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Development of ship weather routing system with higher accuracy using SPSS and an improved genetic algorithm

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A Correction to this article was published on 24 March 2021

This article has been updated

Abstract

Fuel consumption is an important factor to be considered in the process of weather routing. How to choose an appropriate route according to the requirements is particularly important. This paper proposes a method to optimize the ship weather routing. Based on the original genetic algorithm, the trigonometric function selection operator is introduced, the mutation operator is improved to increase the search range in the initial stage of the algorithm, and gradually narrow the search range in the middle and late stages of the algorithm, thus the convergence is sped up and the running time of the algorithm is reduced. Aiming at the incomplete ship speed fuel consumption comparison table, this paper uses SPSS (Statistical Product and Service Solutions) software to perform curve fitting and a curve with the best fitting degree is found. Then the curve equation is used to calculate the total fuel consumption of the case ship (S175 container ship) sailing through between the two ports Yokohama (35° N, 141° E) and San Francisco (37° N, 123° W), to verify the performance of the improved algorithm. Aiming at the minimum fuel consumption, the fuel consumption of the optimized route is reduced by 7.84% compared with that of the Rhumb Line.

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Abbreviations

\(\left( {N\left( t \right),v\left( t \right)} \right)\) :

A particular route, where \(N\left( t \right)\) is the ship position at time \(t\) and \(v\left( t \right)\) is the ship speed at time \(t\)

\(J_{L}\) :

Route length

\(J_{T}\) :

Sailing time

\(J_{Q}\) :

Fuel consumption

\(t_{0}\) :

The departure time

\(t_{n}\) :

The arrival time

\(q\left( {v\left( t \right)} \right)\) :

The fuel consumption per unit time at a given hydrostatic speed

\(\Omega_{a} \left( t \right)\) :

The permitted navigational area

\(V_{\min }\) :

The minimum hydrostatic speed of the ship

\(V_{\max }\) :

The maximum hydrostatic speed of the ship

\(n\) :

Number of waypoints

\(\left( {\lambda_{i} ,l_{i} } \right)\) :

The latitude and longitude coordinates of the ship at the waypoint \(i\)

\(\varphi_{i}\) :

The heading angle at the waypoint \(i\)

\(T\) :

The transformed objective function value of the sailing time

\(T_{{{\text{alarm}}}}\) :

The time of sailing in the dangerous area

\(T_{{{\text{total}}}}\) :

The actual total sailing time

\(S\) :

The transformed objective function value of the sailing distance

\(S_{{{\text{total}}}}\) :

The actual total sailing distance

\(C\) :

The transformed objective function value of the fuel consumption

\(C_{{{\text{total}}}}\) :

The actual total fuel consumption

\({\text{ETA}}\) :

The specified arrival time

\(C_{{{\text{ETA}}}}\) :

The transformed objective function value of the fuel consumption with the specified arrival time

\(V_{a}\) :

The actual speed of the ship in the wind and waves

\(V_{0}\) :

The given hydrostatic speed of the ship

\(q\) :

The relative angle between the ship’s heading and the wave direction

\(h\) :

The significant wave height

\(F\) :

The wind speed

\(\alpha\) :

The relative angle between the ship heading and the wind direction and

\(D\) :

The actual displacement of the ship

\(V_{{\text{c}}}\) :

The ship critical speed in the wind and waves

\({\text{Top}}_{i}\) :

The upper boundary point of navigation area

\({\text{Bottom}}_{i}\) :

The lower boundary point of navigation area

\(\overrightarrow {{X_{T} }}\) :

The upper boundary of navigation area

\(\overrightarrow {{X_{B} }}\) :

The lower boundary of navigation area

\(\overrightarrow {X}\) :

The chromosomes used to represent routes

\({\text{fit}}_{i}\) :

Original fitness value

\(F_{i}\) :

The fitness value after a sine transform

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Acknowledgements

The reported study has been funded by RFBR (Russian Foundation for Basic Research) according to research project number 17-07-00361a. In addition, we are very grateful to the editors and reviewers for their careful, impartial and constructive suggestions, which lead to this revision.

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Authors and Affiliations

  1. State Key Laboratory on Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun, 130012, China

    Chong Pan, Zhanshuo Zhang & Hongbo Wang

  2. CSSC Marine Technology Co., Ltd, Shanghai, China

    Weikang Sun & Jun Shi

Authors
  1. Chong Pan
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  2. Zhanshuo Zhang
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  3. Weikang Sun
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  4. Jun Shi
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  5. Hongbo Wang
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Correspondence to Hongbo Wang.

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Pan, C., Zhang, Z., Sun, W. et al. Development of ship weather routing system with higher accuracy using SPSS and an improved genetic algorithm. J Mar Sci Technol 26, 1324–1339 (2021). https://doi.org/10.1007/s00773-021-00800-6

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