nikiforos A. Papadakis & Anastassios N. Perakis, 1990. "Deterministic Minimal Time Vessel Routing," Operations Research, INFORMS, vol. 38(3), pages 426-438, June.
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    Cited by:

    1. 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.
    2. Lo, Hong K. & McCord, Mark R., 1998. "Adaptive ship routing through stochastic ocean currents: general formulations and empirical results," Transportation Research Part A: Policy and Practice, Elsevier, vol. 32(7), pages 547-561, September.
    3. Adland, Roar & Cariou, Pierre & Wolff, Francois-Charles, 2020. "Optimal ship speed and the cubic law revisited: Empirical evidence from an oil tanker fleet," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 140(C).
    4. An, Kun & Lo, Hong K., 2014. "Ferry service network design with stochastic demand under user equilibrium flows," Transportation Research Part B: Methodological, Elsevier, vol. 66(C), pages 70-89.
    5. Wu, Lingxiao & Wang, Shuaian & Laporte, Gilbert, 2021. "The Robust Bulk Ship Routing Problem with Batched Cargo Selection," Transportation Research Part B: Methodological, Elsevier, vol. 143(C), pages 124-159.
    6. 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.
    7. 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).

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